Interfaces of Malignant and Immunologic Clonal Dynamics in Ovarian Cancer.

High-grade serous ovarian cancer (HGSC) exhibits extensive malignant clonal diversity with widespread but non-random patterns of disease dissemination. We investigated whether local immune microenvironment factors shape tumor progression properties at the interface of tumor-infiltrating lymphocytes (TILs) and cancer cells. Through multi-region study of 212 samples from 38 patients with whole-genome sequencing, immunohistochemistry, histologic image analysis, gene expression profiling, and T and B cell receptor sequencing, we identified three immunologic subtypes across samples and extensive within-patient diversity. Epithelial CD8+ TILs negatively associated with malignant diversity, reflecting immunological pruning of tumor clones inferred by neoantigen depletion, HLA I loss of heterozygosity, and spatial tracking between T cell and tumor clones. In addition, combinatorial prognostic effects of mutational processes and immune properties were observed, illuminating how specific genomic aberration types associate with immune response and impact survival. We conclude that within-patient spatial immune microenvironment variation shapes intraperitoneal malignant spread, provoking new evolutionary perspectives on HGSC clonal dispersion.

Clonal fitness inferred from time-series modelling of single-cell cancer genomes.

Progress in defining genomic fitness landscapes in cancer, especially those defined by copy number alterations (CNAs), has been impeded by lack of time-series single-cell sampling of polyclonal populations and temporal statistical models1-7. Here we generated 42,000 genomes from multi-year time-series single-cell whole-genome sequencing of breast epithelium and primary triple-negative breast cancer (TNBC) patient-derived xenografts (PDXs), revealing the nature of CNA-defined clonal fitness dynamics induced by TP53 mutation and cisplatin chemotherapy. Using a new Wright-Fisher population genetics model8,9 to infer clonal fitness, we found that TP53 mutation alters the fitness landscape, reproducibly distributing fitness over a larger number of clones associated with distinct CNAs. Furthermore, in TNBC PDX models with mutated TP53, inferred fitness coefficients from CNA-based genotypes accurately forecast experimentally enforced clonal competition dynamics. Drug treatment in three long-term serially passaged TNBC PDXs resulted in cisplatin-resistant clones emerging from low-fitness phylogenetic lineages in the untreated setting. Conversely, high-fitness clones from treatment-naive controls were eradicated, signalling an inversion of the fitness landscape. Finally, upon release of drug, selection pressure dynamics were reversed, indicating a fitness cost of treatment resistance. Together, our findings define clonal fitness linked to both CNA and therapeutic resistance in polyclonal tumours.

SpatialSort: a Bayesian model for clustering and cell population annotation of spatial proteomics data.

MOTIVATION: Recent advances in spatial proteomics technologies have enabled the profiling of dozens of proteins in thousands of single cells in situ. This has created the opportunity to move beyond quantifying the composition of cell types in tissue, and instead probe the spatial relationships between cells. However, most current methods for clustering data from these assays only consider the expression values of cells and ignore the spatial context. Furthermore, existing approaches do not account for prior information about the expected cell populations in a sample. RESULTS: To address these shortcomings, we developed SpatialSort, a spatially aware Bayesian clustering approach that allows for the incorporation of prior biological knowledge. Our method is able to account for the affinities of cells of different types to neighbour in space, and by incorporating prior information about expected cell populations, it is able to simultaneously improve clustering accuracy and perform automated annotation of clusters. Using synthetic and real data, we show that by using spatial and prior information SpatialSort improves clustering accuracy. We also demonstrate how SpatialSort can perform label transfer between spatial and nonspatial modalities through the analysis of a real world diffuse large B-cell lymphoma dataset. AVAILABILITY AND IMPLEMENTATION: Source code is available on Github at: https://github.com/Roth-Lab/SpatialSort.

Revisiting the vulnerable dark triad hypothesis using a bifactor model.

