Abandoning the Notion of Non-Small Cell Lung Cancer

Lung cancers are classified as small-cell (SCLC) or non-small cell (NSCLC). Such distinction reflects the different clinical presentation, disease course, and therapeutic options of the two subgroups. However, recent advances question the reason for categorizing together heterogeneous NSCLC subtypes such as adenocarcinomas (LUADs) and squamous cell carcinomas (LUSCs). Experimental evidence indicates that LUADs and LUSCs are sharply distinct at the transcriptomic level. Distinct genetic drivers, control networks, and prognostic profiles were identified in the two tumor subgroups. Therapeutic clinical trials in NSCLC indicated differential LUAD versus LUSC response to chemotherapy, kinase mutation-targeted treatments, and immune checkpoint inhibitors. LUAD and LUSC thus appear to be vastly distinct diseases at the molecular, pathological, and clinical level. Non-small cell lung cancers (NSCLCs) represent 85% of lung tumors. NSCLCs encompass multiple cancer types, such as adenocarcinomas (LUADs), squamous cell cancers (LUSCs), and large cell cancers. Among them, LUADs and LUSCs are the largest NSCLC subgroups. LUADs and LUSCs appear sharply distinct at the transcriptomic level, as well as for cellular control networks. LUADs show distinct genetic drivers and divergent prognostic profiles versus LUSCs. Therapeutic clinical trials in NSCLC indicate differential LUAD versus LUSC response to treatments. Hence, LUAD and LUSC appear to be vastly distinct diseases at the molecular, pathological, and clinical level. Abandoning the notion of NSCLC may critically help in developing novel, more effective subtype-specific molecular alteration-targeted therapeutic procedures. Non-small cell lung cancers (NSCLCs) represent 85% of lung tumors. NSCLCs encompass multiple cancer types, such as adenocarcinomas (LUADs), squamous cell cancers (LUSCs), and large cell cancers. Among them, LUADs and LUSCs are the largest NSCLC subgroups. LUADs and LUSCs appear sharply distinct at the transcriptomic level, as well as for cellular control networks. LUADs show distinct genetic drivers and divergent prognostic profiles versus LUSCs. Therapeutic clinical trials in NSCLC indicate differential LUAD versus LUSC response to treatments. Hence, LUAD and LUSC appear to be vastly distinct diseases at the molecular, pathological, and clinical level. Abandoning the notion of NSCLC may critically help in developing novel, more effective subtype-specific molecular alteration-targeted therapeutic procedures. body weight divided by the square of body height. The BMI quantifies the amount of different tissue components (muscle, fat, and bone), and categorize that person as underweight (<18.5 kg/m2), normal weight (18.5–25 kg/m2), overweight (25–30 kg/m2), or obese (>30 kg/m2). signal transduction cascades through which individual genes exert their effects. Control pathways are nonlinear and converge into networks of multiple, intertwined signaling paths. Key components of such networks are represented as nodes. Node–node interactions are represented by connecting lines. consolidates knowledge on disease origin, pathogenetic mechanism, natural history, and response to therapy. This body of knowledge is utilized to classify diseases as separate entities. the ratio of the frequencies of adverse events in the two subgroups under comparison. procedure for detecting antigens in tissue sections through antibody binding. Antibody-bound enzymes, such as horseradish peroxidase or alkaline phosphatase, are used to catalyze a color-producing reaction, which can be visualized and quantified under the microscope. disease relapse curves, which indicate the time of any adverse event and compute the remaining cases as a percentage of patients that remain alive or disease-free at any given time. Kaplan–Meier curves depict cancer biological history as a cascade of disease events over time. specific genetic changes or protein/mRNA biomarkers can show associations with distinct cancer groups or disease severity. The intensity of such association quantifies their impact on disease prognosis. the ensemble of all RNAs transcribed by the genome in a specific tissue or cell type. Transcriptome analysis, whether by RNA-seq or DNA array hybridization, thus provides quantitative details on the transcription of all expressed genes. This information is utilized to infer gene function and gene expression regulation.