Phase I study of taminadenant (PBF509/NIR178), an adenosine 2A receptor antagonist, with or without spartalizumab, in patients with advanced non-small cell lung cancer

PURPOSE

The adenosine 2A receptor (A2AR) mediates the immunosuppressive effects of adenosine in the tumor microenvironment and is highly expressed in non-small cell lung cancer (NSCLC). Taminadenant (PBF509/NIR178) is an A2AR antagonist able to reactivate the antitumor immune response.

EXPERIMENTAL DESIGN

In this phase I/Ib, dose-escalation/expansion study, patients with advanced/metastatic NSCLC and {greater than or equal to}1 prior therapy received taminadenant (80-640 mg; orally; twice-daily) with or without spartalizumab (anti-programmed cell death-1; 400 mg; intravenously; every four weeks). Primary endpoints: safety, tolerability, and feasibility of the combination.

RESULTS

During dose escalation, 25 patients each received taminadenant alone or with spartalizumab; 19 (76.0%) and 9 (36.0%) had prior immunotherapy, respectively. Dose-limiting toxicities (all Grade 3) with taminadenant alone were alanine/aspartate aminotransferase increase and nausea (n=1 [4.0%] each; 640 mg) and in the combination group were pneumonitis (n=2 [8.0%]; 160 and 240 mg), fatigue and alanine/aspartate aminotransferase increase (n=1 [4.0%] each; 320 mg); pneumonitis cases responded to steroids rapidly and successfully. Complete and partial responses were observed in one patient each in the single-agent and combination groups; all immunotherapy-naive. In the single-agent and combination groups, seven and 14 patients experienced stable disease; seven and six patients were immunotherapy-pretreated, respectively.

CONCLUSIONS

Taminadenant, with and without spartalizumab, was well tolerated in patients with advanced NSCLC. The maximum tolerated dose of taminadenant alone was 480 mg twice-daily, and 240 mg twice-daily plus spartalizumab. Efficacy was neither a primary or secondary endpoint; however, some clinical benefit was noted regardless of prior immunotherapy or programmed cell death ligand-1 status.

Trastuzumab deruxtecan in patients with HER2-overexpressing locally advanced, unresectable, or metastatic colorectal cancer (mCRC): A randomized, multicenter, phase 2 study (DESTINY-CRC02)

TPS224

Background: Trastuzumab deruxtecan (T-DXd) is an antibody-drug conjugate comprising an anti-HER2 antibody (trastuzumab) linked to a potent topoisomerase I inhibitor (DXd). T-DXd has been approved to treat HER2-positive metastatic breast cancer (United States [US], Japan, Europe, Israel) and advanced gastric cancer (US, Japan, Israel). It is currently being evaluated in other solid tumor types including colorectal cancer. The phase 2 DESTINY-CRC01 study included patients with RAS wild-type mCRC, with a median 4 (range, 2-11) prior lines of therapy. Preliminary results in patients with HER2-overexpressing (IHC 3+ or IHC 2+/ISH+) mCRC showed T-DXd treatment (6.4-mg/kg intravenously [IV] every 3 weeks [Q3W]) resulted in a confirmed objective response rate (ORR) of 45.3% (24/53; 95% CI, 31.6-59.6%) and a median progression-free survival (PFS) of 6.9 months (95% CI, 4.1-not estimable; Siena J Clin Oncol. 2020). Activity was also seen in patients treated with prior anti-HER2 therapy. Although 5.4-mg/kg and 6.4-mg/kg doses of T-DXd have shown clinical efficacy in multiple cancer indications, the lower dose has not yet been tested in patients with HER2-overexpressing mCRC. Preliminary data also suggest T-DXd may be active in RAS mutant mCRC, unlike other anti-HER2 therapies. The DESTINY-CRC02 study aims to determine efficacy and safety of T-DXd in patients with HER2-overexpressing, RAS wild-type or mutant mCRC at 5.4-mg/kg and 6.4-mg/kg doses. Methods: DESTINY-CRC02 (NCT04744831) is a multicenter, randomized, double-blind, 2-arm, parallel phase 2 study that will be conducted in 2 stages. Eligible patients (≥18 years; ≥20 years in Japan, Taiwan, and Korea) will have HER2-overexpressing (IHC 3+ or IHC 2+/ISH+) locally advanced, unresectable or metastatic CRC and have previously received chemotherapy, anti-EGFR therapy, anti-VEGF treatment, and/or anti–PD-1/PD-L1 therapy, as clinically indicated. Prior anti-HER2 therapy will be allowed. In stage 1, patients will be randomly assigned 1:1 to receive T-DXd IV Q3W at a dose of 5.4-mg/kg (n = 40; arm 1) or 6.4-mg/kg (n = 40; arm 2). Randomization will be stratified by ECOG PS (0 or 1), HER2 status (IHC 3+ or IHC 2+/ISH+), and RAS status (wild-type or mutant). After stage 1 enrollment is complete, eligible patients in stage 2 (n = 40) will receive T-DXd 5.4 mg/kg until disease progression or other treatment discontinuation criteria are met. The study is actively enrolling and aims to enroll 120 patients across 60 sites. The primary objective is to assess efficacy of T-DXd at the 5.4-mg/kg and 6.4-mg/kg doses, with a primary endpoint of confirmed ORR by blinded independent central review. Secondary endpoints include investigator-assessed ORR, PFS, duration of response, disease control rate, clinical benefit rate, overall survival, pharmacokinetics, and safety. Clinical trial information: NCT04744831.

