Highly variable timing renders immunotherapy efficacy and toxicity impractical biomarkers of one another in clinical practice.

Background: A useful clinical biomarker requires not only association but also a consistent temporal relationship. For instance, chemotherapy-induced neutropenia and epidermal growth-factor inhibitor-related acneiform rash both occur within weeks of treatment initiation, thereby providing information prior to efficacy assessment. Although immune checkpoint inhibitor (ICI)-associated immune-related adverse events (irAE) have been associated with therapeutic benefit, irAE may have delayed and highly variable onset. To determine whether ICI efficacy and irAE could serve as clinically useful biomarkers for predicting each other, we determined the temporal relationship between initial efficacy assessment and irAE onset in a diverse population treated with ICI. Methods: Using two-sided Fisher exact and Cochran-Armitage tests, we determined the relative timing of initial efficacy assessment and irAE occurrence in a cohort of 155 ICI-treated patients (median age 68 years, 40% women). Results: Initial efficacy assessment was performed a median of 50 days [interquartile range (IQR) 39-59 days] after ICI initiation; median time to any irAE was 77 days (IQR 28-145 days) after ICI initiation. Median time to first irAE was 42 days (IQR 20-88 days). Overall, 58% of any irAE and 47% of first irAE occurred after initial efficacy assessment. For clinically significant (grade ≥2) irAE, 60% of any and 53% of first occurred after initial efficacy assessment. The likelihood of any future irAE did not differ according to response (45% for complete or partial response vs. 47% for other cases; P=1). In landmark analyses controlling for clinical and toxicity follow-up, patients demonstrating greater tumor shrinkage at initial efficacy assessment were more likely to develop future grade ≥2 (P=0.05) and multi-organ (P=0.02) irAE. Conclusions: In contrast to that seen with chemotherapy and molecularly targeted therapies, the temporal relationship between ICI efficacy and toxicity is complex and bidirectional. In practice, neither parameter can be routinely relied on as a clinical biomarker to predict the other.

Systemic immune parameters after prior radiation therapy in patients receiving immune checkpoint inhibitors.

INTRODUCTION: Preclinical studies have demonstrated the ability of radiation therapy (RT) to augment immune response and tumor control by immune checkpoint inhibitors (ICI). However, numerous clinical trials combining RT and ICI have yielded relatively disappointing results. To improve understanding of optimal use of these therapies, we assessed systemic immune effects of prior RT in patients receiving ICI. METHODS AND MATERIALS: Pre- and post-ICI blood samples were collected from patients enrolled in a prospective immunotherapy biospecimen protocol. Mutiplex panels of 40 cytokines and 120 autoantibodies (Ab) were analyzed. We identified differences in these parameters according to receipt, timing, and type of prior RT. We calculated P values using the Pearson product-moment correlation coefficient and false discovery rate (FDR) using the Benjamini-Hochberg Procedure. RESULTS: Among 277 total patients, 69 (25%) received RT in the 6 months prior to ICI initiation. Among RT-treated patients, 23 (33%) received stereotactic RT, and 33 (48%) received curative intent RT. There was no significant difference in demographics or type of immunotherapy between patients according to prior RT exposure. Baseline complement C8 Ab and MIP-1d/CCL15 were significantly higher among patients with prior RT. For MIP-1d/CCL15, only prior stereotactic RT was associated with significant differences. CONCLUSIONS: Prior RT is associated with few changes in systemic immune parameters in patients receiving ICI. The underlying mechanisms and optimal approach to harnessing the potential synergy of RT and ICI require further prospective clinical investigation.

Association between Antibiotic Exposure and Systemic Immune Parameters in Cancer Patients Receiving Checkpoint Inhibitor Therapy.

