Next Generation " Pre-clinical Model for Colorectal Cancer Metastases and Hepatocellular Carcinomas (BORG)

Next Generation " of Liver Derived-organoïd Biobank: Case of Colorectal Cancer Metastases and Hepatocellular Carcinomas

Recently, oncology has moved to a new clinical practice, more personalized, called Predictive Oncology (PO).

PO comes from our knowledge about tumor heterogeneity that implies that each disease, thus each patient, is unique. PO's goal is to identify and administrate the right treatment to the right patient.

For this, PO requires to go through 3 majors steps:

1. A good characterization of the tumor to identify candidates,
2. A well-established panel of drugs targeting the identified candidates,
3. A relevant model to functionally test these candidates.

The first point could easily be addressed with recent technologies that now allow the Next Generation Sequencing (NGS) and/or the simultaneous analysis of transcriptomic profiles from thousands of patients. The last two points have not been efficiently achieved so far, which prevents PO to be really efficient.

Indeed, even if NGS allows the identification of potential targets, the presence of a molecular candidate does not necessary means obligatory functional response.

The number of drugs approved by the Food and Drug Administration remains limited and most frequent targets in solid tumors (for ex. RAS, P53, MYC, RB1 ...) still do not have specific drugs approved in clinic.

Finally, available pre-clinical models still present many major inconvenient:

• Chimiogrammes on 2D cultures are not sufficiently relevant to be really predictive of the in vivo situation;
• Patient derived xenograft (PDX) are not adapted for clinical use because not all tumors graft and the time to develop a PDX is too long (several months), thus incompatible with the history of the disease (especially for most severe patients). Furthermore the host (NOD-SCID mouse) is immuno-depressed, preventing to objectively test antibodies-mediated drugs.

Recently, the 3D cell culture technology has proven its superiority to predict drug response over classical 2D chimiogrammes. It consists in growing "mini-tissues", or organoid-derived from tumor/healthy tissues, thanks to the amplification of stem cells contained within the sample. The generated organoids are personalized and biologically relevant (organoids are expend form the patient's stem cells which self-organized according to the architecture of the tissue they are originating from), they are genetically stable, their growth is compatible with patient's disease history (organoids grow in few weeks), easy and convenient to achieve, even from small biological material quantities (0.5< x < 1cm3), and they can be amplified, frozen and thawed on demand. Moreover, organoids can be made more complex with the addition of other cell types (fibroblasts, immune cells …). None of the actual available pre-clinical model regroups all these characteristics.

The constitution of a "next generation" biobank of liver samples (Metastases to the liver and Hepato Cellular Adenocarcinoma) will be very useful in the context of predictive oncology.

For this, a biopsy needs to be dissociated and grown in Matrigel™, in presence of a well-defined list of growth factors. Once the culture is established, organoids can be frozen then defrost on demand.

Our main objective is to evaluate the feasibility for building a biobank of liver-derived organoids, from liver metastases of colorectal cancers, hepatocellular adenoma and adenocarcinoma (waste tissues).

Applications related to organoids derived from tumors are quasi indefinite, from drug screening assays, tests for novel therapies or original drug combinations, to patients' stratifications or fundamental research.

In our case, we are interested in building this a biobank in the prospect of using it to build the "next generation of model for predictive oncology" to study liver-related cancers and related drugs testing. Briefly, we want to implement these organoids with cells from the microenvironment in order to makes the global model more pertinent for drug testing.

If successful, the generation of such biobank, including both tumor-derived organoids and healthy counterpart, could be really helpful for the scientific and medical community.

Study Overview

• Status: Completed
• Conditions: Colorectal Cancer Metastases and Hepatocellular Carcinomas
• Intervention / Treatment: Procedure: Tumor /metastases removal

Detailed Description

Recently, oncology has moved to a new clinical practice, more personalized, called Predictive Oncology (PO).

PO comes from our knowledge about tumor heterogeneity that implies that each disease, thus each patient, is unique. PO's goal is to identify and administrate the right treatment to the right patient.

For this, PO requires to go through 3 majors steps:

1. A good characterization of the tumor to identify candidates,
2. A well-established panel of drugs targeting the identified candidates,
3. A relevant model to functionally test these candidates.

The first point could easily be addressed with recent technologies that now allow the Next Generation Sequencing (NGS) and/or the simultaneous analysis of transcriptomic profiles from thousands of patients. The last two points have not been efficiently achieved so far, which prevents PO to be really efficient.

Indeed, even if NGS allows the identification of potential targets, the presence of a molecular candidate does not necessary means obligatory functional response.

