NCT06463444

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

Precision Treatment of Unresectable Liver Cancer Based on Multi-omics Deep Learning Model: a Multi-center Prospective Single-arm Study

• Status: Recruiting
• Phase: Phase 1
• Study type: Interventional
• Enrollment: 30 (estimated)
• Sponsor: Chen Xiaoping · Academic / Other
• Sex: All
• Age: 18 Years – 75 Years
• Healthy volunteers: Not accepted

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

Interventions

Type	Name	Description
DRUG	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.

Timeline

• Start date: 2024-06-01
• Primary completion: 2025-06-30
• Completion: 2026-06-30
• First posted: 2024-06-17
• Last updated: 2024-06-17

Locations

1 site across 1 country: China