Miller et al. (2010) previously suggested that borderline pathology, vulnerable narcissism, and Factor 2 psychopathy share a common "Vulnerable Dark Triad" (VDT) core. The present study (N = 1,023 community participants) aims to test that hypothesis using exploratory and confirmatory bifactor analyses. We found support for a bifactor model that obtained satisfactory fits and other adequate validity indices, which included a general VDT factor and three group factors (Reckless, Entitled, Hiding). The general VDT factor was mostly saturated with borderline symptoms items reflecting self-hatred and worthlessness, which did not form a group factor; these results add to previous research suggesting that features of borderline pathology may represent the core of personality pathology. The three group factors had distinctive relationships with Dark Triad traits, pathological trait domains, and aggression. In contrast with the three group factors, the general VDT factor more strongly incremented the prediction of negative affectivity and hostility; the group factors more strongly incremented the prediction of grandiosity, egocentrism, callousness, Machiavellianism, and direct (physical/verbal) aggression. Alignment of the retained bifactor model with influent models of personality pathology and conceptual/methodological implications of the present results for research on the hypothesized VDT are discussed, as well as some clinical implications of the findings.

Identification and characterization of second-generation EZH2 inhibitors with extended residence times and improved biological activity.

The histone methyltransferase EZH2 has been the target of numerous small-molecule inhibitor discovery efforts over the last 10+ years. Emerging clinical data have provided early evidence for single agent activity with acceptable safety profiles for first-generation inhibitors. We have developed kinetic methodologies for studying EZH2-inhibitor-binding kinetics that have allowed us to identify a unique structural modification that results in significant increases in the drug-target residence times of all EZH2 inhibitor scaffolds we have studied. The unexpected residence time enhancement bestowed by this modification has enabled us to create a series of second-generation EZH2 inhibitors with sub-pM binding affinities. We provide both biophysical evidence validating this sub-pM potency and biological evidence demonstrating the utility and relevance of such high-affinity interactions with EZH2.

An Annealed Sequential Monte Carlo Method for Bayesian Phylogenetics.

We describe an "embarrassingly parallel" method for Bayesian phylogenetic inference, annealed Sequential Monte Carlo (SMC), based on recent advances in the SMC literature such as adaptive determination of annealing parameters. The algorithm provides an approximate posterior distribution over trees and evolutionary parameters as well as an unbiased estimator for the marginal likelihood. This unbiasedness property can be used for the purpose of testing the correctness of posterior simulation software. We evaluate the performance of phylogenetic annealed SMC by reviewing and comparing with other computational Bayesian phylogenetic methods, in particular, different marginal likelihood estimation methods. Unlike previous SMC methods in phylogenetics, our annealed method can utilize standard Markov chain Monte Carlo (MCMC) tree moves and hence benefit from the large inventory of such moves available in the literature. Consequently, the annealed SMC method should be relatively easy to incorporate into existing phylogenetic software packages based on MCMC algorithms. We illustrate our method using simulation studies and real data analysis.

Clonal genotype and population structure inference from single-cell tumor sequencing.

Single-cell DNA sequencing has great potential to reveal the clonal genotypes and population structure of human cancers. However, single-cell data suffer from missing values and biased allelic counts as well as false genotype measurements owing to the sequencing of multiple cells. We describe the Single Cell Genotyper (https://bitbucket.org/aroth85/scg), an open-source software based on a statistical model coupled with a mean-field variational inference method, which can be used to address these problems and robustly infer clonal genotypes.

Premature psychotherapy termination in an outpatient treatment program for personality disorders: a survival analysis.

OBJECTIVE: Psychological treatment for patients with personality disorders (PD) is plagued with a high proportion of early dropouts, and attempts to identify risk factors for attrition have generated very few conclusive results. The purpose of the present study is to identify significant predictors of early treatment termination in a long-term psychotherapy program for PD. METHODS: Data was retrospectively retrieved from 174 files of patients who began long-term psychotherapy in an outpatient treatment program in Quebec City, Canada. Socio-demographic, initial disturbance, and diagnostic variables were considered for prediction, along with a measure specifically designed to identify PD patients at risk of dropping out early from psychotherapy, the Treatment Attrition-Retention Scale for Personality Disorders (TARS-PD). Survival analysis using Cox proportional hazard regression was performed to identify significant predictors. RESULTS: Results using univariate Cox proportional hazards regressions revealed that unemployment, Global Assessment of Functioning scores, and recent hetero-aggressive behavior were significant predictors of early dropout in the first six months of therapy. Adjusting for these three confounders, four of the factor scores from the TARS-PD (Narcissism, Secondary gains, Low distress, and Cluster A features) were significantly associated with dropout in univariate Cox proportional hazards regressions. Secondary gains and Narcissism remained significant predictors after entering all five TARS-PD factors in a multivariate Cox proportional hazards regression adjusting for confounders. CONCLUSIONS: Taking into consideration specific treatment prognosis variables, such as those measured by the TARS-PD, might be more useful for dropout prediction in PD patients in comparison with more general demographic and diagnostic variables.