Trastuzumab deruxtecan in patients with HER2-overexpressing locally advanced, unresectable, or metastatic colorectal cancer (mCRC): A randomized, multicenter, phase 2 study (DESTINY-CRC02)

TPS3620

Background: Trastuzumab deruxtecan (T-DXd) is an antibody–drug conjugate consisting of an anti-HER2 antibody (trastuzumab) linked to a potent topoisomerase I inhibitor (DXd). T-DXd has been approved to treat HER2-positive metastatic breast cancer (United States, Japan, Europe) and advanced gastric cancer (United States, Japan). It is currently being evaluated in other solid tumor types including colorectal cancer. The phase 2 DESTINY-CRC01 study included patients with RAS wild-type mCRC, with median 4 (range, 2-11) prior lines of therapy. Preliminary results in patients with HER2-overexpressing (IHC 3+ or IHC 2+/ISH+) mCRC showed T-DXd treatment (6.4 mg/kg intravenously [IV] every 3 weeks [Q3W]) resulted in a confirmed objective response rate (ORR) of 45.3% (24/53; 95% CI, 31.6%-59.6%) and a median progression-free survival (PFS) of 6.9 months (95% CI, 4.1 months-not evaluable; Siena J Clin Oncol. 2020;38[15]:4000). Activity was also seen in patients treated with prior anti-HER2 therapy. Although 5.4-mg/kg and 6.4-mg/kg doses of T-DXd have shown clinical efficacy in multiple cancer indications, the lower dose has not yet been tested in patients with HER2-overexpressing mCRC. Preliminary data also suggest T-DXd may be active in RAS mutant mCRC, unlike other anti-HER2 therapies. The DESTINY-CRC02 study aims to determine efficacy and safety of T-DXd in patients with HER2-overexpressing, RAS wild-type or mutant mCRC at 5.4-mg/kg and 6.4-mg/kg doses. Methods: DESTINY-CRC02 (NCT04744831) is a multicenter, randomized, double-blind, 2-arm, parallel phase 2 study that will be conducted in 2 stages. Eligible patients (≥18 years; ≥20 years in Japan, Taiwan, and Korea) will have HER2-overexpressing (IHC 3+ or IHC 2+/ISH+) locally advanced, unresectable or metastatic CRC and have previously received chemotherapy, anti-EGFR therapy, anti-VEGF treatment, and/or anti–PD-1/PD-L1 therapy, as clinically indicated. Prior anti-HER2 therapy will be allowed. In stage 1, patients will be randomly assigned 1:1 to receive T-DXd IV Q3W at a dose of 5.4 mg/kg (n = 40; arm 1) or 6.4 mg/kg (n = 40; arm 2). Randomization will be stratified by ECOG PS (0 or 1), HER2 status (IHC 3+ or IHC 2+/ISH+), and RAS status (wild-type or mutant). After stage 1 enrollment is complete, eligible patients in stage 2 (n = 40) will receive T-DXd 5.4 mg/kg until disease progression or other treatment discontinuation criteria are met. The study is actively enrolling and aims to enroll 120 patients across 60 sites. The primary objective is to assess efficacy of T-DXd at the 5.4-mg/kg and 6.4-mg/kg doses, with a primary end point of confirmed ORR by blinded independent central review. Secondary end points include investigator-assessed ORR, PFS, duration of response, disease control rate, clinical benefit rate, overall survival, pharmacokinetics, and safety. Clinical trial information: NCT04744831.