The number of drugs approved by the Food and Drug Administration remains limited and most frequent targets in solid tumors (for ex. RAS, P53, MYC, RB1 ...) still do not have specific drugs approved in clinic.

Finally, available pre-clinical models still present many major inconvenient:

• Chimiogrammes on 2D cultures are not sufficiently relevant to be really predictive of the in vivo situation;
• Patient derived xenograft (PDX) are not adapted for clinical use because not all tumors graft and the time to develop a PDX is too long (several months), thus incompatible with the history of the disease (especially for most severe patients). Furthermore the host (NOD-SCID mouse) is immuno-depressed, preventing to objectively test antibodies-mediated drugs.

Recently, the 3D cell culture technology has proven its superiority to predict drug response over classical 2D chimiogrammes. It consists in growing "mini-tissues", or organoid-derived from tumor/healthy tissues, thanks to the amplification of stem cells contained within the sample. The generated organoids are personalized and biologically relevant (organoids are expend form the patient's stem cells which self-organized according to the architecture of the tissue they are originating from), they are genetically stable, their growth is compatible with patient's disease history (organoids grow in few weeks), easy and convenient to achieve, even from small biological material quantities (0.5< x < 1cm3), and they can be amplified, frozen and thawed on demand. Moreover, organoids can be made more complex with the addition of other cell types (fibroblasts, immune cells …). None of the actual available pre-clinical model regroups all these characteristics.

The constitution of a "next generation" biobank of liver samples (Metastases to the liver and Hepato Cellular Adenocarcinoma) will be very useful in the context of predictive oncology.

For this, a biopsy needs to be dissociated and grown in Matrigel™, in presence of a well-defined list of growth factors. Once the culture is established, organoids can be frozen then defrost on demand.

Our main objective is to evaluate the feasibility for building a biobank of liver-derived organoids, from liver metastases of colorectal cancers, hepatocellular adenoma and adenocarcinoma (waste tissues).

Applications related to organoids derived from tumors are quasi indefinite, from drug screening assays, tests for novel therapies or original drug combinations, to patients' stratifications or fundamental research.

In our case, we are interested in building this a biobank in the prospect of using it to build the "next generation of model for predictive oncology" to study liver-related cancers and related drugs testing. Briefly, we want to implement these organoids with cells from the microenvironment in order to makes the global model more pertinent for drug testing.

If successful, the generation of such biobank, including both tumor-derived organoids and healthy counterpart, could be really helpful for the scientific and medical community.

• Study Type: Observational
• Enrollment (Actual): 48

Contacts and Locations

Study Locations

• France
  • Marseille, France, 13915
    • AP-HM Hopital Nord

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Patient with a diagnosis of hepatocellular carcinomas or colorectal cancer metastases

Description

Inclusion Criteria:

• > 18 yo
• Patient with a diagnosis of hepatocellular carcinomas or colorectal cancer metastases
• Patient affiliated to the national healthcare program " sécurité sociale "
• Patient who has been informed and agreed to the proposed research program

Exclusion Criteria:

• Patients with more than one malignancy
• Patients receiving sustained immunosupressive treatments
• Patient with severe infection
• Patient under legal supervision, in situation of emergency or not able to express its consent

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Build the next generation biobank of liver-derived organoids	Grow and store organoids derived from liver biopsies (HC and CRC mets)	2 years

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Biobank of liver-derived organoids efficiency	Evaluate the efficicency for building a biobank of liver-derived organoids either from HCC or from CRC mets	2 years
Evaluate the clinical relevance of the generated organoids	Compared histologic & phenotypic traits between the tumor of origin and the corresponding organoids	2 years

Collaborators and Investigators

• Sponsor: Assistance Publique Hopitaux De Marseille

Study record dates

Study Major Dates

• Study Start (Actual): June 6, 2019
• Primary Completion (Actual): December 31, 2022
• Study Completion (Actual): June 30, 2023

Study Registration Dates

• First Submitted: May 17, 2022
• First Submitted That Met QC Criteria: May 17, 2022
• First Posted (Actual): May 20, 2022

Study Record Updates

• Last Update Posted (Actual): November 20, 2025
• Last Update Submitted That Met QC Criteria: November 17, 2025
• Last Verified: November 1, 2025

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Neoplasms by Site; Neoplasms; Neoplasms by Histologic Type; Digestive System Neoplasms; Digestive System Diseases; Liver Diseases; Neoplasms, Glandular and Epithelial; Adenocarcinoma; Liver Neoplasms; Carcinoma; Carcinoma, Hepatocellular; Investigative Techniques; Body Weights and Measures; Anthropometry; Tumor Burden
• Other Study ID Numbers: RCAPHM19_0407

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No