Predicting Response to PD-1 Checkpoint Blockade Using Deep Learning Analysis of Imaging and Clinical Data
Condition: Non-small Cell Lung Cancer (NSCLC)
MeSH terms: Carcinoma, Non-Small-Cell Lung
Study type: Observational

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.
    

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