Precision Treatment of Unresectable HCC Guided by Multi-omics Deep Learning Models

NCT ID: NCT06463444

Last Updated: 2024-06-17

Basic Information

• Recruitment Status: RECRUITING
• Clinical Phase: PHASE1
• Total Enrollment: 30 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2024-06-01
• Study Completion Date: 2026-06-30

Brief Summary

Surgery is the main curative treatment for hepatocellular carcinoma(HCC) patients, but 70%-80% of HCC patients are in the middle and advanced stages at the time of diagnosis and cannot be surgically resected. Local and systemic therapy are the main treatments for unresectable HCC. Two recent trials of HAIC combined with PD-1 monoclonal antibody and targeted therapy reported objective response rates (ORR) as high as 43.3% to 77.1%.

Detailed Description

Surgery is the main curative treatment for hepatocellular carcinoma(HCC) patients, but 70%-80% of HCC patients are in the middle and advanced stages at the time of diagnosis and cannot be surgically resected. Local and systemic therapy are the main treatments for unresectable HCC. Two recent trials of HAIC combined with PD-1 antibody and targeted therapy reported objective response rates (ORR) as high as 43.3% to 77.1%. However, the selection of patients who will benefit from the therapy remains a major challenge for the individualized treatment of HCC, which requires more accurate prediction of combination therapy.

With the advancement of sequencing technology, more and more fine-grained biological data can be obtained, including radiomics, pathology, genomics and immunomics. In recent years, the development of new methods such as graph neural network and multi-scale PHATE makes it possible to integrate multi-omics data. The use of artificial intelligence models to integrate multimodal data is an effective means to predict treatment response more accurately, which is helpful for more accurate and detailed classification of patients with different treatment outcomes, and to explore the internal mechanism of treatment response or not.

We constructed a multi-omics deep learning prediction model based on the retrospective cohort data from multiple medical centers (who received HAIC combined with target therapy and immunotherapy). The model could better distinguish the patients who would benefit from combination therapy, with an AUC of 0.86.

Therefore, the investigators conducted this multicenter, prospective, single-arm study to explore the response and prognosis of combination therapy in a population screened by the model and to evaluate the predictive power of the model.

Conditions

HCC; Precision Therapy

Study Design

• Allocation Method: NA
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: TREATMENT
• Blinding Strategy: NONE

Study Groups

Combined therapy group

All patients received HAIC combined with targeted therapy and immunotherapy

Group Type: EXPERIMENTAL

HAIC + Tislelizumab +lenvatinib

Intervention Type: DRUG

All patients were treated with HAIC combined with tislelizumab and lenvatinib.

1. HAIC was adopted of the FOFOLX 6 program, Folinic acid+5-fluorouracil+Oxaliplatin, 21 days between second HAIC treatments with a window of ±3 days.

2. Lenvatinib was started before HAIC treatment, discontinued during HAIC treatment, Oral 8 mg or 12mg once a day depending body weight.

3. First treatment with Tislelizumab was started 0-1 days after HAIC, 200 mg IV, followed by a second treatment 21 days later.

Interventions

HAIC + Tislelizumab +lenvatinib

All patients were treated with HAIC combined with tislelizumab and lenvatinib.

1. HAIC was adopted of the FOFOLX 6 program, Folinic acid+5-fluorouracil+Oxaliplatin, 21 days between second HAIC treatments with a window of ±3 days.

2. Lenvatinib was started before HAIC treatment, discontinued during HAIC treatment, Oral 8 mg or 12mg once a day depending body weight.

3. First treatment with Tislelizumab was started 0-1 days after HAIC, 200 mg IV, followed by a second treatment 21 days later.

Intervention Type: DRUG

Eligibility Criteria

Inclusion Criteria

1. Aged 18-75.

2. No previous local or systemic treatment for hepatocellular carcinoma.

3. Child-Pugh liver function score ≤ 7.

4. ECOG PS 0-1.

5. No serious organic diseases of the heart, lungs, brain, kidneys, etc.

6. Enhanced MRI determines that the tumor is technically unresectable.

7. Pathologic type of hepatocellular carcinoma confirmed by puncture biopsy.

8. Multimodal Deep Learning Model Screening Based on Pathology, Imaging, and Genetic Data Suggests Benefit from HAIC in Combination with Lenvatinib and PD-1 inhibitors.

Exclusion Criteria

1. Pregnant and lactating women.

2. Suffering from a condition that interferes with the absorption, distribution, metabolism, or clearance of the study drug (e.g., severe vomiting, chronic diarrhea, intestinal obstruction, impaired absorption, etc.).

3. A history of gastrointestinal bleeding within the previous 4 weeks or a definite predisposition to gastrointestinal bleeding (e.g., known locally active ulcer lesions, fecal occult blood ++ or more, or gastroscopy if persistent fecal occult blood +) that has not been targeted, or other conditions that may have caused gastrointestinal bleeding (e.g., severe fundoplication/esophageal varices), as determined by the investigator.

4. Active infection.

5. Other significant clinical and laboratory abnormalities that affect the safety evaluation.

6. Inability to follow the study protocol for treatment or follow up as scheduled.

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 75 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Chen Xiaoping

OTHER

Sponsor Role: lead

Responsible Party

Chen Xiaoping

Professor

Responsibility Role: SPONSOR_INVESTIGATOR

Principal Investigators

WanGuang Zhang

Role: PRINCIPAL_INVESTIGATOR

Tongji Hospital

Locations

Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology

Wuhan, Hubei, China

Site Status: RECRUITING

Countries

China

Central Contacts

WanGuang Zhang

Role: CONTACT

wgzhang@tjh.tjmu.edu.cn

13886195965

xiaoping Chen

Role: CONTACT

chenxpchenxp@163.com

027-83663400

Facility Contacts

WanGuang Zhang

Role: primary

wgzhang@tjh.tjmu.edu.cn

13886195965

Other Identifiers

Precision01

Identifier Type: -

Identifier Source: org_study_id