Antibiotic administration is associated with worse clinical outcomes and changes to the gut microbiome in cancer patients receiving immune checkpoint inhibitors (ICI). However, the effects of antibiotics on systemic immune function are unknown. We, therefore, evaluated antibiotic exposure, therapeutic responses, and multiplex panels of 40 serum cytokines and 124 antibodies at baseline and six weeks after ICI initiation, with p < 0.05 and false discovery rate (FDR) < 0.2 considered significant. A total of 251 patients were included, of whom the 135 (54%) who received antibiotics had lower response rates and shorter survival. Patients who received antibiotics prior to ICI initiation had modestly but significantly lower baseline levels of nucleolin, MDA5, c-reactive protein, and liver cytosol antigen type 1 (LC1) antibodies, as well as higher levels of heparin sulfate and Matrigel antibodies. After ICI initiation, antibiotic-treated patients had significantly lower levels of MDA5, CENP.B, and nucleolin antibodies. Although there were no clear differences in cytokines in the overall cohort, in the lung cancer subset (53% of the study population), we observed differences in IFN-γ, IL-8, and macrophage inflammatory proteins. In ICI-treated patients, antibiotic exposure is associated with changes in certain antibodies and cytokines. Understanding the relationship between these factors may improve the clinical management of patients receiving ICI.

Divergent prognostic effects of pre-existing and treatment-emergent thyroid dysfunction in patients treated with immune checkpoint inhibitors.

BACKGROUND: Thyroid dysfunction is among the most common autoimmune diseases and immune checkpoint inhibitor (ICI)-induced immune-related adverse events (irAE). We determined the association between longitudinal thyroid function and clinical outcomes in patients treated with ICI. METHODS: We identified all patients treated with ICI at UT Southwestern Medical Center from January 1, 2011, through December 31, 2020. We defined normal thyroid stimulating hormone (TSH) and free thyroxine (FT4) levels according to institutional reference range. We defined clinical thyroid dysfunction using established criteria incorporating labs and treatment. We determined the association between thyroid function and overall survival (OS) using Kaplan-Meier curves, log-rank tests, and multivariate Cox proportional hazards model. RESULTS: A total of 1781 patients were included in analyses, of whom 381 (21%) had abnormal baseline TSH. Patients with abnormal baseline TSH were more likely to be female, have kidney cancer, and initiate levothyroxine after ICI initiation (all P < 0.001). Patients with abnormal baseline TSH had inferior OS (median 16 vs 27 months; P < 0.001). Among patients with normal baseline TSH, those who had abnormal TSH after ICI initiation had improved OS (median 41 vs 22 months; P < 0.001). In a multivariate Cox model, abnormal baseline TSH was associated with worse OS (HR 1.62; 95% CI, 1.30-2.02; P < 0.001), while initiation of levothyroxine after ICI initiation was associated with improved OS (HR 0.62; 95% CI, 0.44-0.88; P = 0.008). CONCLUSIONS: ICI-induced thyroid dysfunction is associated with improved survival, although abnormal TSH prior to ICI initiation is associated with inferior survival. PRECIS: Thyroid abnormalities occur commonly in the general population and as immunotherapy toxicities. We found that immunotherapy-induced thyroid dysfunction is associated with better survival, but pre-existing thyroid abnormalities convey worse outcomes.

Longitudinal Experience With and Impressions of COVID-19-Related Clinical Research Changes.

PURPOSE: The COVID-19 pandemic has led to profound changes in clinical research, including remote consent, telehealth, off-site procedures, shipment of therapy, and remote study monitoring. We assessed longitudinal perceptions of these adjustments among clinical research professionals. METHODS: We distributed an anonymous survey assessing experiences, perceptions, and recommendations regarding COVID-19-related clinical research adjustments to cancer clinical research office personnel in May 2020 and again in November 2020. Responses were compared using Fisher's exact and Mann-Whitney U tests. RESULTS: A total of 90 of 102 invited research personnel (88%) responded. Fifty-three (59%) reported participating in both initial and follow-up surveys. The proportion of respondents reporting personal experience with COVID-19-related adjustments increased over time, particularly for remote initial consent (29% v 4%), remote reconsent (24% v 9%), and remote study monitoring (36% v 22%). Perceived impact of COVID-19-related adjustments on data quality (P = .02) and patient experience (P = .002) improved significantly. However, perceived effect on patient safety (P = .02) and respondent's experience (P = .09) became less favorable. Individuals with personal experience with the adjustment were more likely to recommend continuing remote consent (62% v 38%; P = .04), remote monitoring (69% v 45%; P = .05), and therapy shipment (67% v 35%; P = .01) after the COVID-19 pandemic, with nonsignificant trends for off-site diagnostics (44% v 24%; P = .13) and telehealth visits (66% v 45%; P = .08). CONCLUSION: More than 6 months into the global pandemic, perceptions of COVID-19-related clinical research changes remain favorable. Experienced individuals are more likely to recommend that these changes continue in the future.

tcplfit2: an R-language general purpose concentration-response modeling package.

