Methods For Disease Monitoring Research Articles

A generalizable and interpretable model for early warning of pest-induced crop diseases using environmental data

Pest infestations and resulting crop diseases threaten global food security. Traditional pest and disease monitoring methods are time-consuming and prone to delays, thus necessitating the development of effective prediction strategies that facilitate early and timely detection. In response to this challenge, this research proposes a generalizable and interpretable machine learning model to predict two major rice pests—Green Leafhopper and Yellow Stem Borer—and a No Pest class using environmental data collected from various regions in India. The dataset, comprising factors such as temperature, humidity, and rainfall, underwent rigorous preprocessing. Key features like temperature difference, humidity difference, and vapor pressure deficit were engineered to enhance the model’s performance. Twelve baseline models were trained and their performance was evaluated using F1 scores and AUC values due to the imbalanced nature of the dataset. Through statistical analysis of baseline models, Random Forest, Balanced Random Forest, XGBoost, and CatBoost models, were selected for hyperparameter tuning via Optuna. The tuned CatBoost model demonstrated superior performance, achieving AUC values of 0.99 for Green Leafhoppers, 0.98 for Yellow Stem Borers, 1.00 for No Pest class and an overall F1 score of 0.9414 with a mean AUC value of 0.9912 across all classes. Additionally, Explainable Artificial Intelligence techniques, particularly SHAP, were employed to interpret the model’s predictions, identifying the relative importance of environmental factors in pest occurrence. This interpretability aligns the model’s predictions with established agronomic knowledge, enhancing its practical utility for early pest and disease detection. The generalizability of the proposed model suggests it can be adapted to other crops and regions, offering a valuable tool for early warning systems in agriculture, promoting sustainable practices, and reducing crop losses due to pests and diseases.

A high-available segmentation algorithm for corn leaves and leaf spot disease based on feature fusion

Detection and identification of corn diseases are crucial for disease control, and the segmentation of corn disease leaf image is a key step to achieve this goal. However, the images of diseased leaves in real fields are usually very complex, with characteristics of irregular shapes, blurred boundaries and unsharp background, which poses great challenges to disease prevention. To address this issue, our team constructed a dataset of diseased leaves with 857 images. Additionally, this paper proposes a high-availability segmentation algorithm for corn leaves with leaf spot disease, called SEF-UNet, which uses Res-UNet as the backbone network. The algorithm references SElayer and ELA (Efficient Local Attention). Simultaneously,we implement a feature fusion network that focuses on the output of each layer. Experimental results indicate that the Mean Intersection over Union (mIOU),Mean Pixel Accuracy (mPA), Mean Precision (mPrecision), and Mean Recall (mRecall),metrics of SEF-UNet network reach 92.62%, 95.74%, 96.63% and 95.64%.We compared our proposed method with UNet, Res-UNet, PspNet, DeepLabv3+, DANet, CCNet, Segformer-b3, and SEF-UNet under the same experimental conditions. The results demonstrate that our method significantly improves the accuracy of diseased leaf image segmentation. It provides a reference method for disease monitoring, as well as a technical basis for assessing disease severity.

Wavelet Transform and SVM Based Heart Disease Monitoring for Flexible Wearable Devices

INTRODUCTION: Heart disease has been a major health challenge globally, therefore the development of reliable and real-time heart disease monitoring methods is crucial for the prevention and management of heart health. The aim of this study is to explore a flexible wearable device approach based on wavelet transform and support vector machine (SVM) to improve the accuracy and portability of heart disease monitoring. OBJECTIVES: The main objective of this study is to develop a wearable device that combines wavelet transform and SVM techniques to achieve accurate monitoring of physiological signals of heart diseases. METHODS: An integrated method for heart disease monitoring was constructed using flexible sensor technology combined with a wavelet transform and support vector machine. The Marr wavelet transform was applied to the ECG signals, and the feature vectors were constructed by feature parameter extraction. Then, the radial basis kernel SVM was utilized to identify the three ECG signals. The performance of the algorithm was optimized by adjusting the SVM parameters to improve the accurate monitoring of heart diseases. RESULTS: The experimental results show that the proposed wavelet transform and SVM-based approach for flexible wearable devices achieves satisfactory results in heart disease monitoring. In particular, the algorithm successfully extracted feature vectors and accurately classified different ECG signals by skillfully combining the wavelet transform and SVM techniques for the processing of premature beat signals. CONCLUSION: The potential application value of the wavelet transform and SVM-based flexible wearable device approach in heart disease monitoring is emphasized. By efficiently processing ECG signals, the method provides an innovative and comfortable solution for real-time monitoring of cardiac diseases.

