Clinical trial NCT05711914: Predicting Response to PD-1 Checkpoint Blockade Using Deep Learning Analysis of Imaging and Clinical Data
Official title: Predicting Response to PD-1 Checkpoint Blockade Using Deep Learning Analysis of Imaging and Clinical Data
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Non-small Cell Lung Cancer (NSCLC)
Lead sponsor: Centre Hospitalier Universitaire de Nīmes
Sponsor class: OTHER
Collaborators: name: MEDEXPRIM
name: GRATICULE
First posted: 2023-02-03
Enrollment: 300
Sex: ALL
Minimum age (years): 18
Maximum age (years): 100
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: patients with non-small cell lung cancer (NSCLC), having undergone treatment with immunotherapy

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.

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

Exclusion Criteria: Patient under 18 years of age

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Primary outcomes:
measure: developing a multi-omic classifier for predicting PD-1 response
timeFrame: during one month
Secondary outcomes:
Other outcomes:
Locations:
city: Nîmes
country: France
facility: Jean-Paul BEREGI
Study officials:
References: