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Background: Patients with chronic kidney disease (CKD) are at high risk of myocardial infarction. Cardiac troponins are the biomarkers of choice for the diagnosis of acute myocardial infarction (AMI) without ST‐segment elevation (NSTE). In patients with CKD, troponin levels are often chronically elevated, which reduces their diagnostic utility when NSTE‐AMI is suspected. The aim of this study was to derive a diagnostic algorithm for serial troponin measurements in patients with CKD and suspected NSTE‐AMI.
Methods and Results: Two cohorts, 1494 patients from a prospective cohort study with high‐sensitivity troponin I (hs‐cTnI) measurements and 7059 cases from a clinical registry with high‐sensitivity troponin T (hs‐cTnT ) measurements, were analyzed. The prospective cohort comprised 280 CKD patients (estimated glomerular filtration rate <60 mL/min/1.73 m2). The registry data set contained 1581 CKD patients. In both cohorts, CKD patients were more likely to have adjudicated NSTE‐AMI than non‐CKD patients. The specificities of hs‐cTnI and hs‐cTnT to detect NSTE‐AMI were reduced with CKD (0.82 versus 0.91 for hs‐cTnI and 0.26 versus 0.73 for hs‐cTnT) but could be restored by applying optimized cutoffs to either the first or a second measurement after 3 hours. The best diagnostic performance was achieved with an algorithm that incorporates serial measurements and rules in or out AMI in 69% (hs‐cTnI) and 55% (hs‐cTnT) of CKD patients.
Conclusions: The diagnostic performance of high‐sensitivity cardiac troponins in patients with CKD with suspected NSTE‐AMI is improved by use of an algorithm based on admission troponin and dynamic changes in troponin concentration.
Background: Definitive chemoradiotherapy (CRT) is the primary treatment for non-metastatic anal squamous cell carcinoma (ASCC). Despite favorable treatment outcomes in general, failure rates up to 40% occur in locally advanced disease. For treatment escalation or de-escalation strategies easily assessable and valid biomarkers are needed.
Methods: We identified 125 patients with ASCC treated with standard CRT at our department. C-reactive protein (CRP) to albumin ratio (CAR) was calculated dividing baseline CRP by baseline albumin levels. We used maximally selected rank statistics to dichotomize patients to high and low risk groups. Associations of CAR with clinicopathologic parameters were evaluated and the prognostic impact was tested using univariate and multivariate cox regression analysis. In a subset of 78 patients, pretreatment tumor tissue was available and CD8+ tumor infiltrating lymphocytes (TILs) and p16INK4a status were scored by immunohistochemistry and correlated with CAR.
Results: Advanced T-stage and male gender were significantly associated with higher baseline CAR. Using the calculated cutoff of 0.117, a high baseline CAR was also associated with worse locoregional control (p = 0.002), distant metastasis-free survival (p = 0.01), disease-free survival (DFS, p = 0.002) and overall survival (OS, p < 0.001). A combined risk score incorporating N-stage and CAR, termed N-CAR score, was associated with worse outcome across all endpoints and in multivariate analysis independent of T-stage and Gender (HR 4.27, p = 0.003). In the subset of 78 patients, a strong infiltration with intratumoral CD8+ TIL was associated with a significantly lower CAR (p = 0.007). CAR is an easily accessible biomarker that is associated with DFS. Our study revealed a possible link between chronic systemic inflammation and an impaired intratumoral immune response.
Despite the implementation of consolidative immune checkpoint inhibition after definitive chemoradiotherapy (CRT), the prognosis for locally advanced non-small-cell lung cancer (NSCLC) remains poor. We assessed the impact of the C-reactive protein (CRP) to albumin ratio (CAR) as an inflammation-based prognostic score in patients with locally advanced NSCLC treated with CRT. We retrospectively identified and analyzed 52 patients with primary unresectable NSCLC (UICC Stage III) treated with definitive/neoadjuvant CRT between 2014 and 2019. CAR was calculated by dividing baseline CRP by baseline albumin levels and correlated with clinicopathologic parameters to evaluate prognostic impact. After dichotomizing patients by the median, univariate and multivariate Cox regression analyses were performed. An increased CAR was associated with advanced T-stage (p = 0.018) and poor performance status (p = 0.004). Patients with pre-therapeutic elevated CAR had significantly lower hemoglobin and higher leukocyte levels (hemoglobin p = 0.001, leukocytes p = 0.018). High baseline CAR was shown to be associated with worse local control (LPFS, p = 0.006), shorter progression-free survival (PFS, p = 0.038) and overall survival (OS, p = 0.022), but not distant metastasis-free survival (DMFS). Multivariate analysis confirmed an impaired outcome in patients with high CAR (LPFS: HR 3.562, 95% CI 1.294–9.802, p = 0.011). CAR is an easily available and independent prognostic marker after CRT in locally advanced NSCLC. CAR may be a useful biomarker for patient stratification to individualize treatment concepts.
Simple Summary: Penile cancer is a rare but aggressive malignancy characterized by rapid tumor growth as well as prompt metastasis in groin lymphatics. While localized diseases can be successfully cured by surgery in most cases, no truly effective treatment options have been established for metastatic diseases as of yet. In the current investigation, we assessed the value of selected members of the PI3K/mTOR/AKT pathway to serve as tumor markers or therapeutic targets for this disease. Higher expression of AKT was significantly more prevalent in high-grade tumors and independently predictive of the worse survival parameters, while increased expression of pmTOR was associated with an inferior prognosis as well. Treatment with the pan-AKT inhibitor capivasertib in PeCa cell lines induced significant reduction of cell viability and movement capacity. These findings might aid in the understanding of the molecular tumor background as well as development of novel treatment options for advanced penile cancer.
Abstract: The PI3K/mTOR/AKT pathway might represent an intriguing option for treatment of penile cancer (PeCa). We aimed to assess whether members of this pathway might serve as biomarkers and targets for systemic therapy. Tissue of primary cancer from treatment-naïve PeCa patients was used for tissue microarray analysis. Immunohistochemical staining was performed with antibodies against AKT, pAKT, mTOR, pmTOR, pS6, pPRAS, p4EBP1, S6K1 and pp70S6K. Protein expression was correlated with clinicopathological characteristics as well as overall survival (OS), disease-specific survival (DSS), recurrence-free survival (RFS) and metastasis-free survival (MFS). AKT inhibition was tested in two primarily established, treatment-naïve PeCa cell lines by treatment with capivasertib and analysis of cell viability and chemotaxis. A total of 76 patients surgically treated for invasive PeCa were included. Higher expression of AKT was significantly more prevalent in high-grade tumors and predictive of DSS and OS in the Kaplan–Meier analysis, and an independent predictor of worse OS and DSS in the multivariate regression analysis. Treatment with pan-AKT inhibitor capivasertib in PeCa cell lines induced a significant downregulation of both total AKT and pAKT as well as decreased cell viability and chemotaxis. Selected protein candidates of the mTOR/AKT signaling pathway demonstrate association with histological and survival parameters of PeCa patients, whereas AKT appears to be the most promising one.
Based on accumulating evidence of a role of lipid signaling in many physiological and pathophysiological processes including psychiatric diseases, the present data driven analysis was designed to gather information needed to develop a prospective biomarker, using a targeted lipidomics approach covering different lipid mediators. Using unsupervised methods of data structure detection, implemented as hierarchal clustering, emergent self-organizing maps of neuronal networks, and principal component analysis, a cluster structure was found in the input data space comprising plasma concentrations of d = 35 different lipid-markers of various classes acquired in n = 94 subjects with the clinical diagnoses depression, bipolar disorder, ADHD, dementia, or in healthy controls. The structure separated patients with dementia from the other clinical groups, indicating that dementia is associated with a distinct lipid mediator plasma concentrations pattern possibly providing a basis for a future biomarker. This hypothesis was subsequently assessed using supervised machine-learning methods, implemented as random forests or principal component analysis followed by computed ABC analysis used for feature selection, and as random forests, k-nearest neighbors, support vector machines, multilayer perceptron, and naïve Bayesian classifiers to estimate whether the selected lipid mediators provide sufficient information that the diagnosis of dementia can be established at a higher accuracy than by guessing. This succeeded using a set of d = 7 markers comprising GluCerC16:0, Cer24:0, Cer20:0, Cer16:0, Cer24:1, C16 sphinganine, and LacCerC16:0, at an accuracy of 77%. By contrast, using random lipid markers reduced the diagnostic accuracy to values of 65% or less, whereas training the algorithms with randomly permuted data was followed by complete failure to diagnose dementia, emphasizing that the selected lipid mediators were display a particular pattern in this disease possibly qualifying as biomarkers.