Premature Termination of Psychotherapy in Patients With Borderline Personality Disorder: A Cluster-Analytic Study.

The goal of the present study was to establish profiles of patients with borderline personality disorder (BPD) who dropped out early from an outpatient psychotherapy program. From a sample of 56 BPD patients who dropped out after the first of a three-year program, a TwoStep cluster analysis procedure was performed, using the five factors of the Treatment Attrition-Retention Scale for Personality Disorders (Gamache et al., J Pers Disord 1-21, 2017) and the Global Assessment of Functioning score (Spitzer et al., Global Assessment of Functioning [GAF] Scale. In Sederer LI, Dickey B [Eds], Outcomes assessment in clinical practice [pp 76-78]. Baltimore, MD: Walter and Williams) as clustering variables. Four clusters emerged: Higher-functioning, Narcissistic features/entitlement, Pseudo-normality, and Highly dysfunctional. Differences between the clusters were found on sex, occupational status, and presence of antisocial features. These findings could help both identify BPD patients at potential risk of dropping out of psychotherapy and adjust interventions accordingly to reduce premature termination.

Regulatory T Cell Modulation by CBP/EP300 Bromodomain Inhibition.

Covalent modification of histones is a fundamental mechanism of regulated gene expression in eukaryotes, and interpretation of histone modifications is an essential feature of epigenetic control. Bromodomains are specialized binding modules that interact with acetylated histones, linking chromatin recognition to gene transcription. Because of their ability to function in a domain-specific fashion, selective disruption of bromodomain:acetylated histone interactions with chemical probes serves as a powerful means for understanding biological processes regulated by these chromatin adaptors. Here we describe the discovery and characterization of potent and selective small molecule inhibitors for the bromodomains of CREBBP/EP300 that engage their target in cellular assays. We use these tools to demonstrate a critical role for CREBBP/EP300 bromodomains in regulatory T cell biology. Because regulatory T cell recruitment to tumors is a major mechanism of immune evasion by cancer cells, our data highlight the importance of CREBBP/EP300 bromodomain inhibition as a novel, small molecule-based approach for cancer immunotherapy.

PyClone: statistical inference of clonal population structure in cancer.

We introduce PyClone, a statistical model for inference of clonal population structures in cancers. PyClone is a Bayesian clustering method for grouping sets of deeply sequenced somatic mutations into putative clonal clusters while estimating their cellular prevalences and accounting for allelic imbalances introduced by segmental copy-number changes and normal-cell contamination. Single-cell sequencing validation demonstrates PyClone's accuracy.

Inhibition of bromodomain-containing protein 9 for the prevention of epigenetically-defined drug resistance.

Bromodomain-containing protein 9 (BRD9), an epigenetic "reader" of acetylated lysines on post-translationally modified histone proteins, is upregulated in multiple cancer cell lines. To assess the functional role of BRD9 in cancer cell lines, we identified a small-molecule inhibitor of the BRD9 bromodomain. Starting from a pyrrolopyridone lead, we used structure-based drug design to identify a potent and highly selective in vitro tool compound 11, (GNE-375). While this compound showed minimal effects in cell viability or gene expression assays, it showed remarkable potency in preventing the emergence of a drug tolerant population in EGFR mutant PC9 cells treated with EGFR inhibitors. Such tolerance has been linked to an altered epigenetic state, and 11 decreased BRD9 binding to chromatin, and this was associated with decreased expression of ALDH1A1, a gene previously shown to be important in drug tolerance. BRD9 inhibitors may therefore show utility in preventing epigenetically-defined drug resistance.

Evolutionary inference via the Poisson Indel Process.