A phase II study of ribociclib in men with unresectable, incurable teratoma with recent progression

517

Background: Most patients with teratoma are managed by surgery and no standard medical therapy exists for progressive and/or unresectable teratoma. Teratomas have functional expression of retinoblastoma protein and clinical activity was observed with CDK4/6 inhibition (Vaughn DJ, et al. Cancer. 2015). Here, we report results with the CDK4/6 inhibitor ribociclib (RIBO) for unresectable teratoma. Methods: This multicenter, double-blind study enrolled patients (pts) ≥ 15 years old with unresectable, progressive teratoma without malignant transformation, ECOG PS 0-1, and ≥ 1 line of prior chemotherapy. Pts were randomized (2:1) to receive RIBO (600 mg/day, 3 weeks on/1 week off) or placebo (PBO). Crossover to RIBO was permitted following progressive disease (PD) on PBO. The primary endpoint was progression-free survival (PFS). Secondary endpoints included additional efficacy measures, safety, and tolerability. Results: The trial was closed prematurely in the setting of slow accrual. Ten pts were randomized (8 to RIBO, 2 to PBO). Median age was 33 years (range, 21-53). All pts received study treatment, and both pts in the PBO arm crossed over to RIBO following PD. Median exposure was 385 days for RIBO and 166 days for PBO. The PFS rates at 24 months were 71% and 0% in the RIBO and PBO arms, respectively. All 8 pts in the RIBO arm had stable disease (SD) as best response at first evaluation. In the PBO arm, 1 pt had best response of PD and the other SD with durations of treatment of 78 and 254 days, respectively. After crossover, both pts received RIBO, with a best response of SD, and durations of treatment of 943 and 133 days, respectively; the former entered a rollover protocol and the latter discontinued treatment due to an adverse event. The most common reason for discontinuation in both groups was PD. Grade ≥ 3 adverse events in the RIBO group included neutropenia and non-cardiac chest pain in 2 pts, and headache, decreased appetite, asthenia, fatigue, vomiting, and increased blood creatinine in 1 pt each; all but non-cardiac chest pain were suspected to be drug related. Conclusions: In this rare clinical setting, with a limited small sample size, RIBO prolonged PFS as compared to PBO and no new safety signals emerged. Clinical trial information: NCT02300987.

Multiscale measurement error models for aggregated small area health data

Spatial data are often aggregated from a finer (smaller) to a coarser (larger) geographical level. The process of data aggregation induces a scaling effect which smoothes the variation in the data. To address the scaling problem, multiscale models that link the convolution models at different scale levels via the shared random effect have been proposed. One of the main goals in aggregated health data is to investigate the relationship between predictors and an outcome at different geographical levels. In this paper, we extend multiscale models to examine whether a predictor effect at a finer level hold true at a coarser level. To adjust for predictor uncertainty due to aggregation, we applied measurement error models in the framework of multiscale approach. To assess the benefit of using multiscale measurement error models, we compare the performance of multiscale models with and without measurement error in both real and simulated data. We found that ignoring the measurement error in multiscale models underestimates the regression coefficient, while it overestimates the variance of the spatially structured random effect. On the other hand, accounting for the measurement error in multiscale models provides a better model fit and unbiased parameter estimates.

Zero-inflated multiscale models for aggregated small area health data

It is our primary focus to study the spatial distribution of disease incidence at different geographical levels. Often, spatial data are available in the form of aggregation at multiple scale levels such as census tract, county, state, and so on. When data are aggregated from a fine (e.g. county) to a coarse (e.g. state) geographical level, there will be loss of information. The problem is more challenging when excessive zeros are available at the fine level. After data aggregation, the excessive zeros at the fine level will be reduced at the coarse level. If we ignore the zero inflation and the aggregation effect, we could get inconsistent risk estimates at the fine and coarse levels. Hence, in this paper, we address those problems using zero inflated multiscale models that jointly describe the risk variations at different geographical levels. For the excessive zeros at the fine level, we use a zero inflated convolution model, whereas we consider a regular convolution model for the smoothed data at the coarse level. These methods provide a consistent risk estimate at the fine and coarse levels when high percentages of structural zeros are present in the data.

