It is an non-interventional retrospective observational study by using archived paraffin-embedded tumor specimens

non intervention

ACT Genomics ACT Genomics

Pei-Fang Chung

Chung

Genomic alterations have long been recognized as an important factor in tumor formation and drive tumor cell growth. However, the degree of genomic mutation (tumor mutation load, TMB) varies widely between tumors. In addition to gene mutations in tumor cells, the extent of immune cell infiltration in tumor tissues and the type and nature of immune cells (tumor microenvironment, TME) also play an important role in controlling tumor growth.

In recent years, more and more clinical studies have shown that the degree of genomic alteration (TMB) and tumor microenvironment (TME) have great potential in predicting a cancer patients' response to immunotherapy. Therefore, understanding the interaction and correlation between genomic alteration and tumor immune environment will not only deepen our understanding of tumor biology but also provide an important reference for developing immunotherapy treatment strategies for solid tumors.

This is a non-interventional, mono-centric retrospective study to be carried out in Chi Mei Medical Center (CMMC) in order to determine the association between the genomic alterations and gene expression level of immune-related genes in cancer. Samples in paraffin-embedded block of biopsies or surgical pieces (either primary tumor or metastases) will be analyzed. For each sample, clinically relevant data associated with treated cancer and needed for characterization of tumor microenvironment will be documented. No additional procedures besides those already used in the routine clinical practice will be applied to the patients. Treatment assignment will be done according to the current practice.

This trial is, through accessing to archival tumor materials, to establish the relationship between tumor mutation burden and tumor immune microenvironment for future immunotherapy strategy.

Archived paraffin-embedded tumor specimens

Analysis of Tumor Mutations and Tumor Microenvironment Using Archival Paraffin-embedded Tumor Specimens

To explore possible relationship between genomic alteration and expression of immune-related genes in solid tumor, tumor mutation burden (TMB) and PD-1/PD-L1 expression level are examined and such relationship will be calculated by Pearson's correlation coefficient.

To establish a cancer genomic profile for the Asian population, genomic alterations such as point mutations will be assessed by NGS-based ACTOnco assay.

To establish a cancer genomic profile for the Asian population, genomic alterations such as small insertions and deletions will be assessed by NGS-based ACTOnco assay.

To establish a cancer genomic profile for the Asian population, genomic alterations such as copy number alterations will be assessed by NGS-based ACTOnco assay.

To establish a cancer genomic profile for the Asian population, genomic alterations such as microsatellite instability will be assessed by NGS-based ACTOnco assay.

To establish an expression signature of tumor microenvironment (TME) in addition to PD-1 and PD-L1, \>90 immune-related genes will be assessed by ACTTME assay via quantitative PCR

To compare between calculated tumor mutation burden values (TMB, in mutations per megabase) resulting from different methodologies, concordance between two NGS-based assays will be expressed as positive predictive value (PPV) and negative predictive value (NPV).

Chi Mei Medical Hospital

ACT Genomics

Neoplasms

All Conditions

Digestive System Diseases

Symptoms and General Pathology

Carcinoma

Carcinoma, Hepatocellular

Recurrence

Liver Neoplasms

Neoplasms, Glandular and Epithelial

Neoplasms by Histologic Type

Adenocarcinoma

Gastrointestinal Neoplasms

Digestive System Neoplasms

Gastrointestinal Diseases

Liver Diseases

Disease Attributes

Data study with inclusion of patients and retrospective clinical data collection, combining :

* Proofreading by radiologist of the CT scan performed (within 2 months prior to surgical intervention)
* Proofreading by iBiopsy® of the CT scan performed (within 2 months prior to surgical intervention)
* Proofreading of tumor sample slides by pathologists
* Patients follow-up (imaging, clinical)
* Recurrence-free survival

Non intervention

Maïté LEWIN, Professor

Tumor recurrence, which occurs in 70% of patients with HCC within 5 years after hepatic resection, is a major cause of post-resection-death. This recurrence can be true recurrence (intrahepatic metastases), which occurs sooner than 2 years later, or it can be due to the development of de-novo tumors at least 2 years later. Despite this high rate of tumor recurrence, no anti-recurrence adjuvant therapies are currently recommended.

Imaging phenomics is the systematic, large scale extraction of imaging features for the characterization and classification of disease phenotypes. Combining imaging and tissue phenomics could be a solution to predict HCC recurrence. With the emergence of molecular therapies and immunotherapies, identifying patients with HCC at high risk of post-resection recurrence would help determine additional therapeutic and management strategies in clinical practice.

Hepatocellular carcinoma (HCC) is among the most lethal and prevalent cancers in the human population and it is now the third leading cause of cancer deaths worldwide, with over 500,000 people affected. Because of the high recurrence rate after curative hepatectomy, accurate prognostic assessment in HCC patients are quite important. With the emergence of molecular therapies and immunotherapies, the identification of patients at high or low risk for recurrence after hepatic resection would help determine additional therapeutic and management strategies in clinical practice. Although many immunohistochemical markers have been reported to have a prognostic value for HCC patients, there is no consensus on how these markers could add prognostic value to the clinical parameters.

In the initial step of biomarker discovery, no specific sample size is provided, however to test hypothesis, 100 patients are required.

This first study will potentially be followed by a second similar study promoted by the same investigators to increase the statistical power to improve the classification tool according to the patient's future.

Period covered by the data collection: 2011-2019 / Duration data collection: 1 year.

The primary endpoint will be built using machine learning method to obtain prediction of recurrence within 2 years. The Recurrence Free survival (RFS) within two years will be the reference outcome to evaluate the prognostic of the patients.

The secondary endpoint are following :

- A secondary endpoint which will be built using machine learning method to obtain prediction of recurrence after 2 years.

The Recurrence Free survival (RFS) after two years will be the reference outcome to evaluate the prognostic of the patients.

- A secondary endpoint will be the correlation between biomarker from CT scan and pathological biomarkers As the spectrum of HCC disease is very large, many patients to conduct conclusive validation studies for diagnostic and prognostic relevance need to be obtained.

Overall, each specific-read out endpoint will include a sample size calculation and - if appropriate - a power analysis specific to the objective of this study.

During training, phenotyping system performance assessment will be done to guide the calculation of the sample size for the validation.

Identification of Image Phenotypes to Predict Recurrence After Resection of Hepatocellular Carcinoma

Correlation between biomarker from CT scan and nodule size, nodule differentiation (grade OMS), nodule capsule, macroscopie invasion, microscopic vascular invasion, macrotrabecular sub-type, satellite nodule, staging.

The primary endpoint will be built using machine learning method to obtain prediction of recurrence within 2 years. The Recurrence Free survival (RFS) within two years will be the reference outcome to evaluate the prognostic of the patients.

A secondary endpoint which will be built using machine learning method to obtain prediction of recurrence after 2 years. The Recurrence Free survival (RFS) after two years will be the reference outcome to evaluate the prognostic of the patients.

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
Analysis of Tumor Mutations and Tumor Microenvironment Using Archival Paraffin-embedded Tumor Specimens	NCT03719222	Sequence Analys...	non interventio...	-	ACT Genomics
Identification of Image Phenotypes to Predict Recurrence After Resection of Hepatocellular Carcinoma	NCT05235490	CT Scans Prior ...	Non interventio...	18 Years -	Assistance Publique - Hôpitaux de Paris
Analysis of Tumor Mutations and Tumor Microenvironment Using Archival Paraffin-embedded Tumor Specimens	NCT03719222	Sequence Analys...	non interventio...	-	ACT Genomics
Analysis of Tumor Mutations and Tumor Microenvironment Using Archival Paraffin-embedded Tumor Specimens	NCT03719222	Sequence Analys...	non interventio...	-	ACT Genomics

Spots Global Cancer Trial Database for non intervention