scholarly journals Urine biomarker: novel approach to hepatocellular carcinoma screening

2020 ◽  
Author(s):  
Amy K Kim ◽  
James P. Hamilton ◽  
Selena Y. Lin ◽  
Ting-Tsung Chang ◽  
Hie-Won Hann ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cross Validation ◽  
Learning Algorithm ◽  
Early Stage ◽  
High Risk Patient ◽  
Circulating Tumor Dna ◽  
Urine Samples ◽  
Detection Rates ◽  
Non Invasive ◽  
Fold Cross Validation

ABSTRACTBackground & AimsContinued limitations in hepatocellular carcinoma (HCC) screening have led to late diagnosis with poor survival, despite well-defined high-risk patient populations. Our aim is to develop a non-invasive urine circulating tumor DNA (ctDNA) biomarker panel for HCC screening to aid in early detection.MethodsCandidate ctDNA biomarkers was prescreened in urine samples obtained from HCC, cirrhosis, and hepatitis patients. Then, 609 patient urine samples with HCC, cirrhosis, or chronic hepatitis B were collected from five academic medical centers and evaluated by serum alpha feto-protein (AFP) and urine ctDNA panel using logistic regression, a Two-Step machine learning algorithm, and iterated 10-fold cross-validation.ResultsMutated TP53, and methylated RASSF1a and GSTP1, were selected for the urine ctDNA panel. The sensitivity of AFP-alone (9.8 ng/mL cut-off) to detect HCC was 71% by Two-Step. The combination of ctDNA and AFP increased the sensitivity to 81% at a specificity of 90%. The AUROC for the combination of ctDNA and AFP vs. AFP-alone were 0.925 (95% CI, 0.924-0.925) and 0.877 (95% CI, 0.876-0.877), respectively. Notably, among the patients with AFP <20 ng/mL, the combination panel correctly identified 64% of HCC cases. The panel performed superiorly to AFP-alone in early-stage HCC (BCLC A) with 80% sensitivity and 90% specificity. In an iterated 10-fold cross-validation analysis, the AUROC for the combination panel was 0.898 (95% CI, 0.895-0.901).ConclusionsThe combination of urine ctDNA and serum AFP can increase HCC detection rates including in those patients with low-AFP. Given the ease of collection, a urine ctDNA panel could be a potential non-invasive HCC screening test.

scholarly journals Current status of ctDNA in precision oncology for hepatocellular carcinoma

2021 ◽  
Vol 40 (1) ◽  
Author(s):  
Yan Li ◽  
Yuanyuan Zheng ◽  
Liwei Wu ◽  
Jingjing Li ◽  
Jie Ji ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cancer Progression ◽  
Diagnostic Technique ◽  
Response Assessment ◽  
Selection Response ◽  
Circulating Tumor Dna ◽  
Current Status ◽  
Precision Oncology ◽  
Tumor Biopsy ◽  
Non Invasive

AbstractThe conventional method used to obtain a tumor biopsy for hepatocellular carcinoma (HCC) is invasive and does not evaluate dynamic cancer progression or assess tumor heterogeneity. It is thus imperative to create a novel non-invasive diagnostic technique for improvement in cancer screening, diagnosis, treatment selection, response assessment, and predicting prognosis for HCC. Circulating tumor DNA (ctDNA) is a non-invasive liquid biopsy method that reveals cancer-specific genetic and epigenetic aberrations. Owing to the development of technology in next-generation sequencing and PCR-based assays, the detection and quantification of ctDNA have greatly improved. In this publication, we provide an overview of current technologies used to detect ctDNA, the ctDNA markers utilized, and recent advances regarding the multiple clinical applications in the field of precision medicine for HCC.

scholarly journals Detection of KRAS Mutations in Circulating Tumor DNA by Digital PCR in Early Stages of Pancreatic Cancer

