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We investigate the source detection problem in epidemiology, which is one of the most important issues for control of epidemics. Mathematically, we reformulate the problem as one of identifying the relevant component in a multivariate Gaussian mixture model. Focusing on the study of cholera and diseases with similar modes of transmission, we calibrate the parameters of our mixture model using human mobility networks within a stochastic, spatially explicit epidemiological model for waterborne disease. Furthermore, we adopt a Bayesian perspective, so that prior information on source location can be incorporated (e.g., reflecting the impact of local conditions). Posterior-based inference is performed, which permits estimates in the form of either individual locations or regions. Importantly, our estimator only requires first-arrival times of the epidemic by putative observers, typically located only at a small proportion of nodes. The proposed method is demonstrated within the context of the 2000-2002 cholera outbreak in the KwaZulu-Natal province of South Africa.

BackgroundAnti-programmed cell death ligand 1 (PD-L1)/programmed cell death 1 antibodies have shown clinical activity in platinum-treated metastatic urothelial carcinoma, resulting in regulatory approval of several agents, including avelumab (anti-PD-L1). We report ≥2-year follow-up data for avelumab treatment and exploratory subgroup analyses in patients with urothelial carcinoma.MethodsPatients with previously treated advanced/metastatic urothelial carcinoma, pooled from two cohorts of the phase Ib JAVELIN Solid Tumor trial, received avelumab 10 mg/kg every 2 weeks until disease progression, unacceptable toxicity or withdrawal. End points included best overall response and progression-free survival (PFS) per RECIST V.1.1, overall survival (OS) and safety. Post hoc analyses included objective response rates (ORRs) in subgroups defined by established high-risk/poor-prognosis characteristics and association between time to response and outcome.Results249 patients received avelumab; efficacy was assessed in 242 postplatinum patients. Median follow-up was 31.9 months (range 24–43), and median treatment duration was 2.8 months (range 0.5–42.8). The confirmed ORR was 16.5% (95% CI 12.1% to 21.8%; complete response in 4.1% and partial response in 12.4%). Median duration of response was 20.5 months (95% CI 9.7 months to not estimable). Median PFS was 1.6 months (95% CI 1.4 to 2.7 months) and the 12-month PFS rate was 16.8% (95% CI 11.9% to 22.4%). Median OS was 7.0 months (95% CI 5.9 to 8.5 months) and the 24-month OS rate was 20.1% (95% CI 15.2% to 25.4%). In post hoc exploratory analyses, avelumab showed antitumor activity in high-risk subgroups, including elderly patients and those with renal insufficiency or upper tract disease; ORRs were numerically lower in patients with liver metastases or low albumin levels. Objective response achieved by 3 months versus later was associated with longer OS (median not reached (95% CI 18.9 months to not estimable) vs 7.1 months (95% CI 5.2 to 9.0 months)). Safety findings were consistent with previously reported 6-month analyses.ConclusionsAfter ≥2 years of follow-up, avelumab showed prolonged efficacy and acceptable safety in patients with platinum-treated advanced/metastatic urothelial carcinoma, including high-risk subgroups. Survival appeared longer in patients who responded within 3 months. Long-term safety findings were consistent with earlier reports with avelumab treatment in this patient population.

BackgroundPatients treated with immune checkpoint inhibitors (ICIs) may experience pseudoprogression, which can be classified as progressive disease (PD) by Response Evaluation Criteria in Solid Tumors (RECIST) V.1.1 and could lead to inappropriate treatment discontinuation. Immune-response criteria were developed to better capture novel response patterns seen with ICIs.MethodsWe pooled data from 1765 patients with 12 types of advanced solid tumors treated with avelumab (an anti-programmed death ligand 1 (PD-L1) monoclonal antibody) monotherapy in the JAVELIN Solid Tumor and JAVELIN Merkel 200 trials, conducted a comparative analysis of tumor assessments by investigators according to RECIST 1.1 and immune-related RECIST (irRECIST), and evaluated the correlation between progression-free survival (PFS) and overall survival (OS).ResultsIn total, 147 patients (8.3%) had a best overall response (BOR) of PD by RECIST 1.1 but had immune-related disease control by irRECIST (defined as immune-related BOR (irBOR) of immune-related stable disease or better). This discordance was seen irrespective of PD-L1 status and observed across all tumor types. Overall, PFS and immune-related PFS showed similar imputed rank correlations with OS.ConclusionsThe use of irRECIST identified a subset of patients with a BOR of PD by RECIST 1.1 but an irBOR of immune-related disease control by irRECIST with a distinctive survival curve, thereby providing more clinically relevant information than RECIST 1.1 alone. However, as a surrogate endpoint for OS in the whole population, immune-related PFS by irRECIST did not show improved predictive value compared with PFS by RECIST 1.1.

