Phertilizer: Growing a clonal tree from ultra-low coverage single-cell DNA sequencing of tumors.

Emerging ultra-low coverage single-cell DNA sequencing (scDNA-seq) technologies have enabled high resolution evolutionary studies of copy number aberrations (CNAs) within tumors. While these sequencing technologies are well suited for identifying CNAs due to the uniformity of sequencing coverage, the sparsity of coverage poses challenges for the study of single-nucleotide variants (SNVs). In order to maximize the utility of increasingly available ultra-low coverage scDNA-seq data and obtain a comprehensive understanding of tumor evolution, it is important to also analyze the evolution of SNVs from the same set of tumor cells. We present Phertilizer, a method to infer a clonal tree from ultra-low coverage scDNA-seq data of a tumor. Based on a probabilistic model, our method recursively partitions the data by identifying key evolutionary events in the history of the tumor. We demonstrate the performance of Phertilizer on simulated data as well as on two real datasets, finding that Phertilizer effectively utilizes the copy-number signal inherent in the data to more accurately uncover clonal structure and genotypes compared to previous methods.

doubletD: detecting doublets in single-cell DNA sequencing data.

MOTIVATION: While single-cell DNA sequencing (scDNA-seq) has enabled the study of intratumor heterogeneity at an unprecedented resolution, current technologies are error-prone and often result in doublets where two or more cells are mistaken for a single cell. Not only do doublets confound downstream analyses, but the increase in doublet rate is also a major bottleneck preventing higher throughput with current single-cell technologies. Although doublet detection and removal are standard practice in scRNA-seq data analysis, options for scDNA-seq data are limited. Current methods attempt to detect doublets while also performing complex downstream analyses tasks, leading to decreased efficiency and/or performance. RESULTS: We present doubletD, the first standalone method for detecting doublets in scDNA-seq data. Underlying our method is a simple maximum likelihood approach with a closed-form solution. We demonstrate the performance of doubletD on simulated data as well as real datasets, outperforming current methods for downstream analysis of scDNA-seq data that jointly infer doublets as well as standalone approaches for doublet detection in scRNA-seq data. Incorporating doubletD in scDNA-seq analysis pipelines will reduce complexity and lead to more accurate results. AVAILABILITY AND IMPLEMENTATION: https://github.com/elkebir-group/doubletD. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PhyDOSE: Design of follow-up single-cell sequencing experiments of tumors.

The combination of bulk and single-cell DNA sequencing data of the same tumor enables the inference of high-fidelity phylogenies that form the input to many important downstream analyses in cancer genomics. While many studies simultaneously perform bulk and single-cell sequencing, some studies have analyzed initial bulk data to identify which mutations to target in a follow-up single-cell sequencing experiment, thereby decreasing cost. Bulk data provide an additional untapped source of valuable information, composed of candidate phylogenies and associated clonal prevalence. Here, we introduce PhyDOSE, a method that uses this information to strategically optimize the design of follow-up single cell experiments. Underpinning our method is the observation that only a small number of clones uniquely distinguish one candidate tree from all other trees. We incorporate distinguishing features into a probabilistic model that infers the number of cells to sequence so as to confidently reconstruct the phylogeny of the tumor. We validate PhyDOSE using simulations and a retrospective analysis of a leukemia patient, concluding that PhyDOSE's computed number of cells resolves tree ambiguity even in the presence of typical single-cell sequencing errors. We also conduct a retrospective analysis on an acute myeloid leukemia cohort, demonstrating the potential to achieve similar results with a significant reduction in the number of cells sequenced. In a prospective analysis, we demonstrate the advantage of selecting cells to sequence across multiple biopsies and that only a small number of cells suffice to disambiguate the solution space of trees in a recent lung cancer cohort. In summary, PhyDOSE proposes cost-efficient single-cell sequencing experiments that yield high-fidelity phylogenies, which will improve downstream analyses aimed at deepening our understanding of cancer biology.

