A Phase I Study of Acapatamab, a Half-life Extended, PSMA-Targeting Bispecific T-cell Engager for Metastatic Castration-Resistant Prostate Cancer.

PURPOSE: Safety and efficacy of acapatamab, a prostate-specific membrane antigen (PSMA) x CD3 bispecific T-cell engager were evaluated in a first-in-human study in metastatic castration-resistant prostate cancer (mCRPC). PATIENTS AND METHODS: Patients with mCRPC refractory to androgen receptor pathway inhibitor therapy and taxane-based chemotherapy received target acapatamab doses ranging from 0.003 to 0.9 mg in dose exploration (seven dose levels) and 0.3 mg (recommended phase II dose) in dose expansion intravenously every 2 weeks. Safety (primary objective), pharmacokinetics, and antitumor activity (secondary objectives) were assessed. RESULTS: In all, 133 patients (dose exploration, n = 77; dose expansion, n = 56) received acapatamab. Cytokine release syndrome (CRS) was the most common treatment-emergent adverse event seen in 97.4% and 98.2% of patients in dose exploration and dose expansion, respectively; grade ≥ 3 was seen in 23.4% and 16.1%, respectively. Most CRS events were seen in treatment cycle 1; incidence and severity decreased at/beyond cycle 2. In dose expansion, confirmed prostate-specific antigen (PSA) responses (PSA50) were seen in 30.4% of patients and radiographic partial responses in 7.4% (Response Evaluation Criteria in Solid Tumors 1.1). Median PSA progression-free survival (PFS) was 3.3 months [95% confidence interval (CI): 3.0-4.9], radiographic PFS per Prostate Cancer Clinical Trials Working Group 3 was 3.7 months (95% CI: 2.0-5.4). Acapatamab induced T-cell activation and increased cytokine production several-fold within 24 hours of initiation. Treatment-emergent antidrug antibodies were detected in 55% and impacted serum exposures in 36% of patients in dose expansion. CONCLUSIONS: Acapatamab was safe and tolerated and had a manageable CRS profile. Preliminary signs of efficacy with limited durable antitumor activity were observed. Acapatamab demonstrated pharmacokinetic and pharmacodynamic activity.

An inverse probability weighted regression method that accounts for right-censoring for causal inference with multiple treatments and a binary outcome.

Comparative effectiveness research often involves evaluating the differences in the risks of an event of interest between two or more treatments using observational data. Often, the post-treatment outcome of interest is whether the event happens within a pre-specified time window, which leads to a binary outcome. One source of bias for estimating the causal treatment effect is the presence of confounders, which are usually controlled using propensity score-based methods. An additional source of bias is right-censoring, which occurs when the information on the outcome of interest is not completely available due to dropout, study termination, or treatment switch before the event of interest. We propose an inverse probability weighted regression-based estimator that can simultaneously handle both confounding and right-censoring, calling the method CIPWR, with the letter C highlighting the censoring component. CIPWR estimates the average treatment effects by averaging the predicted outcomes obtained from a logistic regression model that is fitted using a weighted score function. The CIPWR estimator has a double robustness property such that estimation consistency can be achieved when either the model for the outcome or the models for both treatment and censoring are correctly specified. We establish the asymptotic properties of the CIPWR estimator for conducting inference, and compare its finite sample performance with that of several alternatives through simulation studies. The methods under comparison are applied to a cohort of prostate cancer patients from an insurance claims database for comparing the adverse effects of four candidate drugs for advanced stage prostate cancer.

Cross-Sectional Estimation of Endogenous Biomarker Associations with Prenatal Phenols, Phthalates, Metals, and Polycyclic Aromatic Hydrocarbons in Single-Pollutant and Mixtures Analysis Approaches.