Background Avelumab first‐line (1 L) maintenance is a standard of care for advanced urothelial carcinoma (aUC) based on the JAVELIN Bladder 100 phase 3 trial, which showed that avelumab 1 L maintenance + best supportive care (BSC) significantly prolonged overall survival (OS) and progression‐free survival (PFS) vs BSC alone in patients who were progression free after receiving 1 L platinum‐containing chemotherapy. Here, we comprehensively screened JAVELIN Bladder 100 trial datasets to identify prognostic factors that define subpopulations of patients with longer or shorter OS irrespective of treatment, and predictive factors that select patients who could obtain a greater OS benefit from avelumab 1 L maintenance treatment. Methods We performed machine learning analyses to screen a large set of baseline covariates, including patient demographics, disease characteristics, laboratory values, molecular biomarkers, and patient‐reported outcomes. Covariates were identified from previously reported analyses and established prognostic and predictive markers. Variables selected from random survival forest models were processed further in univariate Cox models with treatment interaction and visually inspected using correlation analysis and Kaplan–Meier curves. Results were summarized in a multivariable Cox model. Results Prognostic baseline covariates associated with OS included in the final model were assignment to avelumab 1 L maintenance treatment, Eastern Cooperative Oncology Group performance status, site of metastasis, sum of longest target lesion diameters, levels of C‐reactive protein and alkaline phosphatase in blood, lymphocyte proportion in intratumoral stroma, tumor mutational burden, and tumor CD8+ T‐cell infiltration. Potential predictive factors included site of metastasis, tumor mutation burden, and tumor CD8+ T‐cell infiltration. An analysis in patients with PD‐L1+ tumors had similar findings to those in the overall population. Conclusions Machine learning analyses of data from the JAVELIN Bladder 100 trial identified potential prognostic and predictive factors for avelumab 1 L maintenance treatment in patients with aUC, which warrant further evaluation in other clinical datasets.

Avelumab (anti–PD‐L1) is an approved anticancer treatment for several indications. The JAVELIN Gastric 100 phase III trial did not meet its primary objective of demonstrating superior overall survival (OS) with avelumab maintenance versus continued chemotherapy in patients with advanced gastric cancer/gastroesophageal junction cancer; however, the OS rate was numerically higher with avelumab at timepoints after 12 months. Machine learning (random forests, SIDEScreen, and variable‐importance assessments) was used to build models to identify prognostic/predictive factors associated with long‐term OS and tumor growth dynamics (TGDs). Baseline, re‐baseline, and longitudinal variables were evaluated as covariates in a parametric time‐to‐event model for OS and Gompertzian population model for TGD. The final OS model incorporated a treatment effect on the log‐logistic shape parameter but did not identify a treatment effect on OS or TGD. Variables identified as prognostic for longer OS included older age; higher gamma‐glutamyl transferase (GGT) or albumin; absence of peritoneal carcinomatosis; lower neutrophil‐lymphocyte ratio, lactate dehydrogenase, or C‐reactive protein (CRP); response to induction chemotherapy; and Eastern Cooperative Oncology Group performance status of 0. Among baseline and time‐varying covariates, the largest effects were found for GGT and CRP, respectively. Liver metastasis at re‐baseline predicted higher tumor growth. Tumor size after induction chemotherapy was associated with number of metastatic sites and stable disease (vs. response). Asian region did not impact OS or TGD. Overall, an innovative workflow supporting pharmacometric modeling of OS and TGD was established. Consistent with the primary trial analysis, no treatment effect was identified. However, potential prognostic factors were identified.