2016 ◽  
Vol 62 (11) ◽  
pp. 1482-1491 ◽  
Author(s):  
Nora Brychta ◽  
Thomas Krahn ◽  
Oliver von Ahsen
Keyword(s):  
Pancreatic Cancer ◽  
Early Stage ◽  
Digital Pcr ◽  
Circulating Tumor Dna ◽  
Surgical Removal ◽  
Plasma Samples ◽  
Kras Mutations ◽  
Detection Rates ◽  
Early Stages ◽  
Tumor Dna

Abstract BACKGROUND Since surgical removal remains the only cure for pancreatic cancer, early detection is of utmost importance. Circulating biomarkers have potential as diagnostic tool for pancreatic cancer, which typically causes clinical symptoms only in advanced stage. Because of their high prevalence in pancreatic cancer, KRAS proto-oncogene, GTPase [KRAS (previous name: Kirsten rat sarcoma viral oncogene homolog)] mutations may be used to identify tumor-derived circulating plasma DNA. Here we tested the diagnostic sensitivity of chip based digital PCR for the detection of KRAS mutations in circulating tumor DNA (ctDNA) in early stage pancreatic cancer. METHODS We analyzed matched plasma (2 mL) and tumor samples from 50 patients with pancreatic cancer. Early stages (I and II) were predominant (41/50) in this cohort. DNA was extracted from tumor and plasma samples and tested for the common codon 12 mutations G12D, G12V, and G12C by chip-based digital PCR. RESULTS We identified KRAS mutations in 72% of the tumors. 44% of the tumors were positive for G12D, 20% for G12V, and 10% for G12C. One tumor was positive for G12D and G12V. Analysis of the mutations in matched plasma samples revealed detection rates of 36% for G12D, 50% for G12V, and 0% for G12C. The detection appeared to be correlated with total number of tumor cells in the primary tumor. No KRAS mutations were detected in 20 samples of healthy control plasma. CONCLUSIONS Our results support further evaluation of tumor specific mutations as early diagnostic biomarkers using plasma samples as liquid biopsy.

Virus induced hepatocellular carcinoma (HCC) and protein biomarkers

2021 ◽  
Author(s):  
Hamza Abbas Jaffari ◽  
Sumaira Mazhar
Keyword(s):  
Hepatocellular Carcinoma ◽  
Early Stage ◽  
Imaging Techniques ◽  
Disease Diagnosis ◽  
Protein Biomarkers ◽  
Early Disease ◽  
Detection Systems ◽  
Non Invasive ◽  
Viral Hepatitis B ◽  
Current Article

Hepatocellular carcinoma (HCC) is a standout amongst the most widely recognized cancers around the world, and just as the alcoholic liver disease it is also progressed by extreme viral hepatitis B or C. At the early stage of the disease, numerous patients are asymptomatic consequently late diagnosis of HCC occurs resulting in expensive surgical resection or transplantation. On the basis of the alpha fetoprotein (AFP) estimation, combined with the ultrasound and other sensitive imaging techniques used, the non-invasive detection systems are available. For early disease diagnosis and its use in the effective treatment of HCC patients, the identification of HCC biomarkers has provided a breakthrough utilizing the molecular genetics and proteomics. In the current article, most recent reports on the protein biomarkers of HBV or HCV-related HCC and their co-evolutionary association with liver cancer are reviewed.

scholarly journals Gut microbiome analysis as a tool towards targeted non-invasive biomarkers for early hepatocellular carcinoma

Gut ◽  
2018 ◽  
Vol 68 (6) ◽  
pp. 1014-1023 ◽  
Author(s):  
Zhigang Ren ◽  
Ang Li ◽  
Jianwen Jiang ◽  
Lin Zhou ◽  
Zujiang Yu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Gut Microbiome ◽  
Cross Validation ◽  
Area Under The Curve ◽  
Northwest China ◽  
Central China ◽  
Advanced Hcc ◽  
Non Invasive ◽  
Faecal Samples ◽  
Early Hcc

