ROC analysis for multiple markers with tree-based classification

Metabolomics of early blight (Alternaria solani) susceptible tomato (Solanum lycopersicum) unfolds key biomarker metabolites and involved metabolic pathways

Tomato (Solanum lycopersicum) is among the most important commercial horticultural crops worldwide. The crop quality and production is largely hampered due to the fungal pathogen Alternaria solani causing necrotrophic foliage early blight disease. Crop plants usually respond to the biotic challenges with altered metabolic composition and physiological perturbations. We have deciphered altered metabolite composition, modulated metabolic pathways and identified metabolite biomarkers in A. solani-challenged susceptible tomato variety Kashi Aman using Liquid Chromatography-Mass Spectrometry (LC-MS) based metabolomics. Alteration in the metabolite feature composition of pathogen-challenged (m/z 9405) and non-challenged (m/z 9667) plant leaves including 8487 infection-exclusive and 8742 non-infection exclusive features was observed. Functional annotation revealed putatively annotated metabolites and pathway mapping indicated their enrichment in metabolic pathways, biosynthesis of secondary metabolites, ubiquinone and terpenoid-quinones, brassinosteroids, steroids, terpenoids, phenylpropanoids, carotenoids, oxy/sphingolipids and metabolism of biotin and porphyrin. PCA, multivariate PLS-DA and OPLS-DA analysis showed sample discrimination. Significantly up regulated 481 and down regulated 548 metabolite features were identified based on the fold change (threshold ≥ 2.0). OPLS-DA model based on variable importance in projection (VIP scores) and FC threshold (> 2.0) revealed 41 up regulated discriminant metabolite features annotated as sphingosine, fecosterol, melatonin, serotonin, glucose 6-phosphate, zeatin, dihydrozeatin and zeatin-β-D-glucoside. Similarly, 23 down regulated discriminant metabolites included histidinol, 4-aminobutyraldehyde, propanoate, tyramine and linalool. Melatonin and serotonin in the leaves were the two indoleamines being reported for the first time in tomato in response to the early blight pathogen. Receiver operating characteristic (ROC)-based biomarker analysis identified apigenin-7-glucoside, uridine, adenosyl-homocysteine, cGMP, tyrosine, pantothenic acid, riboflavin (as up regulated) and adenosine, homocyctine and azmaline (as down regulated) biomarkers. These results could aid in the development of metabolite-quantitative trait loci (mQTL). Furthermore, stress-induced biosynthetic pathways may be the potential targets for modifications through breeding programs or genetic engineering for improving crop performance in the fields.

Total atrial conduction time provides novel information in prediction for stroke in patients with sinus rhythm

The total atrial conduction time (TACT) measured by echocardiography predicts the risk of atrial fibrillation (AF). This study aimed to investigate whether adding the TACT to the revised Framingham stroke risk profile (rFSRP) improves the efficacy of predicting stroke incidence in patients without prior stroke or known AF. The TACT was measured in 376 consecutive patients > 18 years (58.5 ± 16.3 years; 46% male) receiving echocardiography without any prior history of stroke or AF. The primary endpoint was the occurrence of ischemic stroke, and the secondary endpoint was any documentation of AF during the 2 years of follow-up. During the follow-up period, ischemic strokes occurred in 10 patients (2.65%), and AF in 22 patients (5.85%). The TACT was significantly longer in those who later had a stroke compared with those who did not (169.4 vs. 142.7 ms, p < 0.001). Both rFSRP and TACT predicted the risk for stroke incidence. The univariate model showed that the TACT was a predictor of ischemic stroke incidence (p < 0.001; hazard ratio of 1.94 for every 10 ms; 95% confidence interval, 1.49-2.54). The addition of TACT to rFSRP significantly improved the area under the receiver operating characteristic curve (0.79 vs. 0.85, p = 0.001). Stroke risk prediction was significantly improved by the addition of TACT to rFSRP. The utility of the TACT should be further investigated in large-scale randomized clinical trials.

Exploring Metabolic Signatures of Ex Vivo Tumor Tissue Cultures for Prediction of Chemosensitivity in Ovarian Cancer

Simple Summary

Women diagnosed with ovarian cancer have 5-year survival rates below 45%. Prediction of patient’s outcome and the onset of drug resistance are still major challenges. The patient’s drug response is influenced by the environment that surrounds the tumor cells. We previously showed that patient-derived tumor tissue can be kept in the lab, alive and retaining aspects of that environment. In this study, we exposed tumor tissue derived from ovarian cancer patients to the chemotherapy patients receive and identified metabolites released by the tumor tissue after treatment (metabolic footprint). Using machine learning, we uncovered metabolic signatures that discriminate tumor tissues with higher vs. lower drug sensitivity. We propose potential biomarkers involved in the production of specific building blocks of cells and energy generation processes. Overall, we established a platform to explore metabolic features of the complex environment of each patient’s tumor that can underpin the discovery of biomarkers of drug response.