In 2011, a large outbreak of entero-hemorrhagic E. coli (EHEC) and hemolytic uremic syndrome (HUS) occurred in Germany. The City of Hamburg was the first focus of the epidemic and had the highest incidences among all 16 Federal States of Germany. In this article, we present epidemiological characteristics of the Hamburg notification data. Evaluating the epicurves retrospectively, we found that the first epidemiological signal of the outbreak, which was in form of a HUS case cluster, was received by local health authorities when already 99 EHEC and 48 HUS patients had experienced their first symptoms. However, only two EHEC and seven HUS patients had been notified. Middle-aged women had the highest risk for contracting the infection in Hamburg. Furthermore, we studied timeliness of case notification in the course of the outbreak. To analyze the spatial distribution of EHEC/HUS incidences in 100 districts of Hamburg, we mapped cases' residential addresses using geographic information software. We then conducted an ecological study in order to find a statistical model identifying associations between local socio-economic factors and EHEC/HUS incidences in the epidemic. We employed a Bayesian Poisson model with covariates characterizing the Hamburg districts as well as incorporating structured and unstructured spatial effects. The Deviance Information Criterion was used for stepwise variable selection. We applied different modeling approaches by using primary data, transformed data, and preselected subsets of transformed data in order to identify socio-economic factors characterizing districts where EHEC/HUS outbreak cases had their residence.

BackgroundAdverse events (AEs) of special interest that arise during treatment with immune checkpoint inhibitors, including immune-related AEs (irAEs), have been reported to be associated with improved clinical outcomes. We analyzed patients treated with avelumab from the JAVELIN Solid Tumor and Merkel 200 trials, examining the association between AEs and efficacy while adjusting for confounding factors such as treatment duration and event order.MethodsWe analyzed efficacy and safety data from 1783 patients treated with the programmed death ligand 1 inhibitor avelumab who were enrolled in expansion cohorts of the JAVELIN Solid Tumor and Merkel 200 trials. To analyze the association between irAEs and efficacy with regard to survival, we used a time-dependent Cox model with time-varying indicators for irAEs, as well as multistate models that accounted for competing risks and time inhomogeneity.Results295 patients (16.5%) experienced irAEs and 454 patients (25.5%) experienced infusion-related reactions. There was a reduced risk of death in patients who experienced irAEs compared with those who did not (HR 0.71, 95% CI 0.59 to 0.85) using the time-dependent Cox model. The multistate model did not suggest that the occurrence of irAEs could predict response; however, it predicted a higher chance of irAEs occurring after a response. No association was observed between response and infusion-related reactions.ConclusionsPatients who experience irAEs showed improved survival. Although irAEs are not predictors for response to immune checkpoint inhibitors, increased vigilance for irAEs is needed after treatment with avelumab.Trial registration numbers NCT01772004 and NCT02155647.

The correct identification of the source of a propagation process is crucial in many research fields. As a specific application, we consider source estimation of delays in public transportation networks. We propose two approaches: an effective distance median and a backtracking method. The former is based on a structurally generic effective distance-based approach for the identification of infectious disease origins, and the latter is specifically designed for delay propagation. We examine the performance of both methods in simulation studies and in an application to the German railway system, and we compare the results with those of a centralitybased approach for source detection.

Biological pathways provide rich information and biological context on the genetic causes of complex diseases. The logistic kernel machine test integrates prior knowledge on pathways in order to analyze data from genome-wide association studies (GWAS). In this study, the kernel converts the genomic information of 2 individuals into a quantitative value reflecting their genetic similarity. With the selection of the kernel, one implicitly chooses a genetic effect model. Like many other pathway methods, none of the available kernels accounts for the topological structure of the pathway or gene-gene interaction types. However, evidence indicates that connectivity and neighborhood of genes are crucial in the context of GWAS, because genes associated with a disease often interact. Thus, we propose a novel kernel that incorporates the topology of pathways and information on interactions. Using simulation studies, we demonstrate that the proposed method maintains the type I error correctly and can be more effective in the identification of pathways associated with a disease than non-network-based methods. We apply our approach to genome-wide association case-control data on lung cancer and rheumatoid arthritis. We identify some promising new pathways associated with these diseases, which may improve our current understanding of the genetic mechanisms.

We investigate the source detection problem in epidemiology, which is one of the most important issues for control of epidemics. Mathematically, we reformulate the problem as one of identifying the relevant component in a multivariate Gaussian mixture model. Focusing on the study of cholera and diseases with similar modes of transmission, we calibrate the parameters of our mixture model using human mobility networks within a stochastic, spatially explicit epidemiological model for waterborne disease. Furthermore, we adopt a Bayesian perspective, so that prior information on source location can be incorporated (e.g., reflecting the impact of local conditions). Posterior-based inference is performed, which permits estimates in the form of either individual locations or regions. Importantly, our estimator only requires first-arrival times of the epidemic by putative observers, typically located only at a small proportion of nodes. The proposed method is demonstrated within the context of the 2000-2002 cholera outbreak in the KwaZulu-Natal province of South Africa.