Next-Generation Cancer Phenomics: A Transformative Approach to Unraveling Lung Cancer Complexity and Advancing Precision Medicine.

Abstract

Lung cancer remains one of the leading causes of cancer-related deaths globally, with its complexity driven by intricate and intertwined genetic, epigenetic, and environmental factors. Despite advances in genomics, transcriptomics, and proteomics, understanding the phenotypic diversity of lung cancer has lagged behind. Next-generation phenomics, which integrates high-throughput phenotypic data with multiomics approaches and digital technologies such as artificial intelligence (AI), offers a transformative strategy for unraveling the complexity of lung cancer. This approach leverages advanced imaging, single-cell technologies, and AI to capture dynamic phenotypic variations at cellular, tissue, and whole organism levels and in ways resolved in temporal and spatial contexts. By mapping the high-throughput and spatially and temporally resolved phenotypic profiles onto molecular alterations, next-generation phenomics provides deeper insights into the tumor microenvironment, cancer heterogeneity, and drug efficacy, safety, and resistance mechanisms. Furthermore, integrating phenotypic data with genomic and proteomic networks allows for the identification of novel biomarkers and therapeutic targets in ways informed by biological structure and function, fostering precision medicine in lung cancer treatment. This expert review examines and places into context the current advances in next-generation phenomics and its potential to redefine lung cancer diagnosis, prognosis, and therapy. It highlights the emerging role of AI and machine learning in analyzing complex phenotypic datasets, enabling personalized therapeutic interventions. Ultimately, next-generation phenomics holds the promise of bridging the gap between molecular alterations and clinical and population health outcomes, providing a holistic understanding of lung cancer biology that could revolutionize its management and improve patient survival rates.