Distinct Cellular Mechanisms Underlie Anti-CTLA-4 and Anti-PD-1 Checkpoint Blockade.

Immune-checkpoint blockade is able to achieve durable responses in a subset of patients; however, we lack a satisfying comprehension of the underlying mechanisms of anti-CTLA-4- and anti-PD-1-induced tumor rejection. To address these issues, we utilized mass cytometry to comprehensively profile the effects of checkpoint blockade on tumor immune infiltrates in human melanoma and murine tumor models. These analyses reveal a spectrum of tumor-infiltrating T cell populations that are highly similar between tumor models and indicate that checkpoint blockade targets only specific subsets of tumor-infiltrating T cell populations. Anti-PD-1 predominantly induces the expansion of specific tumor-infiltrating exhausted-like CD8 T cell subsets. In contrast, anti-CTLA-4 induces the expansion of an ICOS+ Th1-like CD4 effector population in addition to engaging specific subsets of exhausted-like CD8 T cells. Thus, our findings indicate that anti-CTLA-4 and anti-PD-1 checkpoint-blockade-induced immune responses are driven by distinct cellular mechanisms.

An Immune Atlas of Clear Cell Renal Cell Carcinoma.

Immune cells in the tumor microenvironment modulate cancer progression and are attractive therapeutic targets. Macrophages and T cells are key components of the microenvironment, yet their phenotypes and relationships in this ecosystem and to clinical outcomes are ill defined. We used mass cytometry with extensive antibody panels to perform in-depth immune profiling of samples from 73 clear cell renal cell carcinoma (ccRCC) patients and five healthy controls. In 3.5 million measured cells, we identified 17 tumor-associated macrophage phenotypes, 22 T cell phenotypes, and a distinct immune composition correlated with progression-free survival, thereby presenting an in-depth human atlas of the immune tumor microenvironment in this disease. This study revealed potential biomarkers and targets for immunotherapy development and validated tools that can be used for immune profiling of other tumor types.

Innate Immune Landscape in Early Lung Adenocarcinoma by Paired Single-Cell Analyses.

To guide the design of immunotherapy strategies for patients with early stage lung tumors, we developed a multiscale immune profiling strategy to map the immune landscape of early lung adenocarcinoma lesions to search for tumor-driven immune changes. Utilizing a barcoding method that allows a simultaneous single-cell analysis of the tumor, non-involved lung, and blood cells, we provide a detailed immune cell atlas of early lung tumors. We show that stage I lung adenocarcinoma lesions already harbor significantly altered T cell and NK cell compartments. Moreover, we identified changes in tumor-infiltrating myeloid cell (TIM) subsets that likely compromise anti-tumor T cell immunity. Paired single-cell analyses thus offer valuable knowledge of tumor-driven immune changes, providing a powerful tool for the rational design of immune therapies. VIDEO ABSTRACT.

Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis.

Acute myeloid leukemia (AML) manifests as phenotypically and functionally diverse cells, often within the same patient. Intratumor phenotypic and functional heterogeneity have been linked primarily by physical sorting experiments, which assume that functionally distinct subpopulations can be prospectively isolated by surface phenotypes. This assumption has proven problematic, and we therefore developed a data-driven approach. Using mass cytometry, we profiled surface and intracellular signaling proteins simultaneously in millions of healthy and leukemic cells. We developed PhenoGraph, which algorithmically defines phenotypes in high-dimensional single-cell data. PhenoGraph revealed that the surface phenotypes of leukemic blasts do not necessarily reflect their intracellular state. Using hematopoietic progenitors, we defined a signaling-based measure of cellular phenotype, which led to isolation of a gene expression signature that was predictive of survival in independent cohorts. This study presents new methods for large-scale analysis of single-cell heterogeneity and demonstrates their utility, yielding insights into AML pathophysiology.

Negative Co-stimulation Constrains T Cell Differentiation by Imposing Boundaries on Possible Cell States.

