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Clinical Trials/NCT01957241
NCT01957241
Unknown
Not Applicable

Serum Biomarker Study for the Prognosis of Patients With Hepatocellular Carcinoma and Esophageal Cancer Undergoing Radiotherapy Using Multiplex Proximity Ligation Assay

National Taiwan University Hospital1 site in 1 country164 target enrollmentAugust 2011

Overview

Phase
Not Applicable
Intervention
Not specified
Conditions
Hepatocellular Carcinoma
Sponsor
National Taiwan University Hospital
Enrollment
164
Locations
1
Primary Endpoint
BIOMARKER PANEL SELECTION AND MODELING
Last Updated
12 years ago

Overview

Brief Summary

The primary goal of this study is to quantify the biomarkers of pre-radiation therapy(RT), during-RT, and post-RT serum samples from hepatocellular carcinoma (HCC) and esophageal cancer patients undergoing definitive or neoadjuvant RT, and to correlate them with tumor response, patterns of failure, survival outcome, and RT-related lung or liver toxicity. The secondary goal of this study is to set up the PLA platform in our institute for future biomarker test.

Detailed Description

There have been many biomarkers, such as angiogenesis factors and cytokines, related to cancer progression or microenvironment interaction. However, the commonly used enzyme-linked immunosorbent assay (ELISA) requires the certain volume of each sample for specific antigen or antibody. It may not be practically efficient to test a broad spectrum of biomarkers with limited volumes of serum from cancer patients. Proximity ligation assay (PLA), an established concept and platform requiring very little sample volume to quantitatively detect a variety of biomarkers, is being developed with multiplex versions of improved sensitivity and dynamic range by the Stanford group. From the three completed trials ("In vivo/vitro radiation-induced liver disease in HBV carrier(9261700196)", "Bystander effect study of radiation-induced viral hepatitis B reactivation(9261700196)", and "Pre- and post-chemoradiation blood RNA-microarray analysis to predict response and outcome of locally advanced esophageal squamous cell carcinoma(200805061R)") and one ongoing trial ("A phase I dose escalation trial of conformal hypofractionated radiation therapy for patients with hepatitis B virus-related Child A cirrhosis and hepatocellular carcinoma(200906051R)"), we have collected the pre-treatment and post-treatment serum samples of patients with hepatocellular carcinoma undergoing definitive radiotherapy and patients with esophageal cancer undergoing neoadjuvant chemoradiotherapy. Altered patterns of failure for post-radiotherapy hepatocellular carcinoma, especially intrahepatic and extrahepatic metastasis, and treatment response for post-chemoradiotherapy esophageal cancer upon esophagectomy, demands the effective biomarkers for the early prediction and appropriate management. The limited sample volumes form the obstacle of testing adequate number of biomarkers by ELISA. In this study we plan to collaborate with the Stanford group, to send and process these samples (100 μL each) to measure the dynamic changes of up to 56 or more biomarkers. We try to find the potential biomarkers correlating with treatment responses and patterns of failure for the future clinical practice, and wish to set up this viable PLA platform in our institute through this collaboration.

Registry
clinicaltrials.gov
Start Date
August 2011
End Date
December 2014
Last Updated
12 years ago
Study Type
Observational
Sex
All

Investigators

Responsible Party
Sponsor

Eligibility Criteria

Inclusion Criteria

  • Clinical diagnosis of locally advanced esophageal cancer or Hepatocellular Carcinoma, RT is indicated
  • Informed consent signed

Exclusion Criteria

  • not completed RT

Outcomes

Primary Outcomes

BIOMARKER PANEL SELECTION AND MODELING

Time Frame: 3 years

All statistical analyses completed in this study are executed using the R statistical computing environment. To select the discrete set of biomarkers used to fit models of HCC or esophageal cancer diagnosis, we use the R distribution of the Prediction Analysis of Microarrays statistical technique, PAMR. Logistic regression models are fit using the generalized linear model function in R.

Secondary Outcomes

  • SURVIVAL AND RT-RELATED TOXICITY ANALYSIS AND MODELING(3 years)

Study Sites (1)

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