Effect of Longitudinal Variation in Tumor Volume Estimation for MRI-guided Personalization of Breast Cancer Neoadjuvant Treatment

Purpose To investigate the impact of longitudinal variation in functional tumor volume (FTV) underestimation and overestimation in predicting pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC). Materials and Methods Women with breast cancer who were enrolled in the prospective I-SPY 2 TRIAL (Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis 2) from May 2010 to November 2016 were eligible for this retrospective analysis. Participants underwent four MRI examinations during NAC treatment. FTV was calculated based on automated segmentation. Baseline FTV before treatment (FTV0) and the percentage of FTV change at early treatment and inter-regimen time points relative to baseline (∆FTV1 and ∆FTV2, respectively) were classified into high-standard or standard groups based on visual assessment of FTV under- and overestimation. Logistic regression models predicting pCR using single predictors (FTV0, ∆FTV1, and ∆FTV2) and multiple predictors (all three) were developed using bootstrap resampling with out-of-sample data evaluation with the area under the receiver operating characteristic curve (AUC) independently in each group. Results This study included 432 women (mean age, 49.0 years ± 10.6 [SD]). In the FTV0 model, the high-standard and standard groups showed similar AUCs (0.61 vs 0.62). The high-standard group had a higher estimated AUC compared with the standard group in the ∆FTV1 (0.74 vs 0.63), ∆FTV2 (0.79 vs 0.62), and multiple predictor models (0.85 vs 0.64), with a statistically significant difference for the latter two models

HSP90 is essential for Jak-STAT signaling in classical Hodgkin lymphoma cells

In classical Hodgkin lymphoma (cHL) chemotherapeutic regimens are associated with stagnant rates of secondary malignancies requiring the development of new therapeutic strategies. We and others have shown that permanently activated Signal Transducer and Activator of Transcription (STAT) molecules are essential for cHL cells. Recently an overexpression of heat-shock protein 90 (HSP90) in cHL cells has been shown and inhibition of HSP90 seems to affect cHL cell survival. Here we analysed the effects of HSP90 inhibition by geldanamycin derivative 17-AAG or RNA interference (RNAi) on aberrant Jak-STAT signaling in cHL cells. Treatment of cHL cell lines with 17-AAG led to reduced cell proliferation and a complete inhibition of STAT1, -3, -5 and -6 tyrosine phosphorylation probably as a result of reduced protein expression of Janus kinases (Jaks). RNAi-mediated inhibition of HSP90 showed similar effects on Jak-STAT signaling in L428 cHL cells. These results suggest a central role of HSP90 in permanently activated Jak-STAT signaling in cHL cells. Therapeutics targeting HSP90 may be a promising strategy in cHL and other cancer entities associated with deregulated Jak-STAT pathway activation

Disentangling fine-scale effects of environment on malaria detection and infection to design risk-based disease surveillance systems in changing landscapes

AbstractLandscape changes have complex effects on malaria transmission, disrupting social and ecological systems determining the spatial distribution of risk. Within Southeast Asia, forested landscapes are associated with both increased malaria transmission and reduced healthcare access. Here, we adapt an ecological modelling framework to identify how local environmental factors influence the spatial distributions of malaria infections, diagnostic sensitivity and detection probabilities in the Philippines. Using convenience sampling of health facility attendees and Bayesian latent process models, we demonstrate how risk-based surveillance incorporating forest data increases the probability of detecting malaria foci over three-fold and enables estimation of underlying distributions of malaria infections. We show the sensitivity of routine diagnostics varies spatially, with the decreased sensitivity in closed canopy forest areas limiting the utility of passive reporting to identify spatial patterns of transmission. By adjusting for diagnostic sensitivity and targeting spatial coverage of health systems, we develop a model approach for how to use landscape data within disease surveillance systems. Together, this illustrates the essential role of environmental data in designing risk-based surveillance to provide an operationally feasible and cost-effective method to characterise malaria transmission while accounting for imperfect detection.</jats:p

Serologic Responses in Childhood Pulmonary Tuberculosis.