Co-stimulation regulates T cell activation, but it remains unclear whether co-stimulatory pathways also control T cell differentiation. We used mass cytometry to profile T cells generated in the genetic absence of the negative co-stimulatory molecules CTLA-4 and PD-1. Our data indicate that negative co-stimulation constrains the possible cell states that peripheral T cells can acquire. CTLA-4 imposes major boundaries on CD4+ T cell phenotypes, whereas PD-1 subtly limits CD8+ T cell phenotypes. By computationally reconstructing T cell differentiation paths, we identified protein expression changes that underlied the abnormal phenotypic expansion and pinpointed when lineage choice events occurred during differentiation. Similar alterations in T cell phenotypes were observed after anti-CTLA-4 and anti-PD-1 antibody blockade. These findings implicate negative co-stimulation as a key regulator and determinant of T cell differentiation and suggest that checkpoint blockade might work in part by altering the limits of T cell phenotypes.

Competition for time: Evidence for an overlooked, diversity-maintaining competitive mechanism.

Understanding how diversity is maintained in plant communities requires that we first understand the mechanisms of competition for limiting resources. In ecology, there is an underappreciated but fundamental distinction between systems in which the depletion of limiting resources reduces the growth rates of competitors and systems in which resource depletion reduces the time available for competitors to grow, a mechanism we call 'competition for time'. Importantly, modern community ecology and our framing of the coexistence problem are built on the implicit assumption that competition reduces the growth rate. However, recent theoretical work suggests competition for time may be the predominant competitive mechanism in a broad array of natural communities, a significant advance given that when species compete for time, diversity-maintaining trade-offs emerge organically. In this study, we first introduce competition for time conceptually using a simple model of interacting species. Then, we perform an experiment in a Mediterranean annual grassland to determine whether competition for time is an important competitive mechanism in a field system. Indeed, we find that species respond to increased competition through reductions in their lifespan rather than their rate of growth. In total, our study suggests competition for time may be overlooked as a mechanism of biodiversity maintenance.

Competition for water and species coexistence in phenologically structured annual plant communities.

Both competition for water and phenological variation are important determinants of plant community structure, but ecologists lack a synthetic theory for how they affect coexistence outcomes. We developed an analytically tractable model of water competition for Mediterranean annual communities and demonstrated that variation in phenology alone can maintain high diversity in spatially homogenous assemblages of water-limited plants. We modelled a system where all water arrives early in the season and species vary in their ability to grow under drying conditions. As a consequence, species differ in growing season length and compete by shortening the growing season of their competitors. This model replicates and offers mechanistic explanations for patterns observed in empirical studies of how phenology influences coexistence among Mediterranean annuals. Additionally, we found that a decreasing, concave-up trade-off between growth rate and access to water can maintain high diversity under simple but realistic assumptions. High diversity is possible because: (1) later plants escape competition after their earlier season competitors have gone to seed and (2) early-season species are more than compensated for their shortened growing season by a growth rate advantage. Together, these mechanisms provide an explanation for how phenologically variable annual plant species might coexist when competing only for water.

Ecological lags govern the pace and outcome of plant community responses to 21st-century climate change.

Forecasting the trajectories of species assemblages in response to ongoing climate change requires quantifying the time lags in the demographic and ecological processes through which climate impacts species' abundances. Since experimental climate manipulations are typically abrupt, the observed species responses may not match their responses to gradual climate change. We addressed this problem by transplanting alpine grassland turfs to lower elevations, recording species' demographic responses to climate and competition, and using these data to parameterise community dynamics models forced by scenarios of gradual climate change. We found that shifts in community structure following an abrupt climate manipulation were not simply accelerated versions of shifts expected under gradual warming, as the former missed the transient rise of species benefiting from moderate warming. Time lags in demography and species interactions controlled the pace and trajectory of changing species' abundances under simulated 21st-century climate change, and thereby prevented immediate diversity loss.

Combination anti-CTLA-4 plus anti-PD-1 checkpoint blockade utilizes cellular mechanisms partially distinct from monotherapies.