Isotype-aware inference of B cell clonal lineage trees from single-cell sequencing data.

Single-cell RNA sequencing (scRNA-seq) enables comprehensive characterization of the micro-evolutionary processes of B cells during an adaptive immune response, capturing features of somatic hypermutation (SHM) and class switch recombination (CSR). Existing phylogenetic approaches for reconstructing B cell evolution have primarily focused on the SHM process alone. Here, we present tree inference of B cell clonal lineages (TRIBAL), an algorithm designed to optimally reconstruct the evolutionary history of B cell clonal lineages undergoing both SHM and CSR from scRNA-seq data. Through simulations, we demonstrate that TRIBAL produces more comprehensive and accurate B cell lineage trees compared to existing methods. Using real-world datasets, TRIBAL successfully recapitulates expected biological trends in a model affinity maturation system while reconstructing evolutionary histories with more parsimonious class switching than state-of-the-art methods. Thus, TRIBAL significantly improves B cell lineage tracing, useful for modeling vaccine responses, disease progression, and the identification of therapeutic antibodies.

Complex posttraumatic stress disorder symptoms among midlife to older female survivors of intimate partner violence.

OBJECTIVE: Survivors of intimate partner violence are exposed to prolonged and repeated trauma due to the methods of control associated with abuse but do not always seek help from trauma-focused service provision. Despite links between complex posttraumatic stress disorder and partner violence, research has not explored how symptoms may be presented within the stories of abuse and the clinical implications of this. The aim was to explore the narratives of intimate partner violence and uncover how aspects of complex posttraumatic stress disorder may be present. METHOD: The stories from 13 women with a mean age of 52.3 years were explored using thematic analysis. RESULTS: Across the survivors' stories, four themes with associated subthemes were uncovered: (a) difficulties in affect regulation, (b) belief systems that erode self-determination, (c) managing the threat response, and (d) difficulties in sustaining relationships. The findings suggest underlying symptoms of complex trauma were present. Presentations of symptoms associated with complex posttraumatic stress disorder demonstrate a need for therapeutic practitioners to be more aware of the prevalence of this for those who have experienced relational abuse and the implications for therapeutic interventions and engagement. CONCLUSIONS: Exploring traumatic experiences from the perspectives of survivors is an important way of understanding the impacts on and consequences for the survivor and their adjustment beyond abuse, moving from a symptomatic lens to an approach of trauma journey exploration. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

TRIBAL: Tree Inference of B cell Clonal Lineages.

B cells are a critical component of the adaptive immune system, responsible for producing antibodies that help protect the body from infections and foreign substances. Single cell RNA-sequencing (scRNA-seq) has allowed for both profiling of B cell receptor (BCR) sequences and gene expression. However, understanding the adaptive and evolutionary mechanisms of B cells in response to specific stimuli remains a significant challenge in the field of immunology. We introduce a new method, TRIBAL, which aims to infer the evolutionary history of clonally related B cells from scRNA-seq data. The key insight of TRIBAL is that inclusion of isotype data into the B cell lineage inference problem is valuable for reducing phylogenetic uncertainty that arises when only considering the receptor sequences. Consequently, the TRIBAL inferred B cell lineage trees jointly capture the somatic mutations introduced to the B cell receptor during affinity maturation and isotype transitions during class switch recombination. In addition, TRIBAL infers isotype transition probabilities that are valuable for gaining insight into the dynamics of class switching. Via in silico experiments, we demonstrate that TRIBAL infers isotype transition probabilities with the ability to distinguish between direct versus sequential switching in a B cell population. This results in more accurate B cell lineage trees and corresponding ancestral sequence and class switch reconstruction compared to competing methods. Using real-world scRNA-seq datasets, we show that TRIBAL recapitulates expected biological trends in a model affinity maturation system. Furthermore, the B cell lineage trees inferred by TRIBAL were equally plausible for the BCR sequences as those inferred by competing methods but yielded lower entropic partitions for the isotypes of the sequenced B cell. Thus, our method holds the potential to further advance our understanding of vaccine responses, disease progression, and the identification of therapeutic antibodies.