Spatiotemporal multivariate mixture models for Bayesian model selection in disease mapping

It is often the case that researchers wish to simultaneously explore the behavior of and estimate overall risk for multiple, related diseases with varying rarity while accounting for potential spatial and/or temporal correlation. In this paper, we propose a flexible class of multivariate spatio-temporal mixture models to fill this role. Further, these models offer flexibility with the potential for model selection as well as the ability to accommodate lifestyle, socio-economic, and physical environmental variables with spatial, temporal, or both structures. Here, we explore the capability of this approach via a large scale simulation study and examine a motivating data example involving three cancers in South Carolina. The results which are focused on four model variants suggest that all models possess the ability to recover simulation ground truth and display improved model fit over two baseline Knorr-Held spatio-temporal interaction model variants in a real data application.

Comparing multilevel and multiscale convolution models for small area aggregated health data

In spatial epidemiology, data are often arrayed hierarchically. The classification of individuals into smaller units, which in turn are grouped into larger units, can induce contextual effects. On the other hand, a scaling effect can occur due to the aggregation of data from smaller units into larger units. In this paper, we propose a shared multilevel model to address the contextual effects. In addition, we consider a shared multiscale model to adjust for both scale and contextual effects simultaneously. We also study convolution and independent multiscale models, which are special cases of shared multilevel and shared multiscale models, respectively. We compare the performance of the models by applying them to real and simulated data sets. We found that the shared multiscale model was the best model across a range of simulated and real scenarios as measured by the deviance information criterion (DIC) and the Watanabe Akaike information criterion (WAIC).

Spatial small area smoothing models for handling survey data with nonresponse

Spatial smoothing models play an important role in the field of small area estimation. In the context of complex survey designs, the use of design weights is indispensable in the estimation process. Recently, efforts have been made in these spatial smoothing models, in order to obtain reliable estimates of the spatial trend. However, the concept of missing data remains a prevalent problem in the context of spatial trend estimation as estimates are potentially subject to bias. In this paper, we focus on spatial health surveys where the available information consists of a binary response and its associated design weight. Furthermore, we investigate the impact of nonresponse as missing data on a range of spatial models for different missingness mechanisms and different degrees of missingness by means of an extensive simulation study. The computations were performed in R, using INLA and other existing packages. The results show that weight adjustment to correct for missingness has a beneficial effect on the bias in the missing at random setting for all models. Furthermore, we estimate the geographical distribution of perceived health at the district level based on the Belgian Health Interview Survey (2001). Copyright © 2017 John Wiley & Sons, Ltd.

Space-time variation of respiratory cancers in South Carolina: a flexible multivariate mixture modeling approach to risk estimation

PURPOSE

Many types of cancer have an underlying spatiotemporal distribution. Spatiotemporal mixture modeling can offer a flexible approach to risk estimation via the inclusion of latent variables.

METHODS

In this article, we examine the application and benefits of using four different spatiotemporal mixture modeling methods in the modeling of cancer of the lung and bronchus as well as "other" respiratory cancer incidences in the state of South Carolina.

RESULTS

Of the methods tested, no single method outperforms the other methods; which method is best depends on the cancer under consideration. The lung and bronchus cancer incidence outcome is best described by the univariate modeling formulation, whereas the "other" respiratory cancer incidence outcome is best described by the multivariate modeling formulation.

CONCLUSIONS

Spatiotemporal multivariate mixture methods can aid in the modeling of cancers with small and sparse incidences when including information from a related, more common type of cancer.

Spatially-dependent Bayesian model selection for disease mapping

In disease mapping where predictor effects are to be modeled, it is often the case that sets of predictors are fixed, and the aim is to choose between fixed model sets. Model selection methods, both Bayesian model selection and Bayesian model averaging, are approaches within the Bayesian paradigm for achieving this aim. In the spatial context, model selection could have a spatial component in the sense that some models may be more appropriate for certain areas of a study region than others. In this work, we examine the use of spatially referenced Bayesian model averaging and Bayesian model selection via a large-scale simulation study accompanied by a small-scale case study. Our results suggest that BMS performs well when a strong regression signature is found.

