Onc AI: Predicting Response to PD-1 Checkpoint Blockade Using Deep Learning Analysis of Imaging and Clinical Data
Study Details
Study Description
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
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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 Design
Outcome Measures
Primary Outcome Measures
- developing a multi-omic classifier for predicting PD-1 response [during one month]
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…
Eligibility Criteria
Criteria
Inclusion Criteria:Patients between 18 and 100 years of age -
Exclusion Criteria: Patient under 18 years of age
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Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Jean-Paul BEREGI | Nîmes | France | 30900 |
Sponsors and Collaborators
- Centre Hospitalier Universitaire de Nīmes
- MEDEXPRIM
- GRATICULE
Investigators
None specified.Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- Local2021/JPB-01