Distinguishing linear and branched evolution given single-cell DNA sequencing data of tumors.

BACKGROUND: Cancer arises from an evolutionary process where somatic mutations give rise to clonal expansions. Reconstructing this evolutionary process is useful for treatment decision-making as well as understanding evolutionary patterns across patients and cancer types. In particular, classifying a tumor's evolutionary process as either linear or branched and understanding what cancer types and which patients have each of these trajectories could provide useful insights for both clinicians and researchers. While comprehensive cancer phylogeny inference from single-cell DNA sequencing data is challenging due to limitations with current sequencing technology and the complexity of the resulting problem, current data might provide sufficient signal to accurately classify a tumor's evolutionary history as either linear or branched. RESULTS: We introduce the Linear Perfect Phylogeny Flipping (LPPF) problem as a means of testing two alternative hypotheses for the pattern of evolution, which we prove to be NP-hard. We develop Phyolin, which uses constraint programming to solve the LPPF problem. Through both in silico experiments and real data application, we demonstrate the performance of our method, outperforming a competing machine learning approach. CONCLUSION: Phyolin is an accurate, easy to use and fast method for classifying an evolutionary trajectory as linear or branched given a tumor's single-cell DNA sequencing data.

Organochlorine Levels in Plasma and Risk of Multiple Myeloma.

OBJECTIVE: The aim of this study was to estimate the association between organochlorine pesticides and polychlorinated biphenyls (PCBs) and multiple myeloma (MM). METHODS: The risk of MM from organochlorine compounds was examined in a population-based case-control study in British Columbia, Canada. Congeners of PCBs and pesticides or pesticide metabolites were measured in plasma of 325 cases and 327 controls. RESULTS: Most organochlorine analytes showed a significant association with MM. The strongest association (highest vs lowest quartile) was oxychlordane (odds ratio = 7.44; 95% confidence interval = 4.19 to 13.21). No heterogeneity was detected between organochlorines levels and MM subtypes. Only oxychlordane and ß-hexachlorocyclohexane (ß-HCCH) were identified as significant independent predictors of MM. CONCLUSION: Our study provides evidence that organochlorines contribute to the risk of MM.

A Quantitative Analysis of the Relationship Between Radiation Therapy Use and Travel Time.

PURPOSE: To model and quantify the relationship between radiation therapy (RT) use and travel time to RT services. METHODS AND MATERIALS: Population-based registries and databases were used to identify both incident cancer patient and patients receiving RT within 1 year of diagnosis (RT1y) in British Columbia, Canada, between 1992 and 2011. The effects of age, gender, diagnosis year, income, prevailing wait time, and travel duration for RT on RT1y were assessed. Significant factors from univariate analyses were included in a multivariable logistic regression model. The shape of the travel time-RT1y curve was represented by generalized additive and segmented regression models. Analyses were conducted for breast, lung, and genitourinary cancer separately and for all cancer sites combined. RESULTS: After adjustment for age, gender, diagnosis year, income, and prevailing wait times, increasing travel time to the closest RT facility had a negative impact RT1y. The shape of the travel time-RT1y curve varied with cancer type. For breast cancer, the odds of RT1y were constant for the first 2 driving hours and decreased at 17% per hour thereafter. For lung cancer, the odds of RT1y decreased by 16% after 20 minutes and then decreased at 6% per hour. Genitourinary cancer RT1y was relatively independent of travel time. For all cancer sites combined, the odds of RT1y were constant within the first 2 driving hours and decreased at 7% per hour thereafter. CONCLUSIONS: Travel time to receive RT has a different impact on RT1y for different tumor sites. The results provide evidence-based insights for the configuration of catchment areas for new and existing cancer centers providing RT.