BACKGROUND: Identification of the Mycobacterium tuberculosis immunoproteome and antigens associated with serologic responses in adults has renewed interest in developing a serologic test for childhood tuberculosis (TB). We investigated IgG antibody responses against M. tuberculosis antigens in children with well-characterized TB. METHODS: We studied archived sera obtained from hospitalized children with suspected pulmonary TB, and classified as having confirmed TB (culture-confirmed), unlikely TB (clinical improvement without TB treatment), or unconfirmed TB (all others). A multiplexed bead-based assay for IgG antibodies against 119 M. tuberculosis antigens was developed, validated and used to test sera. The area under the curves (AUCs) of the empiric receiver-operator characteristic curves were generated as measures of predictive ability. A cross-validated generalized linear model was used to select the most predictive combinations of antigens. RESULTS: For the confirmed TB versus unlikely TB comparison, the maximal single antigen AUC was 0.63, corresponding to sensitivity 0.60 and specificity 0.60. Older (age: 60+ months old) children's responses were better predictive of TB status than younger (age: 12-59 months old) children's, with a maximal single antigen AUC of -0.76. For the confirmed TB versus unlikely TB groups, the most predictive combinations of antigens assigned TB risk probabilities of 0.33 and 0.33, respectively, when all ages were considered, and 0.57 (interquartile range: 0.48-0.64) and 0.35 (interquartile range: 0.32-0.40) when only older children were considered. CONCLUSION: An antigen-based IgG test is unlikely to meet the performance characteristics required of a TB detection test applicable to all age groups

Application of two machine learning algorithms to genetic association studies in the presence of covariates

BACKGROUND: Population-based investigations aimed at uncovering genotype-trait associations often involve high-dimensional genetic polymorphism data as well as information on multiple environmental and clinical parameters. Machine learning (ML) algorithms offer a straightforward analytic approach for selecting subsets of these inputs that are most predictive of a pre-defined trait. The performance of these algorithms, however, in the presence of covariates is not well characterized. METHODS AND RESULTS: In this manuscript, we investigate two approaches: Random Forests (RFs) and Multivariate Adaptive Regression Splines (MARS). Through multiple simulation studies, the performance under several underlying models is evaluated. An application to a cohort of HIV-1 infected individuals receiving anti-retroviral therapies is also provided. CONCLUSION: Consistent with more traditional regression modeling theory, our findings highlight the importance of considering the nature of underlying gene-covariate-trait relationships before applying ML algorithms, particularly when there is potential confounding or effect mediation

Analytical approaches for antimalarial antibody responses to confirm historical and recent malaria transmission: an example from the Philippines

Background: Assessing the status of malaria transmission in endemic areas becomes increasingly challenging as countries approach elimination. Serology can provide robust estimates of malaria transmission intensities, and multiplex serological assays allow for simultaneous assessment of markers of recent and historical malaria exposure. Methods: Here, we evaluated different statistical and machine learning methods for analyzing multiplex malaria-specific antibody response data to classify recent and historical exposure to Plasmodium falciparum and Plasmodium vivax. To assess these methods, we utilized samples from a health-facility based survey (n = 9132) in the Philippines, where we quantified antibody responses against 8 P. falciparum and 6 P. vivax-specific antigens from 3 sites with varying transmission intensity. Findings: Measurements of antibody responses and seroprevalence were consistent with the 3 sites’ known endemicity status. Among the models tested, a machine learning (ML) approach (Random Forest model) using 4 serological markers (PfGLURP R2, Etramp5.Ag1, GEXP18, and PfMSP119) gave better predictions for P. falciparum recent infection in Palawan (AUC: 0.9591, CI 0.9497–0.9684) than individual antigen seropositivity. Although the ML approach did not improve P. vivax infection predictions, ML classifications confirmed the absence of recent exposure to P. falciparum and P. vivax in both Occidental Mindoro and Bataan. For predicting historical P. falciparum and P. vivax transmission, seroprevalence and seroconversion rates based on cumulative exposure markers AMA1 and MSP119 showed reliable trends in the 3 sites. Interpretation: Our study emphasizes the utility of serological markers in predicting recent and historical exposure in a sub-national elimination setting, and also highlights the potential use of machine learning models using multiplex antibody responses to improve assessment of the malaria transmission status of countries aiming for elimination. This work also provides baseline antibody data for monitoring risk in malaria-endemic areas in the Philippines. Funding: Newton Fund, Philippine Council for Health Research and Development, UK Medical Research Council

Pragmatic cluster-randomized trial of home-based preventive treatment for TB in Ethiopia and South Africa (CHIP-TB)