We address the problem of the joint statistical inference of phylogenetic trees and multiple sequence alignments from unaligned molecular sequences. This problem is generally formulated in terms of string-valued evolutionary processes along the branches of a phylogenetic tree. The classic evolutionary process, the TKF91 model [Thorne JL, Kishino H, Felsenstein J (1991) J Mol Evol 33(2):114-124] is a continuous-time Markov chain model composed of insertion, deletion, and substitution events. Unfortunately, this model gives rise to an intractable computational problem: The computation of the marginal likelihood under the TKF91 model is exponential in the number of taxa. In this work, we present a stochastic process, the Poisson Indel Process (PIP), in which the complexity of this computation is reduced to linear. The Poisson Indel Process is closely related to the TKF91 model, differing only in its treatment of insertions, but it has a global characterization as a Poisson process on the phylogeny. Standard results for Poisson processes allow key computations to be decoupled, which yields the favorable computational profile of inference under the PIP model. We present illustrative experiments in which Bayesian inference under the PIP model is compared with separate inference of phylogenies and alignments.

Automated reconstruction of ancient languages using probabilistic models of sound change.

One of the oldest problems in linguistics is reconstructing the words that appeared in the protolanguages from which modern languages evolved. Identifying the forms of these ancient languages makes it possible to evaluate proposals about the nature of language change and to draw inferences about human history. Protolanguages are typically reconstructed using a painstaking manual process known as the comparative method. We present a family of probabilistic models of sound change as well as algorithms for performing inference in these models. The resulting system automatically and accurately reconstructs protolanguages from modern languages. We apply this system to 637 Austronesian languages, providing an accurate, large-scale automatic reconstruction of a set of protolanguages. Over 85% of the system's reconstructions are within one character of the manual reconstruction provided by a linguist specializing in Austronesian languages. Being able to automatically reconstruct large numbers of languages provides a useful way to quantitatively explore hypotheses about the factors determining which sounds in a language are likely to change over time. We demonstrate this by showing that the reconstructed Austronesian protolanguages provide compelling support for a hypothesis about the relationship between the function of a sound and its probability of changing that was first proposed in 1955.

Development of methyl isoxazoleazepines as inhibitors of BET.

In this report we detail the evolution of our previously reported thiophene isoxazole BET inhibitor chemotype exemplified by CPI-3 to a novel bromodomain selective chemotype (the methyl isoxazoleazepine chemotype) exemplified by carboxamide 23. The methyl isoxazoleazepine chemotype provides potent inhibition of the bromodomains of the BET family, excellent in vivo PK across species, low unbound clearance, and target engagement in a MYC PK-PD model.

Comprehensive Target Engagement by the EZH2 Inhibitor Tulmimetostat Allows for Targeting of ARID1A Mutant Cancers.

Recurrent somatic mutations in the BAF chromatin remodeling complex subunit ARID1A occur frequently in advanced urothelial carcinoma, endometrial cancers, and ovarian clear cell carcinoma, creating an alternative chromatin state that may be exploited therapeutically. The histone methyltransferase EZH2 has previously been identified as targetable vulnerability in the context of ARID1A mutations. Here, we describe the discovery of tulmimetostat, an orally available, clinical stage EZH2 inhibitor and elucidate its therapeutic potential for treating ARID1A mutant tumors. Tulmimetostat administration achieved efficacy in multiple ARID1A mutant bladder, ovarian, and endometrial tumor models and improved cisplatin response in chemotherapy-resistant models. Consistent with its comprehensive and durable level of target coverage, tulmimetostat demonstrated greater efficacy than other PRC2-targeted inhibitors at comparable or lower exposures in a bladder cancer xenograft mouse model. Tulmimetostat mediated extensive changes in gene expression in addition to a profound reduction in global H3K27me3 levels in tumors. Phase I clinical pharmacokinetic and pharmacodynamic data indicated that tulmimetostat exhibits durable exposure and profound target engagement. Importantly, a tulmimetostat controlled gene expression signature identified in whole blood from a cohort of 32 cancer patients correlated with tulmimetostat exposure, representing a pharmacodynamic marker for the assessment of target coverage for PRC2-targeted agents in the clinic. Collectively, this data suggests that tulmimetostat has the potential to achieve clinical benefit in solid tumors as a monotherapy but also in combination with chemotherapeutic agents and may be beneficial in various indications with recurrent ARID1A mutations.

