Plasma Free Amino Acid Profiling of Five Types of Cancer Patients and Its Application for Early Detection

BACKGROUND: Recently, rapid advances have been made in metabolomics-based, easy-to-use early cancer detection methods using blood samples. Among metabolites, profiling of plasma free amino acids (PFAAs) is a promising approach because PFAAs link all organ systems and have important roles in metabolism. Furthermore, PFAA profiles are known to be influenced by specific diseases, including cancers. Therefore, the purpose of the present study was to determine the characteristics of the PFAA profiles in cancer patients and the possibility of using this information for early detection. METHODS AND FINDINGS: Plasma samples were collected from approximately 200 patients from multiple institutes, each diagnosed with one of the following five types of cancer: lung, gastric, colorectal, breast, or prostate cancer. Patients were compared to gender- and age- matched controls also used in this study. The PFAA levels were measured using high-performance liquid chromatography (HPLC)-electrospray ionization (ESI)-mass spectrometry (MS). Univariate analysis revealed significant differences in the PFAA profiles between the controls and the patients with any of the five types of cancer listed above, even those with asymptomatic early-stage disease. Furthermore, multivariate analysis clearly discriminated the cancer patients from the controls in terms of the area under the receiver-operator characteristics curve (AUC of ROC >0.75 for each cancer), regardless of cancer stage. Because this study was designed as case-control study, further investigations, including model construction and validation using cohorts with larger sample sizes, are necessary to determine the usefulness of PFAA profiling. CONCLUSIONS: These findings suggest that PFAA profiling has great potential for improving cancer screening and diagnosis and understanding disease pathogenesis. PFAA profiles can also be used to determine various disease diagnoses from a single blood sample, which involves a relatively simple plasma assay and imposes a lower physical burden on subjects when compared to existing screening methods

Relationships between Viral Load and the Clinical Course of COVID-19

To predict the clinical outcome of coronavirus disease-2019 (COVID-19), we examined relationships among epidemiological data, viral load, and disease severity. We examined viral loads of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) in fatal (15 cases), symptomatic/survived (133 cases), and asymptomatic cases (138 cases) using reverse transcription quantitative real-time PCR (RT-qPCR). We examined 5768 nasopharyngeal swabs (NPS) and attempted to detect the SARS-CoV-2 genome using RT-qPCR. Among them, the viral genome was detected using the method for the 370 NPS samples with a positive rate of 6.4%. A comparison of each age showed that the fatal case was higher than the survived case and asymptomatic patients. Survived cases were older than asymptomatic patients. Notably, the viral load in the fatal cases was significantly higher than in symptomatic or asymptomatic cases (p < 0.05). These results suggested that a high viral load of the SARS-CoV-2 in elderly patients at an early stage of the disease results in a poor outcome. We should, therefore, intervene early to prevent a severe stage of the disease in such cases

Improvement of Severe COVID-19 in an Elderly Man by Sequential Use of Antiviral Drugs

Although a variety of existing drugs are being tested for patients with coronavirus disease 2019 (COVID-19), no efficacious treatment has been found so far, particularly for severe cases. We report successful recovery in an elderly patient with severe pneumonia requiring mechanical ventilation and extracorporeal membrane oxygenation (ECMO). Despite administration of multiple antiviral drugs, including lopinavir/ritonavir, chloroquine, and favipiravir, the patient’s condition did not improve. However, after administration of another antiviral drug, remdesivir, we were able to terminate invasive interventions, including ECMO, and subsequently obtained negative polymerase chain reaction results. Although further validation is needed, remdesivir might be effective in treating COVID-19

Detailed Evolutionary Analyses of the F Gene in the Respiratory Syncytial Virus Subgroup A

We performed evolution, phylodynamics, and reinfection-related antigenicity analyses of respiratory syncytial virus subgroup A (RSV-A) fusion (F) gene in globally collected strains (1465 strains) using authentic bioinformatics methods. The time-scaled evolutionary tree using the Bayesian Markov chain Monte Carlo method estimated that a common ancestor of the RSV-A, RSV-B, and bovine-RSV diverged at around 450 years ago, and RSV-A and RSV-B diverged around 250 years ago. Finally, the RSV-A F gene formed eight genotypes (GA1-GA7 and NA1) over the last 80 years. Phylodynamics of RSV-A F gene, including all genotype strains, increased twice in the 1990s and 2010s, while patterns of each RSV-A genotype were different. Phylogenetic distance analysis suggested that the genetic distances of the strains were relatively short (less than 0.05). No positive selection sites were estimated, while many negative selection sites were found. Moreover, the F protein 3D structure mapping and conformational epitope analysis implied that the conformational epitopes did not correspond to the neutralizing antibody binding sites of the F protein. These results suggested that the RSV-A F gene is relatively conserved, and mismatches between conformational epitopes and neutralizing antibody binding sites of the F protein are responsible for the virus reinfection

A Novel Multivariate Index for Pancreatic Cancer Detection Based On the Plasma Free Amino Acid Profile

<div><p>Background</p><p>The incidence of pancreatic cancer (PC) continues to increase in the world, while most patients are diagnosed with advanced stages and survive <12 months. This poor prognosis is attributable to difficulty of early detection. Here we developed and evaluated a multivariate index composed of plasma free amino acids (PFAAs) for early detection of PC.</p><p>Methods</p><p>We conducted a cross-sectional study in multi-institutions in Japan. Fasting plasma samples from PC patients (n = 360), chronic pancreatitis (CP) patients (n = 28), and healthy control (HC) subjects (n = 8372) without apparent cancers who were undergoing comprehensive medical examinations were collected. Concentrations of 19 PFAAs were measured by liquid chromatography–mass spectrometry. We generated an index consisting of the following six PFAAs: serine, asparagine, isoleucine, alanine, histidine, and tryptophan as variables for discrimination in a training set (120 PC and matching 600 HC) and evaluation in a validation set (240 PC, 28 CP, and 7772 HC).</p><p>Results</p><p>Several amino acid concentrations in plasma were significantly altered in PC. Plasma tryptophan and histidine concentrations in PC were particularly low, while serine was particularly higher than that of HC. The area under curve (AUC) based on receiver operating characteristic (ROC) curve analysis of the resulting index to discriminate PC from HC were 0.89 [95% confidence interval (CI), 0.86–0.93] in the training set. In the validation set, AUCs based on ROC curve analysis of the PFAA index were 0.86 (95% CI, 0.84–0.89) for all PC patients versus HC subjects, 0.81 (95% CI, 0.75–0.86) for PC patients from stage IIA to IIB versus HC subjects, and 0.87 (95% CI, 0.80–0.93) for all PC patients versus CP patients.</p><p>Conclusions</p><p>These findings suggest that the PFAA profile of PC was significantly different from that of HC. The PFAA index is a promising biomarker for screening and diagnosis of PC.</p></div

Correlation of PFAA index and other biomarkers (CA19-9, CEA, and elastase 1).

<p>The dotted line shows the cut-off of each biomarker or PFAA index. For data analysis, the upper normal limits of CA19-9, CEA, and elastase-1 were defined as 37 U/mL, 5 ng/dL, and 300 ng/dL, respectively. There were no significant correlations between each biomarker and the PFAA index.</p

Characteristics of PC patients and healthy controls.

<p>Mann–Whitney U-test (versus HC),</p><p>*<i>p</i> < 0.05;</p><p>***<i>p</i> < 0.001</p><p>^<b>a</b>: Cancer stages were determined according to the Union Internationalis Contra Cancrum (UICC) TNM Classification of Malignant Tumors, 6th Edition.</p><p>^† Chi-square tests to test the differences between PC and HC for categorical data of gender, BMI, and smoking history.</p><p>^‡ Chi-square tests to test the differences between PC and CP for categorical data of gender, BMI, and smoking history.</p><p>Characteristics of PC patients and healthy controls.</p