Microbubble-enhanced and activity-informed FUS liquid biopsy in infiltrating gliomas

In the absence of clinically available biomarkers for CNS malignancies, the conventional method for disease monitoring in glioma patients is radiologic. While most cancers shed cell-free molecules of tumor-derived DNA into the circulation, brain tumors form the exception to this rule, rarely shedding detectable levels of tumor DNA into the bloodstream. Microbubble-enhanced focused ultrasound (MB-FUS) is a rapidly emerging clinical tool to safely open the blood-brain barrier (BBB) for enhanced neurotherapeutic delivery and liquid biopsy. Previous work demonstrated MB-FUS treatments in infiltrating gliomas using multi-transducer systems with embedded acoustic emissions (AEMs) monitoring. In this study, we analyzed serum samples from a Phase 1 clinical trial of combined MB-FUS BBB opening with adjuvant temozolomide treatment of the tumor-infiltrated brain regions surrounding the surgical resection cavity of patients with newly diagnosed glioblastoma. The analysis evaluated the technical and treatment parameters associated with BBB opening, circulating DNA levels, and other biomarkers of MB-FUS effects. We analyzed serum samples using Real-SeqS and ELISA to quantify biomarker levels in longitudinal serum fluid samples derived from 13 patients undergoing repeated cycles of MB-FUS plus temozolomide. Our work and others in the field establish the potential for MB-FUS-enabled, non-invasive biopsy of gliomas, which would fundamentally advance the diagnosis, monitoring, and management of brain tumors.

An Integrated Multimodal Framework for Noninvasive TCL Disease Detection and Monitoring

Background: Early noninvasive identification of therapeutic responses could help accelerate the development of more effective therapies for T-cell lymphoma (TCL), and to improve patient outcomes. However, standardized noninvasive disease-monitoring strategies remain elusive for the most common mature T-cell tumors. Therefore, the development of highly sensitive and accurate disease monitoring methods for TCL represents a strong unmet clinical need. We tackled this challenge using an integrated liquid biopsy strategy leveraging both cell-free DNA (cfDNA) and cell-free RNA (cfRNA), by simultaneously interrogating tumor-specific aberrations, including somatic mutations, clonotypic TCR rearrangements, and TCL gene expression signatures. Methods: We studied 83 serial blood samples from 36 patients diagnosed with TCL and treated in the US or Japan, including 63 longitudinal samples from 30 cases from Stanford University receiving standard TCL therapies (NCT00398177), and 20 blood samples from 6 subjects with relapsed/refractory TCL treated in Kyushu University hospital. Cell-free DNA samples for all cases were profiled to simultaneously detect somatic mutations by CAPP-Seq (Newman et al 2016 Nature Biotech) as well as clonotypic VDJ rearrangements by SABER (Shukla et al 2020 ASH, Sworder et al 2022 Cancer Cell). Therefore, an integrated CAPP-Seq targeted panel was designed for hybrid capture to target recurrently mutated genes in diverse T-cell neoplasms along with TCR rearrangements at 4 loci. Captured libraries were sequenced to a median deduplicated depth of ~3000x. For CAPP-Seq, single nucleotide variants (SNVs) were identified by the analysis of tumor tissue or pre-treatment cfDNA; constitutional germline variants and clonal hematopoiesis (CHIP) variants were censored using matched DNA from either CD4+ T-cell depleted PBMCs or buccal swabs. For SABER, tumor clonotypic VDJ sequences were defined as most prevalent clone in tumor tissue or pretreatment plasma cfDNA. Plasma cfRNA was profiled for a subset of patients (20 serial samples from 13 patients) using RARE-Seq (Nesselbush et al, in preparation). We then measured correlations between radiographic disease status and circulating disease levels, as measured using mutant ctDNA and clonotypic TCR molecules, and cfRNA gene expression signatures. Results: Among the 36 TCL subjects, all cases (100%) could be noninvasively detected in plasma cfDNA using either somatic mutations (mutant ctDNA only: 86%) or using clonotypic cell-free TCR rearrangements (cfTCR only: 88%). When considering the concentrations of these distinct tumor reporters, mutant ctDNA levels (hGE/mL plasma) and cfTCR levels (# of clones/mL plasma) were significantly correlated (Pearson R = 0.65, p<0.01, Fig. 1A). When considering longitudinal measurements during and after therapy, circulating levels of both ctDNA and clonotypic cfTCR were significantly lower in patients experiencing objective responses (CR/PR) than in patients experiencing SD/PD (p<0.01). Moreover, when comparing acellular plasma versus cellular (PBMC) blood fractions, circulating disease levels were significantly higher in plasma and often became undetectable in PBMCs (Fig. 1B). When considering TCR repertoires, CDR3 diversity was significantly lower at timepoints of measurable disease (pre-treatment, SD, PD). Furthermore, clonotypic tumor cell-free TCR (tumor cfTCR) fragments were significantly shorter than non-tumor cfTCR molecules (p<0.01). When considering cfRNA, a gene expression signature of activated CD4 memory T-cells was strongly correlated with both mutant ctDNA and tumor cfTCR levels (R=0.85, 0.64), and was higher at timepoints of active TCL disease. Conclusions: Noninvasive TCL detection and monitoring appears feasible through liquid biopsies interrogating cfDNA and cfRNA, with advantages over PBMCs. The complementarity for disease detection using tumor-specific somatic mutations, clonotypic TCR rearrangements, and gene expression levels, strongly suggests synergies to enable an integrated multimodal liquid biopsy framework for TCL. This novel platform should allow more accurate assessment of disease status and MRD, and to inform risk-adapted treatment strategies in TCL.