Immune checkpoint blockade therapy targets T cell-negative costimulatory molecules such as cytotoxic T lymphocyte antigen-4 (CTLA-4) and programmed cell death-1 (PD-1). Combination anti-CTLA-4 and anti-PD-1 blockade therapy has enhanced efficacy, but it remains unclear through what mechanisms such effects are mediated. A critical question is whether combination therapy targets and modulates the same T cell populations as monotherapies. Using a mass cytometry-based systems approach, we comprehensively profiled the response of T cell populations to monotherapy and combination anti-CTLA-4 plus anti-PD-1 therapy in syngeneic murine tumors and clinical samples. Most effects of monotherapies were additive in the context of combination therapy; however, multiple combination therapy-specific effects were observed. Highly phenotypically exhausted cluster of differentiation 8 (CD8) T cells expand in frequency following anti-PD-1 monotherapy but not combination therapy, while activated terminally differentiated effector CD8 T cells expand only following combination therapy. Combination therapy also led to further increased frequency of T helper type 1 (Th1)-like CD4 effector T cells even though anti-PD-1 monotherapy is not sufficient to do so. Mass cytometry analyses of peripheral blood from melanoma patients treated with immune checkpoint blockade therapies similarly revealed mostly additive effects on the frequencies of T cell subsets along with unique modulation of terminally differentiated effector CD8 T cells by combination ipilimumab plus nivolumab therapy. Together, these findings indicate that dual blockade of CTLA-4 and PD-1 therapy is sufficient to induce unique cellular responses compared with either monotherapy.

Reductions in Drinking Predict Increased Distress: Between- and Within-Person Associations between Alcohol Use and Psychological Distress During and Following Treatment.

BACKGROUND: As the nature of the association between alcohol use disorder (AUD) and other disorders is not well understood, the ways in which psychological distress changes during the course of treatment for AUD are relatively unknown. Existing literatures posit 2 competing hypotheses such that treatment for AUD concurrently decreases alcohol use and psychological distress or treatment for AUD decreases alcohol use and increases psychological distress. The current study examined the ways in which psychological distress changed as a function of treatment for AUD, including the relationship between psychological distress and drinking behaviors. METHODS: Secondary data analysis was conducted on an existing clinical trial dataset that investigated the effect of cognitive-behavioral therapy and therapeutic alliance feedback on AUDs. Specifically, data collected at baseline, posttreatment, 3-month, 6-month, 9-month, and 12-month follow-up assessments were examined. RESULTS: Results indicated decreases in heavy drinking days, increases in percentage of days abstinent, and decreases in overall psychological distress. Findings also revealed that changes in psychological distress did not predict changes in drinking at the next time interval; however, decreases in drinking predicted higher psychological distress at the next assessment. Further, average levels of psychological distress were positively associated with rates of drinking. CONCLUSIONS: The current study provides some insight into how psychological distress changes during the course of treatment for AUD, including the relationship between changes in drinking and such symptoms. Future research should continue to explore these relationships, including the ways in which treatment efforts can address what may be seen as paradoxical effects.

Development and Psychometric Evaluation of a Brief Approach and Avoidance of Alcohol Questionnaire.

BACKGROUND: The Approach and Avoidance of Alcohol Questionnaire (AAAQ) was developed as a measure of craving to assess both desires to consume and desires to avoid consuming alcohol. Although the measure has been used in a variety of populations to predict future alcohol use behavior, the factor structures observed vary based on sample type (e.g., clinical vs. college samples) and may be overly long for use in repeated measures designs. The current article describes the development of a brief version of the AAAQ for use in clinical populations. METHODS: Using existing data sets of individuals in treatment for alcohol use disorder, exploratory analyses (e.g., exploratory factor analysis and item response theory) were conducted using an inpatient sample (N = 298) at a substance abuse treatment facility. Confirmatory analyses (e.g., confirmatory factor analysis and multiple regression) were conducted using an inpatient detoxification sample (N = 175) and a longitudinal outpatient treatment sample (N = 53). RESULTS: The brief AAAQ had comparable internal consistency, explained a similar amount of variance in alcohol consumption and related problems, and exhibited superior model fit as compared to the original measure. CONCLUSIONS: These findings indicate that the brief AAAQ is an effective tool to assess alcohol craving in clinical populations in treatment settings.

Disaggregating within- and between-person effects of affect on drinking behavior in a clinical sample with alcohol use disorder.