Spatio-temporal Bayesian model selection for disease mapping

Spatio-temporal analysis of small area health data often involves choosing a fixed set of predictors prior to the final model fit. In this paper, we propose a spatio-temporal approach of Bayesian model selection to implement model selection for certain areas of the study region as well as certain years in the study time line. Here, we examine the usefulness of this approach by way of a large-scale simulation study accompanied by a case study. Our results suggest that a special case of the model selection methods, a mixture model allowing a weight parameter to indicate if the appropriate linear predictor is spatial, spatio-temporal, or a mixture of the two, offers the best option to fitting these spatio-temporal models. In addition, the case study illustrates the effectiveness of this mixture model within the model selection setting by easily accommodating lifestyle, socio-economic, and physical environmental variables to select a predominantly spatio-temporal linear predictor.

Disease mapping of zero-excessive mesothelioma data in Flanders

PURPOSE

To investigate the distribution of mesothelioma in Flanders using Bayesian disease mapping models that account for both an excess of zeros and overdispersion.

METHODS

The numbers of newly diagnosed mesothelioma cases within all Flemish municipalities between 1999 and 2008 were obtained from the Belgian Cancer Registry. To deal with overdispersion, zero inflation, and geographical association, the hurdle combined model was proposed, which has three components: a Bernoulli zero-inflation mixture component to account for excess zeros, a gamma random effect to adjust for overdispersion, and a normal conditional autoregressive random effect to attribute spatial association. This model was compared with other existing methods in literature.

RESULTS

The results indicate that hurdle models with a random effects term accounting for extra variance in the Bernoulli zero-inflation component fit the data better than hurdle models that do not take overdispersion in the occurrence of zeros into account. Furthermore, traditional models that do not take into account excessive zeros but contain at least one random effects term that models extra variance in the counts have better fits compared to their hurdle counterparts. In other words, the extra variability, due to an excess of zeros, can be accommodated by spatially structured and/or unstructured random effects in a Poisson model such that the hurdle mixture model is not necessary.

CONCLUSIONS

Models taking into account zero inflation do not always provide better fits to data with excessive zeros than less complex models. In this study, a simple conditional autoregressive model identified a cluster in mesothelioma cases near a former asbestos processing plant (Kapelle-op-den-Bos). This observation is likely linked with historical local asbestos exposures. Future research will clarify this.

Spatial mixture multiscale modeling for aggregated health data

One of the main goals in spatial epidemiology is to study the geographical pattern of disease risks. For such purpose, the convolution model composed of correlated and uncorrelated components is often used. However, one of the two components could be predominant in some regions. To investigate the predominance of the correlated or uncorrelated component for multiple scale data, we propose four different spatial mixture multiscale models by mixing spatially varying probability weights of correlated (CH) and uncorrelated heterogeneities (UH). The first model assumes that there is no linkage between the different scales and, hence, we consider independent mixture convolution models at each scale. The second model introduces linkage between finer and coarser scales via a shared uncorrelated component of the mixture convolution model. The third model is similar to the second model but the linkage between the scales is introduced through the correlated component. Finally, the fourth model accommodates for a scale effect by sharing both CH and UH simultaneously. We applied these models to real and simulated data, and found that the fourth model is the best model followed by the second model.

Bayesian model selection methods in modeling small area colon cancer incidence

PURPOSE

Many types of cancer have an underlying spatial incidence distribution. Spatial model selection methods can be useful when determining the linear predictor that best describes incidence outcomes.

METHODS

In this article, we examine the applications and benefits of using two different types of spatial model selection techniques, Bayesian model selection and Bayesian model averaging, in relation to colon cancer incidence in the state of Georgia, United States.

RESULTS

Both methods produce useful results that lead to the determination that median household income and percent African American population are important predictors of colon cancer incidence in the Northern counties of the state, whereas percent persons below poverty level and percent African American population are important in the Southern counties.

CONCLUSIONS

Of the two presented methods, Bayesian model selection appears to provide more succinct results, but applying the two in combination offers even more useful information into the spatial preferences of the alternative linear predictors.

