PLA for HCC and Esophageal ca Serum

• Conditions: Hepatocellular Carcinoma; Esophageal Cancer

• Registration Number: NCT01957241
• Lead Sponsor: National Taiwan University Hospital

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.

Study Timeline

• Study Start: 2011-08
• Study First Submitted: 2013-09-24
• Study First Submitted QC: 2013-09-30
• Study First Posted: 2013-10-08
• Status Verified: 2014-04
• Last Update Submitted: 2014-04-15
• Last Update Posted: 2014-04-16
• Primary Completion: 2014-12
• Study Completion: 2014-12

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: All
• Target Recruitment: 164

Inclusion Criteria

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

Exclusion Criteria

• not completed RT

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
BIOMARKER PANEL SELECTION AND MODELING	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 Outcome Measures

Name	Time	Method
SURVIVAL AND RT-RELATED TOXICITY ANALYSIS AND MODELING	3 years	Survival data are fit to a right-censored model using the Survival function in the R statistical computing environment. Univariate and multivariate Cox proportional hazards models are fit onto survival data using the coxph function. Hazard ratios are calculated as the ratios of risk by the increase or decrease of 1 log2 PLA unit (2-fold increase or decrease in serum concentration of a biomarker). Lung or liver toxicity is graded by Common Toxicity Criteria version 3.0. Grade of toxicity is defined as the categorical variable and is correlated with the ratios of risk by the increase or decrease of 1 log2 PLA unit.

Trial Locations

Locations (1)

National Taiwan University Hospital; 🇨🇳 Taipei, Taiwan