Identification of salivary metabolic biomarker signatures for oral tongue squamous cell carcinoma

ObjectiveTo identify the salivary metabolites associated with squamous cell carcinoma of the tongue to develop easy and non-invasive potential biomarkers for disease diagnosis. DesignInitially, the study utilized untargeted metabolomics to analyze 20 samples of tongue squamous cell carcinoma and 10 control samples. The objective was to determine the salivary metabolites that exhibited differential expression in tongue squamous cell carcinoma. Then the selected metabolites were validated using targeted metabolomics in saliva samples of 100 patients diagnosed with squamous cell carcinoma of the tongue, as well as 30 healthy control individuals. ResultsFrom the analysis of untargeted metabolomics, 10 metabolites were selected as potential biomarkers. In the subsequent targeted metabolomics study on these selected metabolites, it was observed that N-Acetyl-D-glucosamine, L-Pipecolic acid, L-Carnitine, Phosphorylcholine, and Deoxyguanosine exhibited significant differences. The receiver operating characteristic curve analysis indicates a combination of three important metabolites such as N-Acetyl-D-glucosamine, L-Pipecolic acid and L-Carnitine provided the best prediction with an area under the curve of 0.901. ConclusionsThe present result reveals that the N-Acetyl-D-glucosamine, L-Pipecolic acid and L-Carnitine are the signature diagnostic biomarkers for oral tongue squamous cell carcinoma. These findings can be used to develop a rapid and non-invasive method for disease monitoring and prognosis in oral tongue cancer.

Large‐scale eDNA monitoring of multiple aquatic pathogens as a tool to provide risk maps for wildlife diseases

AbstractMultiple parasites and pathogens cause disease in aquatic wildlife and in aquaculture species, generating a need for monitoring and management. Conventional disease monitoring methods involve laborious, costly, and invasive capture and examination of host species, and require specialized expertise for every host and pathogen of interest. Environmental DNA could provide simultaneous occurrence data for multiple pathogens across different host taxa, valuable for using parasite diversity as, for example, a bioindicator of ecosystem disturbance. Here, we tested the potential for simultaneous detection of four wildlife pathogens in water samples from 280, mainly riverine, sites across Switzerland. We targeted the crayfish pathogen Aphanomyces astaci, the amphibian pathogen Batrachochytrium dendrobatidis, and the fish pathogens Saprolegnia parasitica and Tetracapsuloides bryosalmonae. The eDNA detection showed a widespread distribution of A. astaci, S. parasitica, and T. bryosalmonae, although A. astaci and T. bryosalmonae were not detected in some alpine river catchments. B. dendrobatidis was detected only rarely, which was expected since the sampling did not target amphibian breeding sites. Co‐detection rates were higher in rivers than in lakes, likely reflecting the habitat preferences and distributions of the host species. We discuss the advantages and limitations of eDNA‐based pathogen monitoring and list a set of recommendations for managers. Our study illustrates how eDNA‐based techniques can monitor several pathogen species concurrently, thus facilitating more comprehensive disease monitoring schemes. Combined with metabarcoding approaches in the future, eDNA‐based sampling and detection can facilitate the incorporation of parasite and pathogen occurrence and diversity as an indicator for aquatic ecosystem health, and for revealing the hidden biodiversity and structure of parasite communities.