Phylogenetic inference via sequential Monte Carlo.

Bayesian inference provides an appealing general framework for phylogenetic analysis, able to incorporate a wide variety of modeling assumptions and to provide a coherent treatment of uncertainty. Existing computational approaches to bayesian inference based on Markov chain Monte Carlo (MCMC) have not, however, kept pace with the scale of the data analysis problems in phylogenetics, and this has hindered the adoption of bayesian methods. In this paper, we present an alternative to MCMC based on Sequential Monte Carlo (SMC). We develop an extension of classical SMC based on partially ordered sets and show how to apply this framework--which we refer to as PosetSMC--to phylogenetic analysis. We provide a theoretical treatment of PosetSMC and also present experimental evaluation of PosetSMC on both synthetic and real data. The empirical results demonstrate that PosetSMC is a very promising alternative to MCMC, providing up to two orders of magnitude faster convergence. We discuss other factors favorable to the adoption of PosetSMC in phylogenetics, including its ability to estimate marginal likelihoods, its ready implementability on parallel and distributed computing platforms, and the possibility of combining with MCMC in hybrid MCMC-SMC schemes. Software for PosetSMC is available at http://www.stat.ubc.ca/ bouchard/PosetSMC.

A note on probabilistic models over strings: the linear algebra approach.

Probabilistic models over strings have played a key role in developing methods that take into consideration indels as phylogenetically informative events. There is an extensive literature on using automata and transducers on phylogenies to do inference on these probabilistic models, in which an important theoretical question is the complexity of computing the normalization of a class of string-valued graphical models. This question has been investigated using tools from combinatorics, dynamic programming, and graph theory, and has practical applications in Bayesian phylogenetics. In this work, we revisit this theoretical question from a different point of view, based on linear algebra. The main contribution is a set of results based on this linear algebra view that facilitate the analysis and design of inference algorithms on string-valued graphical models. As an illustration, we use this method to give a new elementary proof of a known result on the complexity of inference on the "TKF91" model, a well-known probabilistic model over strings. Compared to previous work, our proving method is easier to extend to other models, since it relies on a novel weak condition, triangular transducers, which is easy to establish in practice. The linear algebra view provides a concise way of describing transducer algorithms and their compositions, opens the possibility of transferring fast linear algebra libraries (for example, based on GPUs), as well as low rank matrix approximation methods, to string-valued inference problems.

GNE-064: A Potent, Selective, and Orally Bioavailable Chemical Probe for the Bromodomains of SMARCA2 and SMARCA4 and the Fifth Bromodomain of PBRM1.

Bromodomains are acetyllysine recognition domains present in a variety of human proteins. Bromodomains also bind small molecules that compete with acetyllysine, and therefore bromodomains have been targets for drug discovery efforts. Highly potent and selective ligands with good cellular permeability have been proposed as chemical probes for use in exploring the functions of many of the bromodomain proteins. We report here the discovery of a class of such inhibitors targeting the family VIII bromodomains of SMARCA2 (BRM) and SMARCA4 (BRG1), and PBRM1 (polybromo-1) bromodomain 5. We propose one example from this series, GNE-064, as a chemical probe for the bromodomains SMARCA2, SMARCA4, and PBRM1(5) with the potential for in vivo use.

Accurate determination of CRISPR-mediated gene fitness in transplantable tumours.

Assessing tumour gene fitness in physiologically-relevant model systems is challenging due to biological features of in vivo tumour regeneration, including extreme variations in single cell lineage progeny. Here we develop a reproducible, quantitative approach to pooled genetic perturbation in patient-derived xenografts (PDXs), by encoding single cell output from transplanted CRISPR-transduced cells in combination with a Bayesian hierarchical model. We apply this to 181 PDX transplants from 21 breast cancer patients. We show that uncertainty in fitness estimates depends critically on the number of transplant cell clones and the variability in clone sizes. We use a pathway-directed allelic series to characterize Notch signaling, and quantify TP53 / MDM2 drug-gene conditional fitness in outlier patients. We show that fitness outlier identification can be mirrored by pharmacological perturbation. Overall, we demonstrate that the gene fitness landscape in breast PDXs is dominated by inter-patient differences.