BACKGROUND: Humans are exposed to mixtures of toxicants that can impact several biological pathways. We investigated the associations between multiple classes of toxicants and an extensive panel of biomarkers indicative of lipid metabolism, inflammation, oxidative stress, and angiogenesis. METHODS: We conducted a cross-sectional study of 173 participants (median 26 wk gestation) from the LIFECODES birth cohort. We measured exposure analytes of multiple toxicant classes [metals, phthalates, phenols, and polycyclic aromatic hydrocarbons (PAHs)] in urine samples. We also measured endogenous biomarkers (eicosanoids, cytokines, angiogenic markers, and oxidative stress markers) in either plasma or urine. We estimated pair-wise associations between exposure analytes and endogenous biomarkers using multiple linear regression after adjusting for covariates. We used adaptive elastic net regression, hierarchical Bayesian kernel machine regression, and sparse-group LASSO regression to evaluate toxicant mixtures associated with individual endogenous biomarkers. RESULTS: After false-discovery adjustment (q<0.2), single-pollutant models yielded 19 endogenous biomarker signals associated with phthalates, 13 with phenols, 17 with PAHs, and 18 with trace metals. Notably, adaptive elastic net revealed that phthalate metabolites were selected for several positive signals with the cyclooxygenase (n=7), cytochrome p450 (n=7), and lipoxygenase (n=8) pathways. Conversely, the toxicant classes that exhibited the greatest number of negative signals overall in adaptive elastic net were phenols (n=20) and metals (n=21). DISCUSSION: This study characterizes cross-sectional endogenous biomarker signatures associated with individual and mixtures of prenatal toxicant exposures. These results can help inform the prioritization of specific pairs or clusters of endogenous biomarkers and exposure analytes for investigating health outcomes. https://doi.org/10.1289/EHP7396.

A comparison of parametric propensity score-based methods for causal inference with multiple treatments and a binary outcome.

We consider comparative effectiveness research (CER) from observational data with two or more treatments. In observational studies, the estimation of causal effects is prone to bias due to confounders related to both treatment and outcome. Methods based on propensity scores are routinely used to correct for such confounding biases. A large fraction of propensity score methods in the current literature consider the case of either two treatments or continuous outcome. There has been extensive literature with multiple treatment and binary outcome, but interest often lies in the intersection, for which the literature is still evolving. The contribution of this article is to focus on this intersection and compare across methods, some of which are fairly recent. We describe propensity-based methods when more than two treatments are being compared, and the outcome is binary. We assess the relative performance of these methods through a set of simulation studies. The methods are applied to assess the effect of four common therapies for castration-resistant advanced-stage prostate cancer. The data consist of medical and pharmacy claims from a large national private health insurance network, with the adverse outcome being admission to the emergency room within a short time window of treatment initiation.

Urinary trace metals in association with fetal ultrasound measures during pregnancy.

Toxic metals have been associated with lower birth weight while essential metals have been associated with higher birth weight. Evidence for other metals is either inconsistent or limited in terms of number of studies. This study analyzed 17 urinary metals, individually and as a mixture, and their association with measures of fetal growth in the LIFECODES birth cohort. Ultrasound was used to measure abdominal circumference, head circumference, and femur length and measures were used to calculate estimated fetal weight at ~26 and ~35 weeks. We calculated the z-score based on gestational age at scan, and estimated fetal weight (EFW) was combined with birth weight for longitudinal analyses. Metals were measured in samples collected at ~26 weeks. We used linear mixed effects models to examine associations between metals and repeated measures of each outcome, controlling for covariates. Principal components analysis reduced the biomarkers to predictors that may share some commonality. We found that an interquartile range increase in selenium was inversely associated with femur length z-score as well as other growth outcomes. Other essential metals, however, were associated with an increase in growth. Finally, the PCA component comprised of arsenic, mercury, and tin was associated with decreased head circumference z-score (-0.14 [95% CI: -0.23, -0.05]).

Interaction analysis under misspecification of main effects: Some common mistakes and simple solutions.