OBJECTIVE: The goal of the current study was to better understand affect-drinking relations among those diagnosed with an alcohol use disorder (AUD), as recent meta-analytic work suggests that daily negative affect may not universally predict subsequent alcohol consumption in those nondependent on alcohol. Specifically, we investigated the between- and within-person effects of positive and negative affects on drinking. METHOD: Participants (n = 92) who met AUD diagnostic criteria completed a 90-day daily assessment of drinking behavior and positive and negative affects. RESULTS: Time-lagged multilevel modeling revealed that within-person elevations in negative affect predicted increased odds and quantity of drinking later in the day. Relations between positive affect and drinking were nonsignificant. CONCLUSIONS: These findings are in contrast to recent meta-analytic findings and highlight the complexity of affect-drinking relations among those diagnosed with AUD. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Analyzing Digital Evidence From a Telemental Health Platform to Assess Complex Psychological Responses to the COVID-19 Pandemic: Content Analysis of Text Messages.

BACKGROUND: The novel COVID-19 disease has negatively impacted mortality, economic conditions, and mental health. These impacts are likely to continue after the COVID-19 pandemic ends. There are no methods for characterizing the mental health burden of the COVID-19 pandemic, and differentiating this burden from that of the prepandemic era. Accurate illness detection methods are critical for facilitating pandemic-related treatment and preventing the worsening of symptoms. OBJECTIVE: We aimed to identify major themes and symptom clusters in the SMS text messages that patients send to therapists. We assessed patients who were seeking treatment for pandemic-related distress on Talkspace, which is a popular telemental health platform. METHODS: We used a machine learning algorithm to identify patients' pandemic-related concerns, based on their SMS text messages in a large, digital mental health service platform (ie, Talkspace). This platform uses natural language processing methods to analyze unstructured therapy transcript data, in parallel with brief clinical assessment methods for analyzing depression and anxiety symptoms. RESULTS: Our results show a significant increase in the incidence of COVID-19-related intake anxiety symptoms (P<.001), but no significant differences in the incidence of intake depression symptoms (P=.79). During our transcript analyses, we identified terms that were related to 24 symptoms outside of those included in the diagnostic criteria for anxiety and depression. CONCLUSIONS: Our findings for Talkspace suggest that people who seek treatment during the pandemic experience more severe intake anxiety than they did before the COVID-19 outbreak. It is important to monitor the symptoms that we identified in this study and the symptoms of anxiety and depression, to fully understand the effects of the COVID-19 pandemic on mental health.

Psychometric Evaluation of the Approach and Avoidance of Alcohol Questionnaire in a Community Sample.

Contrary to most theories of alcohol craving, which only consider desires to use, the Ambivalence Model of Craving (AMC) conceptualizes craving bi-dimensionally as the concurrent desires to consume (approach) and desires not to consume (avoid) alcohol. Consistent with the AMC, the Approach and Avoidance of Alcohol Questionnaire (AAAQ) is a 14-item self-reported measure designed to assess alcohol approach and avoidance inclinations. Scores on the AAAQ have been shown to have high reliability and validity in clinical and college student populations, with a two-factor solution emerging in clinical samples of patients with Alcohol Use Disorders (AUDs) and a three-factor solution in samples of college students. However, despite a number of studies examining the use of the AAAQ in clinical and college student samples, to the best of our knowledge there have not been any psychometric evaluations of the AAAQ in community samples. The current study examined the psychometric properties of the AAAQ by examining the factor structure in a community sample (N = 537). Consistent with the results of previous studies utilizing non-clinical samples, a three-factor solution fit the data best and was invariant across gender. Additionally, all three factors were significantly associated with variables of drinking behavior. These results suggest that the AAAQ provides valid and reliable scores that measure approach and avoidance inclinations in community populations.

Characterization of cell fate probabilities in single-cell data with Palantir.

Single-cell RNA sequencing studies of differentiating systems have raised fundamental questions regarding the discrete versus continuous nature of both differentiation and cell fate. Here we present Palantir, an algorithm that models trajectories of differentiating cells by treating cell fate as a probabilistic process and leverages entropy to measure cell plasticity along the trajectory. Palantir generates a high-resolution pseudo-time ordering of cells and, for each cell state, assigns a probability of differentiating into each terminal state. We apply our algorithm to human bone marrow single-cell RNA sequencing data and detect important landmarks of hematopoietic differentiation. Palantir's resolution enables the identification of key transcription factors that drive lineage fate choice and closely track when cells lose plasticity. We show that Palantir outperforms existing algorithms in identifying cell lineages and recapitulating gene expression trends during differentiation, is generalizable to diverse tissue types, and is well-suited to resolving less-studied differentiating systems.