Abstract

Predicting patient response to treatment and the onset of chemoresistance are still major challenges in oncology. Chemoresistance is deeply influenced by the complex cellular interactions occurring within the tumor microenvironment (TME), including metabolic crosstalk. We have previously shown that ex vivo tumor tissue cultures derived from ovarian carcinoma (OvC) resections retain the TME components for at least four weeks of culture and implemented assays for assessment of drug response. Here, we explored ex vivo patient-derived tumor tissue cultures to uncover metabolic signatures of chemosensitivity and/or resistance. Tissue cultures derived from nine OvC cases were challenged with carboplatin and paclitaxel, the standard-of-care chemotherapeutics, and the metabolic footprints were characterized by LC-MS. Partial least-squares discriminant analysis (PLS-DA) revealed metabolic signatures that discriminated high-responder from low-responder tissue cultures to ex vivo drug exposure. As a proof-of-concept, a set of potential metabolic biomarkers of drug response was identified based on the receiver operating characteristics (ROC) curve, comprising amino acids, fatty acids, pyrimidine, glutathione, and TCA cycle pathways. Overall, this work establishes an analytical and computational platform to explore metabolic features of the TME associated with response to treatment, which can leverage the discovery of biomarkers of drug response and resistance in OvC.

Values of serum PCT, suPAR combined with severity scores for evaluating prognosis of septic shock patients

Background: To explore the values of serum procalcitonin (PCT), soluble urokinase-type plasminogen activator receptor (suPAR) combined with APACHE II and SOFA scores for evaluating the prognosis of septic shock patients.

Materials and Methods: A total of 118 eligible patients admitted from August 2017 to January 2021 were divided into survival and death groups. Serum PCT and suPAR levels were detected. APACHE II and SOFA scores were evaluated. A combination predictor pre1 was constructed. The predictive efficacy of the indicator alone or in combination was compared using receiver operating characteristic curve. Risk factors leading to death were analyzed, and a predictive model was established.

Results: Serum PCT and suPAR levels as well as APACHE II and SOFA scores of death group significantly exceeded those of the survival group (P<0.05). PCT, suPAR, SOFA and APACHE II scores were valuable for predicting death. The area under curve (AUC) constructed by predictor pre1 for predicting death was largest. PCT, suPAR, APACHE II, and SOFA scores were independent risk factors for death. The model had AUC of 0.828, with the sensitivity of 86.54%, specificity of 89.03%, and accuracy of 82.47%. The death risk predicted by the model had a high concurrence with the actual one.

Conclusion: PCT, suPAR, APACHE II, and SOFA scores are closely related to the prognosis of septic shock patients. The combined predictor pre1 is more effective than a single index for predicting prognosis. The combined prediction model of septic shock based on PCT, suPAR, APACHE II, and SOFA scores has higher predictive efficiency.

Local axonal morphology guides the topography of interneuron myelination in mouse and human neocortex

GABAergic fast-spiking parvalbumin-positive (PV) interneurons are frequently myelinated in the cerebral cortex. However, the factors governing the topography of cortical interneuron myelination remain incompletely understood. Here, we report that segmental myelination along neocortical interneuron axons is strongly predicted by the joint combination of interbranch distance and local axon caliber. Enlargement of PV+ interneurons increased axonal myelination, while reduced cell size led to decreased myelination. Next, we considered regular-spiking SOM+ cells, which normally have relatively shorter interbranch distances and thinner axon diameters than PV+ cells, and are rarely myelinated. Consistent with the importance of axonal morphology for guiding interneuron myelination, enlargement of SOM+ cell size dramatically increased the frequency of myelinated axonal segments. Lastly, we confirm that these findings also extend to human neocortex by quantifying interneuron axonal myelination from ex vivo surgical tissue. Together, these findings establish a predictive model of neocortical GABAergic interneuron myelination determined by local axonal morphology.

Characterization of the Blister Fluid Proteome for Pediatric Burn Classification

Blister fluid (BF) is a novel and viable research matrix for burn injury study, which can reflect both systemic and local microenvironmental responses. The protein abundance in BF from different burn severities were initially observed using a 2D SDS-PAGE approach. Subsequently, a quantitative data independent acquisition (DIA) method, SWATH, was employed to characterize the proteome of pediatric burn blister fluid. More than 600 proteins were quantitatively profiled in 87 BF samples from different pediatric burn patients. These data were correlated with clinically assessed burn depth and time until complete wound re-epithelialization through several different statistical analyses. Several proteins from these analyses exhibited significant abundance change between different burn depth or re-epithelialization groups, and can be considered as potential biomarker candidates. Further gene ontology (GO) enrichment analysis of the significant proteins revealed the most significant burn related biological processes (BP) that are altered with burn depth, including homeostasis and oxygen transport. However, for wounds with re-epithelialization times more or less than 21 days, the significant GO annotations were related to enzyme activity. This quantitative proteomics investigation of burn BF may enable objective classification of burn wound severity and assist with clinical decision-making. Data are available via ProteomeXchange with identifier PXD011102.

