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

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%.

Study Overview

• Status: Recruiting
• Conditions: HCC; Precision Therapy
• Intervention / Treatment: Drug: HAIC + Tislelizumab +lenvatinib

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.

• Study Type: Interventional
• Enrollment (Estimated): 30
• Phase: Phase 1

Contacts and Locations

• Study Contact: Name: Wanguang Zhang; Phone Number: 13886195965; Email: wgzhang@tjh.tjmu.edu.cn
• Study Contact Backup: Name: xiaoping Chen; Phone Number: 027-83663400; Email: chenxpchenxp@163.com

Study Locations

• China
  • Hubei
    • Wuhan, Hubei, China
      • Recruiting
      • Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
      • Contact: Wanguang Zhang; Phone Number: 13886195965; Email: wgzhang@tjh.tjmu.edu.cn

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

Description

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.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Treatment
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

1

Arms and Interventions

Participant Group / Arm

Intervention / Treatment

Experimental: Combined therapy group; All patients received HAIC combined with targeted therapy and immunotherapy

Drug: HAIC + Tislelizumab +lenvatinib; All patients were treated with HAIC combined with tislelizumab and lenvatinib.; 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.; Lenvatinib was started before HAIC treatment, discontinued during HAIC treatment, Oral 8 mg or 12mg once a day depending body weight.; First treatment with Tislelizumab was started 0-1 days after HAIC, 200 mg IV, followed by a second treatment 21 days later.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Objective response rate	Objective response rate(ORR) was defined as the sum of cases with complete response (CR) and partial response (PR) which assessed by the mRESIST criteria.	From the time of enrollment until disease progression, death, or the end of the study,assessed up to 60 months.

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Overall survival	OS is defined as the time from enrollment to death from any cause.	From date of enrollment until the date of death from any cause, assessed up to 60 months.
Safety Assessment	Any adverse event during treatment that is incompatible with the therapeutic purpose of the medication.The incidence and severity of adverse events and serious adverse events as assessed by CTCAE v5.0.	Baseline up to study termination, assessed up to 12 months.

Collaborators and Investigators

• Sponsor: Chen Xiaoping
• Investigators: Principal Investigator: Wanguang Zhang, Tongji Hospital

Study record dates

Study Major Dates

• Study Start (Actual): June 1, 2024
• Primary Completion (Estimated): June 30, 2025
• Study Completion (Estimated): June 30, 2026

Study Registration Dates

• First Submitted: May 19, 2024
• First Submitted That Met QC Criteria: June 12, 2024
• First Posted (Actual): June 17, 2024

Study Record Updates

• Last Update Posted (Actual): June 17, 2024
• Last Update Submitted That Met QC Criteria: June 12, 2024
• Last Verified: June 1, 2024

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Molecular Mechanisms of Pharmacological Action; Enzyme Inhibitors; Antineoplastic Agents; Antineoplastic Agents, Immunological; Protein Kinase Inhibitors; Lenvatinib; Tislelizumab
• Other Study ID Numbers: Precision01

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