Background Each year, 1 million children develop TB resulting in over 200,000 child deaths. TB preventive treatment (TPT) is highly effective in preventing TB but remains poorly implemented for household child contacts. Home-based child contact management and TPT services may improve access to care. In this study, we aim to evaluate the effectiveness and cost-effectiveness of home-based contact management with TPT initiation in two TB high-burden African countries, Ethiopia and South Africa. Methods This pragmatic cluster randomized trial compares home-based versus facility-based care delivery models for contact management. Thirty-six clinics with decentralized TB services (18 in Ethiopia and 18 in South Africa) were randomized in a 1:1 ratio to conduct either home-based or facility-based contact management. The study will attempt to enroll all eligible close child contacts of infectious drug-sensitive TB index patients diagnosed and treated for TB by one of the study clinics. Child TB contact management, including contact tracing, child evaluation, and TPT initiation and follow-up, will take place in the childs home for the intervention arm and at the clinic for the control arm. The primary outcome is the cluster-level ratio of the number of household child contacts less than 15 years of age in Ethiopia and less than 5 years of age in South Africa initiated on TPT per index patient, comparing the intervention to the control arm. Secondary outcomes include child contact identification and the TB prevention continuum of care. Other implementation outcomes include acceptability, feasibility, fidelity, cost, and cost-effectiveness of the intervention. Discussion This implementation research trial will determine whether home-based contact management identifies and initiates more household child contacts on TPT than facility-based contact management.This project is funded by UNITAID and IMPAACT4TB. NSA salary is supported by the National Institutes of Health (K23HD096973). The Aurum Institute, 29 Queens Rd, Parktown, Johannesburg, 2194, South Africa is the sponsor of the trial. The funder had no role in the writing of this manuscript after concept approva

Enhanced health facility surveys to support malaria control and elimination across different transmission settings in the Philippines

Following substantial progress in malaria control in the Philippines, new surveillance approaches are needed to identify and target residual malaria transmission. This study evaluated an enhanced surveillance approach using rolling cross-sectional surveys of all health facility attendees augmented with molecular diagnostics and geolocation. Facility surveys were carried out in three sites representing different transmission intensities: Morong, Bataan (pre-elimination), Abra de Ilog, Occidental Mindoro (stable medium risk), and Rizal, Palawan (high risk, control). Only one rapid diagnostic test (RDT)–positive infection and no PCR confirmed infections were found in Bataan and Occidental Mindoro, suggesting the absence of transmission. In Palawan, the inclusion of all health facility attendees, regardless of symptoms, and use of molecular diagnostics identified 313 infected individuals in addition to 300 cases identified by routine screening of febrile patients with the RDT or microscopy. Of these, the majority (313/613) were subpatent infections and only detected using molecular methods. Simultaneous collection of GPS coordinates on tablet-based applications allowed real-time mapping of malaria infections. Risk factor analysis showed higher risks in children and indigenous groups, with bed net use having a protective effect. Subpatent infections were more common in men and older age-groups. Overall, malaria risks were not associated with participants’ classification, and some of the non-patient clinic attendees reported febrile illnesses (1.9%, 26/1,369), despite not seeking treatment, highlighting the widespread distribution of infection in communities. Together, these data illustrate the utility of health facility–based surveys to augment surveillance data to increase the probability of detecting infections in the wider community

Enhanced health facility surveys to support malaria control and elimination across different transmission settings in The Philippines

Abstract Following substantial progress in malaria control in the Philippines, new surveillance approaches are needed to identify and target residual malaria transmission. This study evaluated an enhanced surveillance approach using rolling cross-sectional surveys of all health facility attendees augmented with molecular diagnostics and geolocation. Facility surveys were carried out in 3 sites representing different transmission intensities: Morong, Bataan (pre-elimination), Abra de Ilog, Occidental Mindoro (stable-medium risk) and Rizal, Palawan (high risk, control). Only 1 RDT positive infection and no PCR confirmed infections were found in Bataan and Occidental Mindoro suggesting the absence of transmission. In Rizal, inclusion of all health facility attendees, regardless of symptoms, and use of molecular diagnostics identified an additional 313 infected individuals in addition to 300 cases identified by routine screening of febrile patients with RDT or microscopy. Of these, the majority (313/613) were subpatent infections and only detected using molecular methods. Simultaneous collection of GPS coordinates on tablet-based applications allowed real-time mapping of malaria infections. Risk factor analysis showed higher risks in children and indigenous groups, with bednet use having a protective effect. Subpatent infections were more common in men and older age groups. Overall, malaria risks were not associated with patient status and some of non-patient clinic attendees reported febrile illnesses (1.9%, 26/1369) despite not seeking treatment highlighting the widespread distribution of infection in communities. Together, these data illustrate the utility of health-facility based surveys to augment surveillance data to increase the probability of detecting infections in the wider community