The statistical practice of modeling interaction with two linear main effects and a product term is ubiquitous in the statistical and epidemiological literature. Most data modelers are aware that the misspecification of main effects can potentially cause severe type I error inflation in tests for interactions, leading to spurious detection of interactions. However, modeling practice has not changed. In this article, we focus on the specific situation where the main effects in the model are misspecified as linear terms and characterize its impact on common tests for statistical interaction. We then propose some simple alternatives that fix the issue of potential type I error inflation in testing interaction due to main effect misspecification. We show that when using the sandwich variance estimator for a linear regression model with a quantitative outcome and two independent factors, both the Wald and score tests asymptotically maintain the correct type I error rate. However, if the independence assumption does not hold or the outcome is binary, using the sandwich estimator does not fix the problem. We further demonstrate that flexibly modeling the main effect under a generalized additive model can largely reduce or often remove bias in the estimates and maintain the correct type I error rate for both quantitative and binary outcomes regardless of the independence assumption. We show, under the independence assumption and for a continuous outcome, overfitting and flexibly modeling the main effects does not lead to power loss asymptotically relative to a correctly specified main effect model. Our simulation study further demonstrates the empirical fact that using flexible models for the main effects does not result in a significant loss of power for testing interaction in general. Our results provide an improved understanding of the strengths and limitations for tests of interaction in the presence of main effect misspecification. Using data from a large biobank study "The Michigan Genomics Initiative", we present two examples of interaction analysis in support of our results.

Prediction and associations of preterm birth and its subtypes with eicosanoid enzymatic pathways and inflammatory markers.

Endogenous signaling molecules derived from lipids, peptides, and DNA, are important regulators of physiological processes during pregnancy. The effect of their collective impact on preterm birth (delivery < 37 weeks gestation) is understudied. We aimed to characterize the associations and predictive capacity of an extensive panel of eicosanoids, immune biomarkers, oxidative stress markers, and growth factors towards preterm birth and its subtypes. We conducted a cross-sectional study of pregnant women (recruited < 15 weeks gestation) in the LIFECODES birth cohort, which included 58 cases of preterm birth and 115 controls that delivered term. Among the cases there were 31 cases who had a spontaneous preterm birth (cases who had spontaneous preterm labor and/or preterm premature rupture of membranes) and 25 that had preterm birth associated with aberrant placentation (cases who had preeclampsia and/or intrauterine growth restriction) and 2 cases that could not be sufficiently categorized as either. We analyzed single biomarker associations with each preterm birth outcome using multiple logistic regression. Adaptive elastic-net was implemented to perform a penalized multiple logistic regression on all biomarkers simultaneously to identify the most predictive biomarkers. We then organized biomarkers into biological groups and by enzymatic pathways and applied adaptive elastic-net and random forest to evaluate the accuracy of each group for predicting preterm birth cases. The majority of associations we observed were for spontaneous preterm birth, and adaptive elastic-net identified 5-oxoeicosatetraenoic acid, resolvin D1, 5,6-epoxy-eicsatrienoic acid, and 15-deoxy-12,14-prostaglandin J2 as most predictive. Overall, lipid biomarkers performed the best at separating cases from controls compared to other biomarker categories (adaptive elastic-net AUC = 0.78 [0.62, 0.94], random forest AUC = 0.84 [0.72, 0.96]). Among the enzymatic pathways that differentiate eicosanoid metabolites, we observed the highest prediction of overall preterm birth by lipoxygenase metabolites using random forest (AUC = 0.83 [0.69, 0.96]), followed by cytochrome p450 metabolites using adaptive elastic-net (AUC = 0.74 [0.52, 0.96]). In this study we translate biological hypothesis into the language of modern machine learning. Many lipid biomarkers were highly associated with overall and spontaneous preterm birth. Among eicosanoids, lipoxygenase and cytochrome p450 products performed best in identifying overall and spontaneous preterm birth. The combination of lipid biomarkers may have good utility in clinical settings to predict preterm birth.

Urinary concentrations of phenols in association with biomarkers of oxidative stress in pregnancy: Assessment of effects independent of phthalates.

