Clinical trial NCT06342414: An Exosome-Based Liquid Biopsy for the Differential Diagnosis of Primary Liver Cancer
Official title: An Exosome-Based Liquid Biopsy for the Differential Diagnosis of Primary Liver Cancer
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Hepatocellular Carcinoma
Intrahepatic Cholangiocarcinoma
Cholangiocarcinoma
Primary Liver Cancer
Primary Liver Carcinoma
Hepatic Cancer
Hepatic Carcinoma
Keywords: Exosome
micro RNA
Differential Diagnosis
Lead sponsor: City of Hope Medical Center
Sponsor class: OTHER
First posted: 2024-04-02
Enrollment: 400
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_ONLY
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Individuals who were diagnosed with either intrahepatic cholangiocarcinoma or hepatocellular carcinoma (case-case design for a differential diagnosis study)

Brief summary: It is sometimes difficult to precisely understand whether a primary liver cancer is a hepatocellular carcinoma or a cholangiocarcinoma. The researchers will develop and validate a liquid biopsy, based on exosomal content analysis and powered by machine learning, to help clinicians differentiate these two cancers before surgery.

Detailed description: Primary liver cancers (PLCs) encompass a diverse group of malignancies originating from the liver, collectively ranking as the third leading cause of cancer-related mortality worldwide in 2020. Among PLCs, intrahepatic cholangiocarcinoma (ICC) and hepatocellular carcinoma (HCC) represent the most predominant subtypes. Despite their collective grouping as PLCs, ICC and HCC patients exhibit distinct etiologies, pathologies, and clinical characteristics, necessitating different treatment approaches. Accurate differentiation between ICC and HCC is paramount to optimize patient outcomes and guide personalized treatment decisions. However, a definitive diagnosis is often obtained only after the pathological review of the resected neoplastic tissue, which requires invasive tumor sampling and poses risks of complications such as hemorrhage and tumor cell seeding. Consequently, there is a pressing clinical need to develop noninvasive diagnostic approaches to achieve an accurate differential diagnosis for patients with these distinct forms of PLCs.

This study involves the development and validation of a liquid biopsy, assessing circulating exosomal microRNAs (exo-miRNA) for indirect sampling of tumor tissue in the bloodstream. The researchers intend to harness machine learning and bioinformatics to create a cost-efficient, non-invasive, clinic-friendly assay with high sensitivity and specificity, aiding the differential diagnosis between ICC and HCC.

The researchers intend to do so in three phases:

1. To perform comprehensive small RNA-Seq from exo-miRNA from patients with ICC and HCC.
2. To develop and train a differential diagnosis panel based on advanced machine-learning models to obtain a final differential diagnosis biomarker.
3. To validate the findings in an independent cohort of ICC and HCC.

In summary, this proposal promises to improve patient care and help clinicians perform a more reliable differential diagnosis between ICC and HCC in patients with primary liver cancer.

Eligibility criteria: Inclusion Criteria:

* A histologically confirmed diagnosis of hepatocellular carcinoma
* A histologically confirmed diagnosis of intrahepatic cholangiocarcinoma
* Received standard diagnostic and staging procedures as per local guidelines
* Availability of at least one blood-derived sample, drawn before receiving any curative-intent treatment

Exclusion Criteria:

* Lack of or inability to provide informed consent
* Synchronous hepatocellular carcinoma and intrahepatic cholangiocarcinoma
* Primary liver cancer other than hepatocellular carcinoma or intrahepatic cholangiocarcinoma
* Secondary liver cancer

Primary outcomes:
measure: Sensitivity
timeFrame: Through study completion, an average of 1 year
Secondary outcomes:
measure: Specificity
timeFrame: Through study completion, an average of 1 year
measure: Proportion of correct predictions (true positives and true negatives) among the total number of cases (i.e., accuracy)
timeFrame: Through study completion, an average of 1 year
Other outcomes:
Locations:
city: Duarte
country: United States
facility: City of Hope Medical Center
state: California
city: Fukuoka
country: Japan
facility: Graduate School of Medical Sciences, Kyushu University
city: Kumamoto
country: Japan
facility: Graduate School of Medical Sciences, Kumamoto University
city: Sapporo
country: Japan
facility: Hokkaido University Graduate School of Medicine
city: Tokushima
country: Japan
facility: Tokushima University
Study officials:
affiliation: City of Hope Medical Center
name: Ajay Goel, PhD
role: PRINCIPAL_INVESTIGATOR
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