ObjectiveTo characterise gut microbiome in patients with hepatocellular carcinoma (HCC) and evaluate the potential of microbiome as non-invasive biomarkers for HCC.DesignWe collected 486 faecal samples from East China, Central China and Northwest China prospectively and finally 419 samples completed Miseq sequencing. We characterised gut microbiome, identified microbial markers and constructed HCC classifier in 75 early HCC, 40 cirrhosis and 75 healthy controls. We validated the results in 56 controls, 30 early HCC and 45 advanced HCC. We further verified diagnosis potential in 18 HCC from Xinjiang and 80 HCC from Zhengzhou.ResultsFaecal microbial diversity was increased from cirrhosis to early HCC with cirrhosis. Phylum Actinobacteria was increased in early HCC versus cirrhosis. Correspondingly, 13 genera including Gemmiger and Parabacteroides were enriched in early HCC versus cirrhosis. Butyrate-producing genera were decreased, while genera producing-lipopolysaccharide were increased in early HCC versus controls. The optimal 30 microbial markers were identified through a fivefold cross-validation on a random forest model and achieved an area under the curve of 80.64% between 75 early HCC and 105 non-HCC samples. Notably, gut microbial markers validated strong diagnosis potential for early HCC and even advanced HCC. Importantly, microbial markers successfully achieved a cross-region validation of HCC from Northwest China and Central China.ConclusionsThis study is the first to characterise gut microbiome in patients with HCC and to report the successful diagnosis model establishment and cross-region validation of microbial markers for HCC. Gut microbiota-targeted biomarkers represent potential non-invasive tools for early diagnosis of HCC.

Genomic characterization of HBV-infected hepatocellular carcinoma patients using circulating tumor DNA.

2020 ◽  
Vol 38 (4_suppl) ◽  
pp. 575-575
Author(s):  
Daniel Lin ◽  
Zhong Ye ◽  
Chun Wang ◽  
Qiang Wei ◽  
Li Bingshan ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cancer Patients ◽  
Early Stage ◽  
Circulating Tumor Dna ◽  
University Hospital ◽  
Clinical Factors ◽  
Characteristic Analysis ◽  
Genomic Aberrations ◽  
Mutant Genes ◽  
Tumor Dna

575 Background: Hepatocellular carcinoma (HCC) is a leading cause of mortality, with Hepatitis B virus (HBV) infection as a dominant etiology. Surgery or ablation may be curative for early-stage HCC. Thus, effective detection strategies are needed. We investigated genomic aberrations in circulating tumor DNA (ctDNA) as a potential diagnostic marker of HCC in HBV-infected patients. Methods: We identified early stage (BCLC 0-A) HCC cases (n = 21) and cancer-free controls (n = 15) from a cohort of Asian patients with HBV, undergoing surveillance at Thomas Jefferson University Hospital between 2013-2017. Blood samples were collected. Circulating cell-free DNA was isolated from plasma and assayed by capture-based next-generation sequencing of a targeted panel of 23 genes implicated in HCC pathogenesis. Sequencing data analysis and somatic mutation identification were conducted using a computational pipeline. Using area under the curve (AUC) in receiver operating characteristic analysis, we evaluated gene alterations and clinical factors (age, gender, cirrhosis) in an exploratory early detection HCC model. Results: Mutant ARID1A, ATM, CDKN2A, CTNNB1, ERBB2, TP53 genes were increased in HCC cases relative to non-cancer patients (85.7% vs 53.3%, P = 0.058; 42.9% vs 6.7%, P = 0.025; 38.1% vs 6.7%, P = 0.051; 42.9% vs 0%, P = 0.005; 52.4% vs 13.3%, P = 0.016; 100% vs 66.7%, P = 0.008, respectively). HCC patients had higher prevalence of cirrhosis than controls (90.5% vs. 60%, P = 0.046). Using the 6 mutant genes alone, the AUC for discriminating HCC from non-cancer patients was 0.827 (95% confidence interval [CI]: 0.701-0.953), which was greater than the AUC for discriminating cirrhosis from non-cirrhosis (0.531). When the 6 mutant genes were combined with clinical factors, the AUC of the exploratory HCC detection model increased to 0.914 ( P= 0.045). Conclusions: We identified 6 genomic aberrations in ctDNA that were more prevalent in HCC patients compared with non-cancer patients. Combining these alterations with clinical factors may identify HCC in HBV-infected patients at an early stage. These findings warrant further validation in future studies.