Skull base osteomyelitis in the Auckland region

Background: Skull base osteomyelitis (SBO) inflicts significant morbidity, mortality, and health resource burden in our region. Despite an increasing literature on the condition, and rising incidence in parts of the world, there continues to be a lack of consensus on prognostication, treatment duration, and disease monitoring methods.

Measurable Residual Disease and Clonal Evolution in Acute Myeloid Leukemia from Diagnosis to Post-Transplant Follow-Up: The Role of Next-Generation Sequencing

It has now been ascertained that acute myeloid leukemias-as in most type of cancers-are mixtures of various subclones, evolving by acquiring additional somatic mutations over the course of the disease. The complexity of leukemia clone architecture and the phenotypic and/or genotypic drifts that can occur during treatment explain why more than 50% of patients-in hematological remission-could relapse. Moreover, the complexity and heterogeneity of clone architecture represent a hindrance for monitoring measurable residual disease, as not all minimal residual disease monitoring methods are able to detect genetic mutations arising during treatment. Unlike with chemotherapy, which imparts a relatively short duration of selective pressure on acute myeloid leukemia clonal architecture, the immunological effect related to allogeneic hematopoietic stem cell transplant is prolonged over time and must be overcome for relapse to occur. This means that not all molecular abnormalities detected after transplant always imply inevitable relapse. Therefore, transplant represents a critical setting where a measurable residual disease-based strategy, performed during post-transplant follow-up by highly sensitive methods such as next-generation sequencing, could optimize and improve treatment outcome. The purpose of our review is to provide an overview of the role of next-generation sequencing in monitoring both measurable residual disease and clonal evolution in acute myeloid leukemia patients during the entire course of the disease, with special focus on the transplant phase.

Crop Disease Detection against Complex Background Based on Improved Atrous Spatial Pyramid Pooling

Timely crop disease detection, pathogen identification, and infestation severity assessments can aid disease prevention and control efforts to mitigate crop-yield decline. However, improved disease monitoring methods are needed that can extract high-resolution, accurate, and rich color and spatial features from leaf disease spots in the field to achieve precise fine-grained disease-severity classification and sensitive disease-recognition accuracy. Here, we propose a neural-network-based method incorporating an improved Rouse spatial pyramid pooling strategy to achieve crop disease detection against a complex background. For neural network construction, first, a dual-attention module was introduced into the cross-stage partial network backbone to enable extraction of multi-dimensional disease information from the channel and space perspectives. Next, a dilated convolution-based spatial pyramid pooling module was integrated within the network to broaden the scope of the collection of crop-disease-related information from images of crops in the field. The neural network was tested using a set of sample data constructed from images collected at a rate of 40 frames per second that occupied only 17.12 MB of storage space. Field data analysis conducted using the miniaturized model revealed an average precision rate approaching 90.15% that exceeded the corresponding rates obtained using comparable conventional methods. Collectively, these results indicate that the proposed neural network model simplified disease-recognition tasks and suppressed noise transmission to achieve a greater accuracy rate than is obtainable using similar conventional methods, thus demonstrating that the proposed method should be suitable for use in practical applications related to crop disease recognition.

Serum Free Light Chain Quantification Testing: Comparison of Two Methods for Disease Monitoring.

Levels of free immunoglobulin light chains in serum and urine are a sensitive measure of dysregulated immunoglobulin synthesis. The development of an assay for free light chains in serum was a major advance in laboratory testing for monoclonal gammopathies. The original assay by The Binding Site, called Freelite®, has been in common use in laboratory monitoring of monoclonal gammopathies. Two clinical entities, myeloma-defining condition and light chain-predominant multiple myeloma, rely on quantitative measurements of serum free light chains. Using polyclonal antisera specific to free light chains, Diazyme Laboratories developed a latex immunoturbidimetric assay for quantification of human kappa and lambda serum free light chains. We evaluated the Diazyme assay by comparing the results of kappa and lambda free light chain quantification, and kappa/lambda ratio with the results on the same specimens by the Freelite method. We also compared the correlation of the 2 methods to evaluate response to treatment and to changes in clinical status of patients with multiple myeloma. The results of Freelite and Diazyme methods are comparable. There was no statistically significant difference in the performance of the 2 assays for quantification of light chains, kappa/lambda ratio, or correlation of clinical parameters from patients with multiple myeloma at various stages of monitoring the disease in 2 geographically diverse laboratory and clinical environments. The Diazyme method is comparable to Freelite and provides an opportunity to add the test to front-end automation and improvement in efficiency of the assay.

Colorectal Cancer Incidence, Inequalities, and Prevention Priorities in Urban Texas: Surveillance Study With the "surveil" Software Package.