SUMMARY: Many applications of chemical screening are performed in concentration or dose-response mode, and it is necessary to extract appropriate parameters, including whether the chemical/assay pair is active and if so, what are concentrations where activity is seen. Typically, multiple mathematical models or curve shapes are tested against the data to assess the best fit. There are several commercial programs used for this purpose as well as open-source libraries. A widely used system for managing high-throughput screening (HTS) concentration-response data is tcpl (ToxCast Pipeline). The current implementation of tcpl has the concentration-response modeling code tightly integrated with the data management and databasing aspects of HTS data processing. Tcplfit2 is a stand-alone version of the curve-fitting and hitcalling core of tcpl that has been extended to include a large number of standard curve classes and to use benchmark dose modeling. This package will be useful for HTS concentration-response data such as high-throughput whole genome transcriptomics. AVAILABILITY AND IMPLEMENTATION: tcplfit2 is written in R and is available from CRAN.

Association of Healthcare Access With Intensive Care Unit Utilization and Mortality in Patients of Hispanic Ethnicity Hospitalized With COVID-19.

BACKGROUND: Racial and ethnic minority groups in the United States experience a disproportionate burden of COVID-19 deaths. OBJECTIVE: To evaluate whether outcome differences between Hispanic and non-Hispanic COVID-19 hospitalized patients exist and, if so, to identify the main malleable contributing factors. DESIGN, SETTING, PARTICIPANTS: Retrospective, cross-sectional, observational study of 6097 adult COVID-19 patients hospitalized within a single large healthcare system from March to November 2020. EXPOSURES: Self-reported ethnicity and primary language. MAIN OUTCOMES AND MEASURES: Clinical outcomes included intensive care unit (ICU) utilization and in-hospital death. We used age-adjusted odds ratios (OR) and multivariable analysis to evaluate the associations between ethnicity/language groups and outcomes. RESULTS: 32.1% of patients were Hispanic, 38.6% of whom reported a non-English primary language. Hispanic patients were less likely to be insured, have a primary care provider, and have accessed the healthcare system prior to the COVID-19 admission. After adjusting for age, Hispanic inpatients experienced higher ICU utilization (non-English-speaking: OR, 1.75; 95% CI, 1.47-2.08; English-speaking: OR, 1.13; 95% CI, 0.95-1.33) and higher mortality (non-English-speaking: OR, 1.43; 95% CI, 1.10-1.86; English-speaking: OR, 1.53; 95% CI, 1.19-1.98) compared to non-Hispanic inpatients. There were no observed treatment disparities among ethnic groups. After adjusting for age, Hispanic inpatients had elevated disease severity at admission (non-English-speaking: OR, 2.27; 95% CI, 1.89-2.72; English-speaking: OR, 1.33; 95% CI, 1.10- 1.61). In multivariable analysis, the associations between ethnicity/language and clinical outcomes decreased after considering baseline disease severity (P < .001). CONCLUSION: The associations between ethnicity and clinical outcomes can be explained by elevated disease severity at admission and limited access to healthcare for Hispanic patients, especially non-English-speaking Hispanics.

Association between body mass index, dosing strategy, and efficacy of immune checkpoint inhibitors.

BACKGROUND: Increased body mass index (BMI) has been associated with improved response to immune checkpoint inhibitors (ICIs) in multiple cancer types. We evaluated associations between BMI, ICI dosing strategy, and clinical outcomes. METHODS: We abstracted clinical data on patients with cancer treated with ICI, including age, sex, cancer type, BMI, ICI type, dosing strategy (weight-based or fixed), radiographic response, overall survival (OS), and progression-free survival (PFS). We compared clinical outcomes between low-BMI and high-BMI populations using Kaplan-Meier curves, Cox regressions, and Pearson product-moment correlation coefficients. RESULTS: A total of 297 patients were enrolled, of whom 40% were women and 59% were overweight (BMI≥25). Of these, 204 (69%) received fixed and 93 (31%) received weight-based ICI dosing. In the overall cohort, overweight BMI was associated with improved PFS (HR 0.69; 95% CI 0.51 to 0.94; p=0.02) and had a trend toward improved OS (HR 0.77; 95% CI 0.57 to 1.04; p=0.08). For both endpoints, improved outcomes in the overweight population were limited to patients who received weight-based ICI dosing (PFS HR 0.53; p=0.04 for weight-based; vs HR 0.79; p=0.2 for fixed dosing) (OS HR 0.56; p=0.03 for weight-based; vs HR 0.89; p=0.54 for fixed dosing). In multivariable analysis, BMI was not associated with PFS or OS. However, the interaction of BMI≥25 and weight-based dosing had a trend toward association with PFS (HR 0.53; 95% CI 0.26 to 1.10; p=0.09) and was associated with OS (HR 0.50; 95% CI 0.25 to 0.99; p=0.05). Patients with BMI<25 tended to have better outcomes with fixed-dose compared with weight-based ICI, while patients with BMI≥25 tended to have better outcomes with weight-based ICI, although these differences did not achieve statistical significance. There was no association between radiographic response and BMI with fixed-dose ICI (p=0.97), but a near-significant trend with weight-based ICI (p=0.1). In subset analyses, the association between BMI, ICI dosing strategy, and clinical outcomes appeared limited to men. CONCLUSIONS: The clinical benefit of ICI in high-BMI populations appears limited to individuals receiving weight-based ICI dosing. Further research into optimal ICI dosing strategies may be warranted.

High-Throughput Transcriptomics Platform for Screening Environmental Chemicals.

New approach methodologies (NAMs) that efficiently provide information about chemical hazard without using whole animals are needed to accelerate the pace of chemical risk assessments. Technological advancements in gene expression assays have made in vitro high-throughput transcriptomics (HTTr) a feasible option for NAMs-based hazard characterization of environmental chemicals. In this study, we evaluated the Templated Oligo with Sequencing Readout (TempO-Seq) assay for HTTr concentration-response screening of a small set of chemicals in the human-derived MCF7 cell model. Our experimental design included a variety of reference samples and reference chemical treatments in order to objectively evaluate TempO-Seq assay performance. To facilitate analysis of these data, we developed a robust and scalable bioinformatics pipeline using open-source tools. We also developed a novel gene expression signature-based concentration-response modeling approach and compared the results to a previously implemented workflow for concentration-response analysis of transcriptomics data using BMDExpress. Analysis of reference samples and reference chemical treatments demonstrated highly reproducible differential gene expression signatures. In addition, we found that aggregating signals from individual genes into gene signatures prior to concentration-response modeling yielded in vitro transcriptional biological pathway altering concentrations (BPACs) that were closely aligned with previous ToxCast high-throughput screening assays. Often these identified signatures were associated with the known molecular target of the chemicals in our test set as the most sensitive components of the overall transcriptional response. This work has resulted in a novel and scalable in vitro HTTr workflow that is suitable for high-throughput hazard evaluation of environmental chemicals.

Spatial molecular profiling: platforms, applications and analysis tools.

Molecular profiling technologies, such as genome sequencing and proteomics, have transformed biomedical research, but most such technologies require tissue dissociation, which leads to loss of tissue morphology and spatial information. Recent developments in spatial molecular profiling technologies have enabled the comprehensive molecular characterization of cells while keeping their spatial and morphological contexts intact. Molecular profiling data generate deep characterizations of the genetic, transcriptional and proteomic events of cells, while tissue images capture the spatial locations, organizations and interactions of the cells together with their morphology features. These data, together with cell and tissue imaging data, provide unprecedented opportunities to study tissue heterogeneity and cell spatial organization. This review aims to provide an overview of these recent developments in spatial molecular profiling technologies and the corresponding computational methods developed for analyzing such data.

Experience, Perceptions, and Recommendations Concerning COVID-19-Related Clinical Research Adjustments.

BACKGROUND: During the COVID-19 public health emergency, the FDA and NIH altered clinical trial requirements to protect participants and manage study conduct. Given their detailed knowledge of research protocols and regular contact with patients, clinicians, and sponsors, clinical research professionals offer important perspectives on these changes. METHODS: We developed and distributed an anonymous survey assessing COVID-19-related clinical trial adjustment experiences, perceptions, and recommendations to Clinical Research Office personnel at the Harold C. Simmons Comprehensive Cancer Center. Responses were compared using the Fisher exact test. RESULTS: A total of 94 of 109 contacted research personnel (87%) responded. Among these individuals, 58% had >5 years' professional experience in clinical research, and 56% had personal experience with a COVID-19-related change. Respondents perceived that these changes had a positive impact on patient safety; treatment efficacy; patient and staff experience; and communication with patients, investigators, and sponsors. More than 90% felt that positive changes should be continued after COVID-19. For remote consent, telehealth, therapy shipment, off-site diagnostics, and remote monitoring, individuals with personal experience with the specific change and individuals with >5 years' professional experience were numerically more likely to recommend continuing the adjustment, and these differences were significant for telehealth (P=.04) and therapy shipment (P=.02). CONCLUSIONS: Clinical research professionals perceive that COVID-19-related clinical trial adjustments positively impact multiple aspects of study conduct. Those with greatest experience-both specific to COVID-19-related changes and more generally-are more likely to recommend that these adjustments continue in the future.

Ensemble QSAR Modeling to Predict Multispecies Fish Toxicity Lethal Concentrations and Points of Departure.

QSAR modeling can be used to aid testing prioritization of the thousands of chemical substances for which no ecological toxicity data are available. We drew on the U.S. Environmental Protection Agency's ECOTOX database with additional data from ECHA to build a large data set containing in vivo test data on fish for thousands of chemical substances. This was used to create QSAR models to predict two types of end points: acute LC50 (median lethal concentration) and points of departure similar to the NOEC (no observed effect concentration) for any duration (named the "LC50" and "NOEC" models, respectively). These models used study covariates, such as species and exposure route, as features to facilitate the simultaneous use of varied data types. A novel method of substituting taxonomy groups for species dummy variables was introduced to maximize generalizability to different species. A stacked ensemble of three machine learning methods-random forest, gradient boosted trees, and support vector regression-was implemented to best make use of a large data set with many descriptors. The LC50 and NOEC models predicted end points within 1 order of magnitude 81% and 76% of the time, respectively, and had RMSEs of roughly 0.83 and 0.98 log10(mg/L), respectively. Benchmarks against the existing TEST and ECOSAR tools suggest improved prediction accuracy.

Ensemble dynamics and the emergence of correlations in one- and two-dimensional wave turbulence.

We investigate statistical properties of wave turbulence by monitoring the dynamics of ensembles of trajectories. The system under investigation is a simplified model for surface gravity waves in one and two dimensions with a square-root dispersion and a four-wave interaction term. The simulations of decaying turbulence confirm the Kolmogorov-Zakharov spectral power distribution of wave turbulence theory. Fourth-order correlations are computed numerically as ensemble averages of trajectories. The shape, scaling, and time evolution of the correlations agree with the predictions of wave turbulence theory.

Transition of weak wave turbulence to wave turbulence with intermittent collapses.

We study the dynamics of one-dimensional nonlinear waves with a square-root dispersion. This dispersion allows strong interactions of distant modes in wave-number space, and it leads to a modulational instability of a carrier wave interacting with distant sidebands. Weak wave turbulence is found when the system is damped and weakly driven. A driving force that exceeds a critical strength leads to wave collapses coexisting with weak wave turbulence. We explain this transition behavior with the modulational instability of waves with the highest power: Below the threshold the instability is suppressed by the external long-wave damping force. Above the threshold the instability initiates wave collapses.