scholarly journals Assessing PD-L1 Expression Level via Preoperative MRI in HCC Based on Integrating Deep Learning and Radiomics Features

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1875
Author(s):  
Yuchi Tian ◽  
Temitope Emmanuel Komolafe ◽  
Jian Zheng ◽  
Guofeng Zhou ◽  
Tao Chen ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Deep Learning ◽  
Cross Validation ◽  
Predictive Ability ◽  
Feature Space ◽  
Integrated Model ◽  
Expression Level ◽  
Deep Learning Features ◽  
Expression Status ◽  
Fold Cross Validation

To assess if quantitative integrated deep learning and radiomics features can predict the PD-L1 expression level in preoperative MRI of hepatocellular carcinoma (HCC) patients. The data in this study consist of 103 hepatocellular carcinoma patients who received immunotherapy in a single center. These patients were divided into a high PD-L1 expression group (30 patients) and a low PD-L1 expression group (73 patients). Both radiomics and deep learning features were extracted from their MRI sequence of T2-WI, which were merged into an integrative feature space for machine learning for the prediction of PD-L1 expression. The five-fold cross-validation was adopted to validate the performance of the model, while the AUC was used to assess the predictive ability of the model. Based on the five-fold cross-validation, the integrated model achieved the best prediction performance, with an AUC score of 0.897 ± 0.084, followed by the deep learning-based model with an AUC of 0.852 ± 0.043 then the radiomics-based model with AUC of 0.794 ± 0.035. The feature set integrating radiomics and deep learning features is more effective in predicting PD-L1 expression level than only one feature type. The integrated model can achieve fast and accurate prediction of PD-L1 expression status in preoperative MRI of HCC patients.

scholarly journals Traditional Chinese Medicine (TCM) Diagnosis Model Building Based on Multi-label Classification

2018 ◽  
Vol 232 ◽  
pp. 02026
Author(s):  
Lu Zhou ◽  
Guang-geng Li ◽  
Yu-mei Zhou ◽  
Dan Yin ◽  
Yan Sun ◽  
...  
Keyword(s):  
Cross Validation ◽  
Model Building ◽  
Learning Algorithm ◽  
Training Data ◽  
Average Precision ◽  
Test Dataset ◽  
Average Accuracy ◽  
Good Potential ◽  
Diagnosis Model ◽  
Fold Cross Validation

In the study, we propose a TCM diagnosis model that can be used for multi-label classification and give clear diagnosis, as well as the basis for diagnosis and differentiation when the symptoms correspond to multiple diseases or syndromes. The implementation of the model is divided into three steps. Firstly, choose the machine learning algorithm to train the TCM diagnosis model. The features of the training data are symptoms and the labels are diseases or syndromes. Secondly, give the number α (α>1, α∈Z+), the model will output the diagnoses with the top α highest probability according to the input symptoms as candidate diagnoses. Finally, the rules of differential diagnosis are designed to determine which candidate diagnoses should be reserved, thereby complete the multi-label classification. In our test dataset, by 10-fold cross-validation, the average accuracy of the single label classification was 0.882; the average precision was 0.974; the average recall was 1.000; the average f1 score was 0.967; the average accuracy of the multi-label classification was 0.706; the average micro precision was 0.934; the average micro recall was 0.941 and the average hamming loss was 0.060. Through the test we can know that this model had a good potential for auxiliary decision making in clinical diagnosis and treatment.

