Gupta, T.; Qawasmeh, T.; McCalla, S. Predictions of Programmed Cell Death Ligand 1 Blockade Therapy Success in Patients with Non-Small-Cell Lung Cancer. BioMedInformatics2023, 3, 1060-1070.
Gupta, T.; Qawasmeh, T.; McCalla, S. Predictions of Programmed Cell Death Ligand 1 Blockade Therapy Success in Patients with Non-Small-Cell Lung Cancer. BioMedInformatics 2023, 3, 1060-1070.
Gupta, T.; Qawasmeh, T.; McCalla, S. Predictions of Programmed Cell Death Ligand 1 Blockade Therapy Success in Patients with Non-Small-Cell Lung Cancer. BioMedInformatics2023, 3, 1060-1070.
Gupta, T.; Qawasmeh, T.; McCalla, S. Predictions of Programmed Cell Death Ligand 1 Blockade Therapy Success in Patients with Non-Small-Cell Lung Cancer. BioMedInformatics 2023, 3, 1060-1070.
Abstract
Lung cancer is the leading cause of cancer death worldwide, with non-small cell lung cancer (NSCLC) making up 80% of cases. Some genetic factors leading to NSCLC development include genetic mutations and PD-L1 expression. PD-L1 proteins are targeted in an NSCLC treatment called targeted gene therapy. However, this treatment is effective in a low percentage of patients. This study aimed to create machine learning models to use features like the number of mutations and the level of PD-L1 proteins in cancer cells, along with others, to predict whether a patient will receive clinical benefit from gene therapy treatment. This was done by downloading and merging datasets from cbioportal.org to create a sample size for the model. Features with high correlations to clinical benefit were identified. Three machine-learning models were created using these features to predict clinical benefits in patients, and each model’s accuracy was evaluated. All three models were accurate between 55-85%, with two of the models averaging an accuracy around 75%. Doctors can use these models to more accurately predict whether gene therapy treatment is likely to work in a patient before prescribing it to them.
Biology and Life Sciences, Immunology and Microbiology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
