Constructing a predictive model for lung cancer immunotherapy

Lead Investigator: Jing Yu, Sichuan university
Title of Proposal Research: Constructing a predictive model for lung cancer immunotherapy
Vivli Data Request: 8809
Funding Source: This study was supported by grants from the Key Technology Research and Development Program of the Sichuan Province (2022YFS0089).
Potential Conflicts of Interest: None

Summary of the Proposed Research:

Lung cancer is the most common malignancy in males and females, and the leading cause of cancer-related deaths. Its incidence continues to increase with the application of screening programs in the community, and the prognosis of patients is poor. Advances in therapies for lung cancer, especially immunotherapy, have improved survival and shifted the paradigm of treatment. Immunotherapy uses the immune system to fight cancer. It works by helping the immune system recognize and attack cancer cells. One type of immunotherapy is monoclonal antibody (MAB) treatment. This works by recognizing and finding specific proteins on cells. Each MAB recognizes one particular protein, and depending on the protein they are targeting, they work in different ways to kill cancer cells, or to stop them from growing. One type of MAB are immune checkpoint inhibitors (ICIs) which block proteins that stop the immune system from attacking cancer cells. Some ICIs have been found to have a durable response in clinical trials in lung cancer.

Various immune checkpoint inhibitors (ICIs), such as pembrolizumab, nivolumab, and ipilimumab, are widely used in the first-line treatment of patients with advanced/metastatic (where the cancer has spread to other parts of the body) lung cancer. However, only a limited fraction of patients benefit from ICIs, even when combined with other therapies such as chemotherapy.

There is an urgent need to explore potential biomarkers for optimizing patient selection to enhance the response rate. Biomarkers are molecules found in blood, other body fluids, or tissues that can indicate how a patient is responding to treatment. This project will analyze the differences in the data from patients who responded and who did not respond to immunotherapy treatment. This information will be used to create a model for predicting lung cancer immunotherapy response.

Requested Studies:

Integration of tumor extrinsic and intrinsic features associates with immunotherapy response in non-small cell lung cancer
Data Contributor: Tempus Labs, Inc.
Study ID: T21.02
Sponsor ID: T21.02