A Binary Classifier for the Prediction of EC Numbers of Enzymes

2019 ◽  
Vol 16 (5) ◽  
pp. 383-391 ◽  
Author(s):  
Hao Cui ◽  
Lei Chen
Keyword(s):  
Cross Validation ◽  
Enzyme Commission ◽  
Matthews Correlation Coefficient ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Binary Classifier ◽  
Two Samples ◽  
Functional Classes ◽  
Fold Cross Validation

Background: Identification of Enzyme Commission (EC) number of enzymes is quite important for understanding the metabolic processes that produce enough energy to sustain life. Previous studies mainly focused on predicting six main functional classes or sub-functional classes, i.e., the first two digits of the EC number. Objective: In this study, a binary classifier was proposed to identify the full EC number (four digits) of enzymes. Methods: Enzymes and their known EC numbers were paired as positive samples and negative samples were randomly produced that were as many as positive samples. The associations between any two samples were evaluated by integrating the linkages between enzymes and EC numbers. The classic machining learning algorithm, Support Vector Machine (SVM), was adopted as the prediction engine. Results: The five-fold cross-validation test on five datasets indicated that the overall accuracy, Matthews correlation coefficient and F1-measure were about 0.786, 0.576 and 0.771, respectively, suggesting the utility of the proposed classifier. In addition, the effectiveness of the classifier was elaborated by comparing it with other classifiers that were based on other classic machine learning algorithms. Conclusion: The proposed classifier was quite effective for prediction of EC number of enzymes and was specially designed for dealing with the problem addressed in this study by testing it on five datasets containing randomly produced samples.

Non-invasive diagnosis and tracking of tumor evolution by targeted sequencing of circulating tumor DNA in metastatic pancreatic cancer patients.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e15769-e15769
Author(s):  
Thomas Seufferlein ◽  
Andreas W. Berger ◽  
Daniel Schwerdel ◽  
Thomas Jens Ettrich ◽  
Stefan A. Schmidt ◽  
...  
Keyword(s):  
Tumor Tissue ◽  
Early Stage ◽  
Therapy Monitoring ◽  
Circulating Tumor Dna ◽  
Ductal Adenocarcinoma ◽  
Tumor Evolution ◽  
Imaging Data ◽  
Non Invasive ◽  
Outcome Monitoring ◽  
Tumor Dna

e15769 Background: Treatment of stage IV pancreatic ductal adenocarcinoma (PDAC) has made substantial progress over the last years, therapy monitoring still is at an early stage. This could be substantially supported by tools that allow to establish and monitor the molecular setup of the tumor even during treatment. In particular, non-invasive approaches are desirable. Characterization of circulating tumor DNA (ctDNA) may help to achieve this goal. Methods: We analyzed a cohort of 20 patients with histologically confirmed metastatic PDAC (mPDAC) prior to and during palliative treatment including disease progression. ctDNA and corresponding tumor tissue were analyzed by targeted NGS and droplet digital PCR for the 7 most frequently mutated genes in PDAC ( TP53, SMAD4, CDKN2A, KRAS, APC, ATM, FBXW7). Findings were correlated with clinical and imaging data to establish its prognostic and predictive value. Results: ctDNA was analyzed at baseline prior to initiation of the respective line of treatment. Mutations in either of the genes examined were detectable in 15/20 patients (75%). Tissue-blood concordance was 80% in therapy naïve patients. 96% of mutations in ctDNA of therapy naïve patients were in KRAS and/or TP53. The combined mutated allele frequencies (CMAF) of theese 2 genes significantly decreased (p = 0.0173) during therapy and increased at progression (p = 0.0145) across all treatment lines. By sequential ctDNA analyses we detected a change in the mutational landscape compared to the respective baseline ctDNA status in 7/11 patients during 1st line, in 3/7 patients during 2nd line and 2/2 patients during 3rdline treatment. In therapy naïve patients, the decline of the CMAF during therapy significantly correlated with progression-free survival (p = 0.0013). Conclusions: Molecular genotyping of ctDNA in mPDAC patients proved to be feasible and there was a high concordance between tumor tissue and ctDNA. The molecular genotype changed significantly during treatment and changes correlated with outcome. Monitoring of ctDNA may enable to adapt therapeutic strategies to the specific molecular changes present at a certain time during treatment of mPDAC.

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