Author Correction: Characterization of cell fate probabilities in single-cell data with Palantir.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Applying a multidimensional model of craving to disordered eating behaviors: Development of the Food Approach and Avoidance Questionnaire.

Despite revisions to the DSM-5, current diagnostic criteria poorly capture the phenomena of eating disorders. The construct of food craving may help to explain the range of disordered eating and compensatory behaviors, but current measures do not fully capture the construct. Borrowing from the substance use literature and emphasizing both approach and avoidance craving inclinations, the ambivalence model of craving (AMC) provides a useful framework for predicting broad patterns of disordered eating behaviors. This study sought to develop and preliminarily validate a multidimensional AMC-based measure of food craving. Items for the Food Approach and Avoidance Questionnaire were generated and development and validation data were collected via online survey from community-based adults and university students (N = 1,070). Exploratory factor and item response theory analyses were used for measure development. Linear regressions were used to examine convergent and discriminant validity. Exploratory sensitivity analyses included logistic regressions and receiver operating characteristic curves. As hypothesized, a 2-factor measure was supported. No sex differences emerged in item functioning. The approach factor was associated with greater trait food craving, more uncontrolled eating, and greater likelihood of meeting self-reported diagnostic criteria for bulimia nervosa and binge eating disorder. The avoidance factor was associated with higher levels of restrained eating, drive for thinness, and an increased likelihood of meeting self-reported diagnostic criteria for anorexia nervosa and bulimia nervosa. Preliminary data support a new multidimensional measure of approach and avoidance food craving with potential for a transdiagnostic conceptualization of disordered eating and compensatory behaviors. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Health-related internet use among opioid treatment patients.

The Internet and smartphones have become commonplace and can be effective in overcoming traditional barriers to accessing health information about substance use disorders (SUD), and their prevention or treatment. Little is known, however, about specific factors that may influence the use of these technologies among socioeconomically disadvantaged populations with SUDs. This study characterized the use of digital technologies and the Internet among individuals receiving treatment for opioid use disorder, focusing on identifying predictors of Internet use for health-related purposes. Participants came from an urban opioid replacement therapy program and completed a face-to-face survey on Internet and technology use. We examined the association between online health information seeking and technology acceptance variables, including perceived usefulness, effort expectancy, social influence, and facilitating conditions (e.g., availability of devices/services and technical support). Participants (N = 178, ages 18-64) endorsed high rates of current smartphone ownership (94%) and everyday Internet use (67%). 88% of participants reported searching online for information about health or medical topics in the past 3 months. Predictors of Internet use for health-related purposes were higher technology acceptance for mobile Internet use, younger age, current employment, and less bodily pain. Our results demonstrate high acceptance and use of mobile technology and the Internet among this sample of socioeconomically disadvantaged individuals with SUDs. However, these findings also highlight the importance of identifying barriers that disadvantaged groups face in using mobile technologies when designing technology-based interventions for this population.

Impact of Pretreatment Change on Mechanism of Behavior Change Research: An Applied Example Using Alcohol Abstinence Self-Efficacy.

OBJECTIVE: With the growing recognition that, for some, significant changes in drinking occur before the first treatment session (i.e., pretreatment change), researchers have called for the careful assessment of when change occurs and its potential impact on mechanism of behavior change (MOBC) research. Using a commonly hypothesized MOBC variable, alcohol abstinence self-efficacy, the primary aim of this study was to examine the effect of pretreatment change on the study of MOBCs. METHOD: Sixty-three individuals diagnosed with alcohol dependence were recruited to participate in a 12-week cognitive-behavioral treatment. Participants completed weekly assessments of self-efficacy and drinking behaviors. RESULTS: Multilevel time-lagged regression models indicated that pretreatment change significantly moderated the effect of self-efficacy on the number of drinking days, such that among those higher on pretreatment change, higher self-efficacy ratings predicted lower rates of drinking days in the week until the next treatment session. In contrast, pretreatment change did not moderate the effect of self-efficacy on the rate of heavy drinking days. CONCLUSIONS: Results from the current study add to a small but growing body of research highlighting the importance of pretreatment change when studying MOBCs. Further, these results provide important insights into the conditions in which self-efficacy may play an important role in treatment outcomes.