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

Study Overview
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Brief Summary
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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
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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.

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

Conditions
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Non-small Cell Lung Cancer (NSCLC)

Participation Criteria
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Eligibility Criteria
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Inclusion Criteria:Patients between 18 and 100 years of age - Exclusion Criteria: Patient under 18 years of age -

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 100 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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What is the study measuring?
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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 |