The impact of artificial intelligence on a multi-omics approach toward predictive biomarkers for non-small cell lung cancer

Abstract

Over the last four decades, lung cancer has been the leading cause of death in the United States. Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, and historically, treatment consists of surgical resection, chemotherapy, and/or radiotherapy. Over the past decade, targeted immunotherapy has improved overall survival and treatment response. However, immunotherapy is expensive, and only select patients respond to immunotherapy. Recently, there has been much interest in using biomarkers to better identify and predict which patients will respond to therapy. There is much hope that the combined use of artificial intelligence (AI) and omics-based technology will provide enhanced capability to predict response to immunotherapy in patients with NSCLC. We performed a literature review and summarized the various approaches in which AI has been integrated with genomics, radiomics, pathomics, metabolomics, immunogenomics, and breathomics to better understand the tumor immune microenvironment and predict response to immunotherapy.

Keywords

AI / NSCLC / machine learning / biomarker / lung cancer

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