Impact of Income on Small Area Low Birth Weight Incidence Using Multiscale Models

Low birth weight (LBW) is an important public health issue in the US as well as worldwide. The two main causes of LBW are premature birth and fetal growth restriction. Socio-economic status, as measured by family income has been correlated with LBW incidence at both the individual and population levels. In this paper, we investigate the impact of household income on LBW incidence at different geographical levels. To show this, we choose to examine LBW incidences collected from the state of Georgia, in the US, at both the county and public health (PH) district. The data at the PH district are an aggregation of the data at the county level nested within the PH district. A spatial scaling effect is induced during data aggregation from the county to the PH level. To address the scaling effect issue, we applied a shared multiscale model that jointly models the data at two levels via a shared correlated random effect. To assess the benefit of using the shared multiscale model, we compare it with an independent multiscale model which ignores the scale effect. Applying the shared multiscale model for the Georgia LBW incidence, we have found that income has a negative impact at both the county and PH levels. On the other hand, the independent multiscale model shows that income has a negative impact only at the county level. Hence, if the scale effect is not properly accommodated in the model, a different interpretation of the findings could result.

Bayesian multi-scale modeling for aggregated disease mapping data

In disease mapping, a scale effect due to an aggregation of data from a finer resolution level to a coarser level is a common phenomenon. This article addresses this issue using a hierarchical Bayesian modeling framework. We propose four different multiscale models. The first two models use a shared random effect that the finer level inherits from the coarser level. The third model assumes two independent convolution models at the finer and coarser levels. The fourth model applies a convolution model at the finer level, but the relative risk at the coarser level is obtained by aggregating the estimates at the finer level. We compare the models using the deviance information criterion (DIC) and Watanabe-Akaike information criterion (WAIC) that are applied to real and simulated data. The results indicate that the models with shared random effects outperform the other models on a range of criteria.

Comparing INLA and OpenBUGS for hierarchical Poisson modeling in disease mapping

The recently developed R package INLA (Integrated Nested Laplace Approximation) is becoming a more widely used package for Bayesian inference. The INLA software has been promoted as a fast alternative to MCMC for disease mapping applications. Here, we compare the INLA package to the MCMC approach by way of the BRugs package in R, which calls OpenBUGS. We focus on the Poisson data model commonly used for disease mapping. Ultimately, INLA is a computationally efficient way of implementing Bayesian methods and returns nearly identical estimates for fixed parameters in comparison to OpenBUGS, but falls short in recovering the true estimates for the random effects, their precisions, and model goodness of fit measures under the default settings. We assumed default settings for ground truth parameters, and through altering these default settings in our simulation study, we were able to recover estimates comparable to those produced in OpenBUGS under the same assumptions.

Empirical Bayes estimates for correlated hierarchical data with overdispersion

An extension of the generalized linear mixed model was constructed to simultaneously accommodate overdispersion and hierarchies present in longitudinal or clustered data. This so-called combined model includes conjugate random effects at observation level for overdispersion and normal random effects at subject level to handle correlation, respectively. A variety of data types can be handled in this way, using different members of the exponential family. Both maximum likelihood and Bayesian estimation for covariate effects and variance components were proposed. The focus of this paper is the development of an estimation procedure for the two sets of random effects. These are necessary when making predictions for future responses or their associated probabilities. Such (empirical) Bayes estimates will also be helpful in model diagnosis, both when checking the fit of the model as well as when investigating outlying observations. The proposed procedure is applied to three datasets of different outcome types.

Modelling multivariate, overdispersed binomial data with additive and multiplicative random effects

When modelling multivariate binomial data, it often occurs that it is necessary to take into consideration both clustering and overdispersion, the former arising from the dependence between data, and the latter due to the additional variability in the data not prescribed by the distribution. If interest lies in accommodating both phenomena at the same time, we can use separate sets of random effects that capture the within-cluster association and the extra variability. In particular, the random effects for overdispersion can be included in the model either additively or multiplicatively. For this purpose, we propose a series of Bayesian hierarchical models that deal simultaneously with both phenomena. The proposed models are applied to bivariate repeated prevalence data for hepatitis C virus (HCV) and human immunodeficiency virus (HIV) infection in injecting drug users in Italy from 1998 to 2007.