BACKGROUND: Maternal exposure to environmental phenols is common in pregnancy and has been linked to preterm birth, preeclampsia, and reduced fetal growth. One potential mechanism may be through increased maternal oxidative stress. OBJECTIVE: We examined the associations between a panel of 10 urinary phenols, including dichlorophenols, benzophenone-3, parabens, triclosan and triclocarban, and bisphenol-S, and two urinary oxidative stress biomarkers, 8-hydroxydeoxyguanosine (8-OHdG) and 8-isoprostane. All exposure and outcome biomarkers were measured at 4 time points in pregnancy. METHODS: We used repeated measures models to examine the association between repeated exposure and outcome biomarkers. Additionally, we used adaptive elastic net (AENET) to identify non-null associations accounting for the correlation structure of exposures, both for phenols and urinary phthalate metabolites that were previously associated with the oxidative stress biomarkers in our study population. RESULTS: In adjusted repeated measures models, we observed that dichlorophenols, benzophenone-3, triclosan, and some parabens were associated with increases in both oxidative stress biomarkers. The greatest effect estimates were observed for 2,5-dichlorophenol; an interquartile range (IQR) increase in this compound was associated with a 15.2% (95% confidence interval [CI] = 11.0, 19.6) increase in 8-OHdG and a 16.7% (95% CI = 9.66, 24.2) increase in 8-isoprostane. Bisphenol-S detection was associated with a clear increase in 8-isoprostane (18.5%, 95% CI = 7.68, 30.5) but a more modest increase in 8-OHdG (6.18%, 95% CI = -0.27, 13.1). However, AENET models did not consistently select any of the phenols as predictors of 8-OHdG or 8-isoprostane when phthalate metabolites were included in the model. CONCLUSION: Overall, urinary phenols were associated with increases in biomarkers of oxidative stress in pregnancy but either to a lesser extent, or due to correlation with, urinary phthalate metabolites.

Foetal ultrasound measurement imputations based on growth curves versus multiple imputation chained equation (MICE).

BACKGROUND: Ultrasound measures are valuable for epidemiologic studies of risk factors for growth restriction. Longitudinal measurements enable investigation of rates of change and identification of windows where growth is impacted more acutely. However, missing data can be problematic in these studies, limiting sample size, ability to characterise windows of vulnerability, and in some instances creating bias. We sought to compare a parametric linear mixed model (LMM) approach to multiple imputation in this setting with multiple imputation by chained equation (MICE) methodology. METHODS: Ultrasound scans performed for clinical purposes were abstracted from women in the LIFECODES birth cohort (n = 1003) if they were close in time to three study visits (median 18, 26, and 35 weeks' gestation). We created imputed datasets using LMM and MICE and calculated associations between demographic factors and ultrasound parameters cross-sectionally and longitudinally. Results were compared with a complete-case analysis. RESULTS: Most participants had ultrasounds at 18 weeks' gestation, and ~50% had measurements at 26 and 35 weeks; 100% had birthweight. Associations between demographic factors and ultrasound measures were similar in magnitude, but more precise, when either imputed datasets were used, compared with a complete-case analysis, in both the cross-sectional or longitudinal analyses. CONCLUSIONS: MICE, though ignoring the non-linear features of the trajectory and within subject correlation, is able to provide reasonable imputation of foetal growth data when compared to LMM. Because it simultaneously imputes missing covariate data and does not require specification of variance structure as in LMM, MICE may be preferable for imputation in this setting.

Subset-Based Analysis Using Gene-Environment Interactions for Discovery of Genetic Associations across Multiple Studies or Phenotypes.

OBJECTIVES: Classical methods for combining summary data from genome-wide association studies only use marginal genetic effects, and power can be compromised in the presence of heterogeneity. We aim to enhance the discovery of novel associated loci in the presence of heterogeneity of genetic effects in subgroups defined by an environmental factor. METHODS: We present a pvalue-assisted subset testing for associations (pASTA) framework that generalizes the previously proposed association analysis based on subsets (ASSET) method by incorporating gene-environment (G-E) interactions into the testing procedure. We conduct simulation studies and provide two data examples. RESULTS: Simulation studies show that our proposal is more powerful than methods based on marginal associations in the presence of G-E interactions and maintains comparable power even in their absence. Both data examples demonstrate that our method can increase power to detect overall genetic associations and identify novel studies/phenotypes that contribute to the association. CONCLUSIONS: Our proposed method can be a useful screening tool to identify candidate single nucleotide polymorphisms that are potentially associated with the trait(s) of interest for further validation. It also allows researchers to determine the most probable subset of traits that exhibit genetic associations in addition to the enhancement of power.