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

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

Study Overview

• Status: Completed
• Conditions: Non-small Cell Lung Cancer (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.

• Study Type: Observational
• Enrollment (Actual): 300

Contacts and Locations

Study Locations

• France
  • Nîmes, France, 30900
    • Jean-Paul BEREGI

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 100 years (ADULT, OLDER_ADULT)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

patients with non-small cell lung cancer (NSCLC), having undergone treatment with immunotherapy

Description

Inclusion Criteria:Patients between 18 and 100 years of age -

Exclusion Criteria: Patient under 18 years of age

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Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
developing a multi-omic classifier for predicting PD-1 response	Once sufficient patient data are accumulated, imaging data (both baseline and follow-up scans) will be annotated (segmented) to delineate lesions, lymph nodes, surrounding organs, etc…	during one month

Collaborators and Investigators

• Sponsor: Centre Hospitalier Universitaire de Nīmes
• Collaborators: MEDEXPRIM; GRATICULE

Study record dates

Study Major Dates

• Study Start (ACTUAL): January 31, 2021
• Primary Completion (ACTUAL): March 31, 2022
• Study Completion (ACTUAL): December 31, 2022

Study Registration Dates

• First Submitted: January 25, 2023
• First Submitted That Met QC Criteria: January 25, 2023
• First Posted (ACTUAL): February 3, 2023

Study Record Updates

• Last Update Posted (ACTUAL): February 3, 2023
• Last Update Submitted That Met QC Criteria: January 25, 2023
• Last Verified: January 1, 2023

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Respiratory Tract Diseases; Neoplasms; Lung Diseases; Neoplasms by Site; Respiratory Tract Neoplasms; Thoracic Neoplasms; Carcinoma, Bronchogenic; Bronchial Neoplasms; Lung Neoplasms; Carcinoma, Non-Small-Cell Lung
• Other Study ID Numbers: Local2021/JPB-01

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