Predicting Response to PD-1 Checkpoint Blockade Using Deep Learning Analysis of Imaging and Clinical Data

NCT ID: NCT05711914

Last Updated: 2023-02-03

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 300 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2021-01-31
• Study Completion Date: 2022-12-31

Brief Summary

Immunotherapy has transformed cancer treatment with the PD-1 class of checkpoint inhibitors - pembrolizumab and nivolumab -- demonstrating durable responses in Stage IV metastatic tumors such as non-small cell lung cancer and melanoma. Despite these numerous successes, PD-1/PD-L1 checkpoint blockade therapies do have a number of shortcomings.

Many approaches to predict response to PD-1/PD-L1 checkpoint therapy have been investigated with limited success. Recent efforts exploring the utility of quantitative imaging biomarkers to predict response to PD-[L]1 immunotherapy have shown promise. The purpose of this retrospective multicenter study is to develop a multi-omic classifier to predict response to PD-1/PD-L1 checkpoint blockade for mutation negative (EGFR, ALK and ROS1) NSCLC

Detailed Description

Recent Phase III studies have demonstrated the effectiveness of atezolizumab (PD-L1) in metastatic triple-negative breast cancer [3] and small cell lung cancer, while the standard of care for Stage III non-small cell lung cancer has changed with positive results of the PACIFIC Phase III study, where durvalumab (PD-L1) administered after chemoradiation showed a significant increase in overall survival.

Low response rates, generally in the 15% to 20% range in most diseases when used as a single agent, high therapy cost globally ($150,000 or more per year in the U.S) and serious immune-mediated adverse events, particularly when PD-1/PD-L1 inhibitors are combined with the CTLA-4 inhibitors (ipilimumab). Unpredictable and low patient response rates coupled with high drugs costs and serious toxicities can significantly burden healthcare systems, third-party payers and patients. Clearly, diagnostic tools to stratify patients according to response likelihood are necessary as PD-[L]1 checkpoint inhibitors continue to gain adoption.

The standard-of-care biomarker is an immunohistochemistry (IHC) test that measures levels of the PD-L1 protein expressed in tumor samples. Tumor mutational burden, presence of Tumor-Infiltrating Lymphocytes and inflammatory cytokines are being explored in multiple clinical trials involving PD-(L)1 often in combination with additional immuno-oncology (IO) therapies In such an approach, a non-invasive imaging scan can provide insight and information on the patient's entire tumor burden rather than a sample of a subset of lesions (as provided by biopsy or serum-based assays). When diagnostic images that depict all treatable lesions are further analyzed with computational techniques such as machine-learning and artificial intelligence, resulting in the identification of relevant imaging biomarkers, an accurate overall assessment of patient response to PD-[L]1 therapy may be attainable.

Conditions

Non-small Cell Lung Cancer (NSCLC)

Study Design

• Observational Model Type: OTHER
• Study Time Perspective: RETROSPECTIVE

Eligibility Criteria

Exclusion Criteria

-

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 100 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

MEDEXPRIM

UNKNOWN

Sponsor Role: collaborator

GRATICULE

UNKNOWN

Sponsor Role: collaborator

Centre Hospitalier Universitaire de Nīmes

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

Jean-Paul BEREGI

Nîmes, , France

Countries

France

Other Identifiers

Local2021/JPB-01

Identifier Type: -

Identifier Source: org_study_id