BackgroundMonitoring disease incidence rates over time with population surveillance data is fundamental to public health research and practice. Bayesian disease monitoring methods provide advantages over conventional methods including greater flexibility in model specification and the ability to conduct formal inference on model-derived quantities of interest. However, software platforms for Bayesian inference are often inaccessible to nonspecialists.ObjectiveTo increase the accessibility of Bayesian methods among health surveillance researchers, we introduce a Bayesian methodology and open source software package, surveil, for time-series modeling of disease incidence and mortality. Given case count and population-at-risk data, the software enables health researchers to draw inferences about underlying risk and derivative quantities including age-standardized rates, annual and cumulative percent change, and measures of inequality.MethodsWe specify a Poisson likelihood for case counts and model trends in log-risk using the first-difference (random-walk) prior. Models in the surveil R package were built using the Stan modeling language. We demonstrate the methodology and software by analyzing age-standardized colorectal cancer (CRC) incidence rates by race and ethnicity for non-Latino Black (Black), non-Latino White (White), and Hispanic/Latino (of any race) adults aged 50-79 years in Texas’s 4 largest metropolitan statistical areas between 1999 and 2018.ResultsOur analysis revealed a cumulative decline of 31% (95% CI –37% to –25%) in CRC risk among Black adults, 17% (95% CI –23% to –11%) for Latino adults, and 35% (95% CI –38% to –31%) for White adults from 1999 to 2018. None of the 3 observed groups experienced significant incidence reduction in the final 4 years of the study (2015-2018). The Black-White rate difference (per 100,000) was 44 (95% CI 30-57) in 1999 and 35 (95% CI 28-43) in 2018. Cumulatively, the Black-White gap accounts for 3983 CRC cases (95% CI 3746-4219) or 31% (95% CI 29%-32%) of total CRC incidence among Black adults in this period.ConclusionsStalled progress on CRC prevention and excess CRC risk among Black residents warrant special attention as cancer prevention and control priorities in urban Texas. Our methodology and software can help the public and health agencies monitor health inequalities and evaluate progress toward disease prevention goals. Advantages of the methodology over current common practice include the following: (1) the absence of piecewise linearity constraints on the model space, and (2) formal inference can be undertaken on any model-derived quantities of interest using Bayesian methods.

Capturing, creating, and characterizing circulating tumor cells and clusters

Microfluidic devices can aid in developing in vitro models and pave the way for liquid biopsy as a minimally invasive method for disease monitoring.

Minimal residual disease monitoring via AML1-ETO breakpoint tracing in childhood acute myeloid leukemia

Relapse of childhood AML1-ETO (AE) acute myeloid leukemia is the most common cause of treatment failure. Optimized minimal residual disease monitoring methods is required to prevent relapse. In this study, we used next-generation sequencing to identify the breakpoints in the fusion gene and the DNA-based droplet digital PCR (ddPCR) method was used for dynamic monitoring of AE-DNA. The ddPCR technique provides more sensitive and precise quantitation of the AE gene during disease progression and relapse. Quantification of the AE fusion gene by ddPCR further contributes to improved prognosis. Our study provides valuable methods for dynamic surveillance of AE fusion DNA and assistance in determining the prognosis.

One-Step Multiplexed Droplet Digital Polymerase Chain Reaction for Quantification of p190 BCR-ABL1 Fusion Transcript in B-Lymphoblastic Leukemia.

Quantification and detection of the t(9;22) (BCR-ABL1) translocation in chronic myelogenous leukemia and B-lymphoblastic leukemia are important for directing treatment protocols and monitoring disease relapse. However, quantification using traditional reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) is dependent on a calibration curve and is prone to laboratory-to-laboratory variation. Droplet digital polymerase chain reaction (ddPCR) is a novel method that allows for highly sensitive absolute quantification of transcript copy number. As such, ddPCR is a good candidate for disease monitoring, an assay requiring reproducible measurements with high specificity and sensitivity. To compare results of ddPCR and RT-qPCR BCR-ABL1 fusion transcript measurements of patient samples and determine if either method is superior. We optimized and standardized a 1-step multiplexed ddPCR assay to detect BCR-ABL1 p190 and ABL1 e10 transcripts. The ddPCR optimization included varying cycle number and primer concentration with standardization of droplet generation and droplet number and analyses to improve data sensitivity. Following optimization, ddPCR measurements were performed on clinical samples and compared with traditional RT-qPCR results. Droplet digital polymerase chain reaction was able to detect the BCR-ABL1 p190 transcript to 0.001% (1:10-5) with a calculated limit of detection and limit of quantitation of 4.1 and 5.3 transcripts, respectively. When tested on patient samples, ddPCR was able to identify 20% more positives than a laboratory-developed 2-step RT-qPCR assay. Droplet digital polymerase chain reaction demonstrated increased detection of BCR-ABL1 compared with RT-qPCR. Improved detection of BCR-ABL1 p190 and the potential for improved standardization across multiple laboratories makes ddPCR a suitable method for disease monitoring in patients with acute B-lymphoblastic leukemia.

