Current Insights and Future Directions of Multiomic and Spatial-Omic Analysis in Non-Small Cell Lung Cancer

Advancements in profiling technologies have deepened our understanding of cancer biology, particularly in non-small cell lung cancer (NSCLC). Genomic data have demonstrated profound clinical impact, enabling the rational design of therapeutics and achieving excellent clinical outcomes for patients with oncogene-addicted disease. While proteomics, transcriptomics, epigenetics, metabolomics, and microbiomics have also generated a wealth of data in NSCLC, their clinical impact is comparatively limited. The increasing use of multiomic profiling has the capacity to change this paradigm, offering new opportunities for improving patient care, particularly for those with non-oncogene-addicted (NOA) NSCLC. This review will summarize the current landscape of multiomic research in NSCLC, emphasizing the potential role in precision oncology. Each omic field is discussed in turn, describing potential clinical applications and challenges of each. In addition, the developing fields of spatial-omic and integrated multiomics, which are becoming increasingly important in understanding cancer biology, are discussed. NSCLC samples have been extensively profiled across different omic technologies, revealing a range of biomarkers associated with prognosis or response to therapy and potential drug targets, many of which are being investigated. While the patient groups analyzed differ between studies, most are performed on early-stage resection samples, and many studies do not stratify results by genomic status, limiting our understanding of NOA-NSCLC. In addition, while many studies independently analyze several omics, fewer use multiomic integration algorithms. Despite significant research into spatial-omic and multiomics in NSCLC, only genomics has significantly affected NSCLC clinical care, leaving an unmet need in NOA-NSCLC. However, nongenomic technologies have significant potential for precision oncology, particularly when used alongside multiomic integration to discover biomarkers or to identify future precision medicine targets.