Partial Youden index and its inferences

In medical diagnostic research, medical tests with continuous values are widely employed to distinguish between diseased and non-diseased subjects. The diagnostic accuracy of a medical test can be assessed by using the receiver operating characteristic (ROC) curve of the test. To summarize the ROC curve and determine an optimal cut-off point for test results, the Youden index is commonly used. In particular, the Youden index is optimized over the entire range of values for sensitivity and specificity, which determine the ROC space. However in clinical practice, one may only be interested in the regions of the ROC curve that correspond to low false-positive rates or/and high sensitivities. In this paper, a new summary index for the ROC curve, called the "partial Youden index", is defined on regions of the ROC space in which sensitivity/specificity values are of practical interest. The traditional Youden index is a special case of the partial Youden index. Various parametric and non-parametric interval estimation methods are proposed for the partial Youden index. Extensive simulation studies are conducted to evaluate the finite sample performances of the proposed methods. A real example is used to illustrate the application of the new methods.

Examining the disconnect between psychometric models and clinical reality of posttraumatic stress disorder

There have been many factor analytic studies aimed at testing alternative latent structures of DSM-IV and DSM-5 posttraumatic stress disorder (PTSD) symptoms. The primary rationale for such studies is that determining the 'best' factor analytic model will result in better diagnoses if that structure is the basis for diagnostic decisions. However, there appears to be a disconnect between the factor analytic modelling and the diagnostic implications. In this study, we derived prevalence rates based on commonly reported models of PTSD, based on data from two clinical samples (N=434), and also assessed if the different models generated consistent risk estimates in relation to the effects of childhood maltreatment. We found that the different models produced different prevalence rates, ranging from 64.5% to 83.9%. Furthermore, we found that the relationship between childhood maltreatment and 'diagnosis' varied considerably depending upon which latent symptom profile was adopted. It is argued that, given the maturity of this area of research, factor analytic studies of PTSD should now include information on the diagnostic implications of their findings.

Relationship between tumor heterogeneity measured on FDG-PET/CT and pathological prognostic factors in invasive breast cancer

Background

There is currently little support to understand which pathological factors led to differences in tumor texture as measured from FDG PET/CT images. We studied whether tumor heterogeneity measured using texture analysis in FDG-PET/CT images is correlated with pathological prognostic factors in invasive breast cancer.

Methods

Fifty-four patients with locally advanced breast cancer who had an initial FDG-PET/CT were retrospectively included. In addition to SUVmax, three robust textural indices extracted from 3D matrices: High-Gray-level Run Emphasis (HGRE), Entropy and Homogeneity were studied. Univariate and multivariate logistic regression was used to identify PET parameters associated with poor prognosis pathological factors: hormone receptor negativity, presence of HER-2 and triple negative phenotype. Receiver operating characteristic (ROC) curves and the (AUC) analysis, and reclassification measures, were performed in order to evaluate the performance of combining texture analysis and SUVmax for characterizing breast tumors.

Results

Tumor heterogeneity, measured with HGRE, was higher in negative estrogen receptor (p = 0.039) and negative progesterone receptor tumors (p = 0.036), and in Scarff-Bloom-Richardson grade 3 tumors (p = 0.047). None of the PET indices could identify HER-2 positive tumors. Only SUVmax was positively correlated with Ki-67 (p<0.0004). Triple negative breast cancer (TNBC) exhibited higher SUVmax (Odd Ratio = 1.22, 95%CI [1.06–1.39],p = 0.004), lower Homogeneity (OR = 3.57[0.98–12.5],p = 0.05) and higher HGRE (OR = 8.06[1.88–34.51],p = 0.005) than non-TNBC. Multivariate analysis showed that HGRE remained associated with TNBC (OR = 5.27[1.12–1.38],p = 0.03) after adjustment for SUVmax. Combining SUVmax and HGRE yielded in higher area under the ROC curves (AUC) than SUVmax for identifying TNBC: AUC =  0.83 and 0.77, respectively. Probability of correct classification also increased in 77% (10/13) of TNBC and 71% (29/41) of non-TNBC (p = 0.003), when combining SUVmax and HGRE.

Conclusions

Tumor heterogeneity measured on FDG-PET/CT was higher in invasive breast cancer with poor prognosis pathological factors. Texture analysis might be used, in addition to SUVmax, as a new tool to assess invasive breast cancer aggressiveness.