Abstract 728: Early assessment of treatment effect in small cell lung cancer (SCLC) via ctDNA analysis

Abstract INTRODUCTION: Quantifying circulating tumor DNA (ctDNA) is an emerging method to non-invasively assess treatment effect for solid tumors. A ctDNA-based method for disease monitoring for small cell lung cancer (SCLC) would be beneficial to disease management. By employing plasma taken during early treatment cycles and NGS, we tested whether early response assessed by ctDNA level could predict treatment effect. METHODS: Using a 197-gene NGS assay, the AVENIO ctDNA Surveillance Kit (for Research Use Only, not for use in diagnostic procedures), ctDNA levels were assessed in post-treatment plasma samples based on variants identified at baseline. Mutant allele concentration or mutant molecules per milliliter-of-plasma (MMPM) was utilized to quantify ctDNA over all variants of all sequenced genomic regions. Samples collected from an observational German Lung Cancer Multi-Marker Study were analyzed retrospectively. In a cohort of 69 stage IV SCLC subjects, the association of survival with ctDNA-based monitoring classifier was evaluated in the first available post-treatment plasma sample after starting chemotherapy (median number of days after start of treatment = 25) by Kaplan-Meier methods and Cox proportional hazards model. RESULTS: The analysis demonstrated that subjects with mean MMPM less than the lower quartile (14.3) had longer progression free survival (PFS) compared to that of subjects with mean MMPM greater than or equal to the lower quartile (log-rank P = 0.0095; HR [95% CI] = 0.48 [0.27, 0.84]). The same classifier was applied for overall survival analysis, and subjects with mean MMPM less than the lower quartile had longer overall survival (OS) compared to that of subjects with mean MMPM greater than or equal to the lower quartile (log-rank P = 0.0095; HR [95% CI] = 0.46 [0.26, 0.84]). CONCLUSIONS: Changes in ctDNA levels in response to treatment may prove to be a valuable way of identifying subjects that either respond or fail to respond to therapy earlier than current standard of care methods such as computed tomography (CT) scan. Hypotheses will be validated in a separate cohort. Future prospective studies to confirm these results are needed. Citation Format: John Jiang, Christine Ju, Corinna Woestmann, Aarthi Balasubramanyam, Liu Xi, Stephanie J. Yaung, Sandeep Gattam, Bernd Hinzmann, Michael Thomas, Felix Lasitschka, Michael Meister, Marc Schneider, Felix Herth, Thomas Muley, Birgit Wehnl, John Palma. Early assessment of treatment effect in small cell lung cancer (SCLC) via ctDNA analysis [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 728.

Elevated Interleukin-6 Levels Predict Clinical Worsening in Pediatric Pulmonary Arterial Hypertension

To assess whether circulating interleukin-6 (IL-6) is associated with measures of disease severity and clinical worsening in pediatric pulmonary arterial hypertension (PAH). IL-6 was measured by enzyme-linked immunosorbent assay in serum samples from a cross-sectional cohort from the National Heart, Lung, and Blood Institute Pulmonary Arterial Hypertension Biobank (n=175) and a longitudinal cohort from Children's Hospital Colorado (CHC) (n=61). Associations between IL-6, disease severity, and outcomes were studied with regression and Kaplan-Meier analysis. In analyses adjusted for age and sex, each log-unit greater IL-6 was significantly associated in the Pulmonary Arterial Hypertension Biobank cohort with greater pulmonary vascular resistance indices, lower odds of having idiopathic PAH or treatment with prostacyclin, and greater odds of having PAH associated with a repaired congenital shunt. In the CHC cohort, each log-unit greater IL-6 was significantly associated with greater mean pulmonary arterial pressure over time. Kaplan-Meier analysis in the CHC cohort revealed that IL-6 was significantly associated with clinical worsening (a composite score of mortality, transplant, or palliative surgery) (P=.037). IL-6 was significantly associated with worse hemodynamics at baseline and over time and may be associated with clinical worsening. IL-6 may provide a less-invasive method for disease monitoring and prognosis in pediatric PAH as well as a potential therapeutic target.

Mobile Health Daily Life Monitoring for Parkinson Disease: Development and Validation of Ecological Momentary Assessments.

BackgroundParkinson disease monitoring is currently transitioning from periodic clinical assessments to continuous daily life monitoring in free-living conditions. Traditional Parkinson disease monitoring methods lack intraday fluctuation detection. Electronic diaries (eDiaries) hold the potential to collect subjective experiences on the severity and burden of motor and nonmotor symptoms in free-living conditions.ObjectiveThis study aimed to develop a Parkinson disease–specific eDiary based on ecological momentary assessments (EMAs) and to explore its validation.MethodsAn observational cohort of 20 patients with Parkinson disease used the smartphone-based EMA eDiary for 14 consecutive days without adjusting free-living routines. The eDiary app presented an identical questionnaire consisting of questions regarding affect, context, motor and nonmotor symptoms, and motor performance 7 times daily at semirandomized moments. In addition, patients were asked to complete a morning and an evening questionnaire.ResultsMean affect correlated moderate-to-strong and moderate with motor performance (R=0.38 to 0.75; P<.001) and motor symptom (R=0.34 to 0.50; P<.001) items, respectively. The motor performance showed a weak-to-moderate negative correlation with motor symptoms (R=−0.31 to −0.48; P<.001). Mean group answers given for on-medication conditions vs wearing-off-medication conditions differed significantly (P<.05); however, not enough questionnaires were completed for the wearing-off-medication condition to reproduce these findings on individual levels.ConclusionsWe presented a Parkinson disease–specific EMA eDiary. Correlations between given answers support the internal validity of the eDiary and underline EMA’s potential in free-living Parkinson disease monitoring. Careful patient selection and EMA design adjustment to this targeted population and their fluctuations are necessary to generate robust proof of EMA validation in future work. Combining clinical Parkinson disease knowledge with practical EMA experience is inevitable to design and perform studies, which will lead to the successful integration of eDiaries in free-living Parkinson disease monitoring.

Monitoring type 2 diabetes from volatile faecal metabolome in Cushing\u2019s syndrome and single Afmid mouse models via a longitudinal study

The analysis of volatile organic compounds (VOCs) as a non-invasive method for disease monitoring, such as type 2 diabetes (T2D) has shown potential over the years although not yet set in clinical practice. Longitudinal studies to date are limited and the understanding of the underlying VOC emission over the age is poorly understood. This study investigated longitudinal changes in VOCs present in faecal headspace in two mouse models of T2D – Cushing’s syndrome and single Afmid knockout mice. Longitudinal changes in bodyweight, blood glucose levels and plasma insulin concentration were also reported. Faecal headspace analysis was carried out using selected ion flow tube mass spectrometry (SIFT-MS) and thermal desorption coupled to gas chromatography-mass spectrometry (TD-GC-MS). Multivariate data analysis of the VOC profile showed differences mainly in acetic acid and butyric acid able to discriminate the groups Afmid and Cushing’s mice. Moreover, multivariate data analysis revealed statistically significant differences in VOCs between Cushing’s mice/wild-type (WT) littermates, mainly short-chain fatty acids (SCFAs), ketones, and alcohols, and longitudinal differences mainly attributed to methanol, ethanol and acetone. Afmid mice did not present statistically significant differences in their volatile faecal metabolome when compared to their respective WT littermates. The findings suggested that mice developed a diabetic phenotype and that the altered VOC profile may imply a related change in gut microbiota, particularly in Cushing’s mice. Furthermore, this study provided major evidence of age-related changes on the volatile profile of diabetic mice.

Value of Flow Cytometry for MRD-Based Relapse Prediction in B-Cell Precursor Acute Lymphoblastic Leukemia in a Multi-Center Setting

Background: PCR of rearranged antigen receptor genes is the method of choice for MRD quantification in ALL. Although FCM-MRD is faster and biologically more informative than PCR, the analysis requires a high level of training. The only larger published studies using FCM-MRD based stratification (Borowitz, Blood, 2008 and 2015) showed a clear association with clinical outcome in BCP-ALL. However, MRD analyses were centralized and these studies included only one MRD-based stratification (MRD levels at the end of induction). Patients and methods: We examined FCM-MRD as stratification tool in BCP-ALL at various timepoints in a large-scale multicenter (18 MRD centers) study. A total of 1487 patients with BCP-ALL (1298 children (younger than 18 years) and 189 adults (18-45 years) are included in the study and were treated according to the NOPHO ALL2008 protocol between July 2008 and February 2016. The median follow-up time for patients in first remission was 51 months (IQR 32-75). MRD was measured by FCM and/or real time quantitative PCR on days 15, 29 (end of induction) and 79 (for standard (SR) and intermediate risk (IR) patients) and prior to and after high risk blocks. A 6-colour FCM analysis including 3 standardized antibody combinations was used and performed in 18 laboratories. Patients were stratified by FCM-MRD, or by PCR-MRD if no FCM-MRD marker was available. End-of-induction MRD (cut-off 10-3) was used to stratify patients to standard risk (SR) vs intermediate risk (IR) or IR vs high risk consolidation therapy (in case of WBC &gt; 100 x 109/L at diagnosis). Patients with MRD &gt;=2.5x10-1 on day 15 were stratified to high risk block therapy. Patients with MRD &gt;=5x10-2 on day 29 or day 79/post high risk-2 block MRD &gt;=10-3 were stratified to HSCT. Primary outcomes were 5year event-free survival (5y EFS) and 5year cumulative incidence of relapse (5y CIR). Results: Only two patients (0.14% of total) had neither an informative FCM nor a PCR marker, and an informative FCM marker combination for MRD monitoring was identified in 96.2% of patients. There was a significant correlation between FCM- and PCR-MRD levels on day 15 (r=0.77, p&lt;0.0001, n=153) and 29 (r=0.81, p&lt;0.0001, n=140). Based on FCM-MRD only, the median MRD level on day 15, 29 and 79/post high risk-2 block was 5x10-3, 1.1x10-4, and below detection limit, respectively. Adults had significantly higher MRD levels at all time-points (p&lt;0.0001 for day 15 and 29, p=0.0019 for day 79, Mann-Whitney). The 5y EFS was 86.1% (95% CI 84.1-88.1) with a 5y CIR of 9.5% (95% CI 7.8-11.3, n=1487). The day 29 FCM-MRD level was closely associated with clinical outcome and a higher hazard of relapse was seen independently for a FCM-MRD &gt;=10-3 (hazard ratio (HR) 2.4, CI 1.6-3.7, p&lt;0.0001), age&gt;18 year (HR 3.0, CI 1.7-5.3, p&lt;0.0001), WBC&gt;=100 (HR 2.7, CI 1.6-4.6, p=0.0001), and B-other (HR 2.1, CI 1.2-3.5, p=0.0052) or high risk B-ALL cytogenetic aberration (rearranged KMT2A/iAMPchr21/hypodiploid) (HR 3.2, CI 1.6-6.1, p=0.0006) (multivariate cause-specific Cox regression, n=1328). Patients with a day 79 FCM-MRD &gt;=10-4 and &lt;10-3 had a significantly higher CIR (22.1%, CI 10.8-33.5%, n=68) compared to FCM-MRD &lt;10-4 (7.5%, CI 2.1-12.8%, n=110) or undetectable (6.3%, CI 4.5-8.2%, n=999, p=0.0087 for FCM-MRD &gt;=10-4 and &lt;10-3vs &lt;10-4 or undetectable). After adjusting for WBC, age, and the day 29 FCM-MRD level, a day 79 FCM-MRD &gt;=10-4 and &lt;10-3 was still significantly associated with a worse 5y CIR for non-transplanted patients (HR 2.3, CI 1.19-4.36, p=0.012 compared to undetectable FCM-MRD, n=1171). Patients with day 15 FCM-MRD &lt;10-3 had a significantly better 5y EFS (92.0%, CI 89.2-95.0%) and CIR (3.9%, CI 1.7-6.1%, n=432) than patients with FCM-MRD &gt;=10-3 and &lt;2.5x10-1, who had a 5y EFS of 85.5% (CI 82.7-88.3%, p=0.0016, n=837) and a 3-fold higher 5y CIR (11.0%, CI 8.4-13.5%, p&lt;0.0001, n=432). Among patients with day 15 FCM-MRD&lt;10-3, the relapse incidence was comparable for patients with FCM-MRD 10-4 - &lt;10-3 and below 10-4 (CIR 3.6, CI 0.5-6.7 vs. CIR 4.1, CI 1.0-7.2, p=0.83, n=432). Conclusion: FCM-MRD performed in a multi-center setting is a clinically useful method for disease monitoring and MRD-based treatment stratification in BCP-ALL. Moreover, FCM-MRD is a reliable indicator of outcome in BCP-ALL independently of other key risk factors. Residual disease &gt;=10-4 and &lt;10-3 at day 79 in SR/IR patients not allocated to HSCT further identifies patients with a high risk of relapse. Disclosures No relevant conflicts of interest to declare.