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Characterization of the genomic and transcriptomic landscape of invasive non-mucinous lung adenocarcinoma based on IASLC grading.

BACKGROUND: The IASLC grading system has prognostic utility and potential therapeutic implications in invasive non-mucinous lung adenocarcinoma (LUAD), but the molecular basis underlying the grading spectrum remains unclear. METHODS: We performed whole-genome sequencing in 138 Chinese patients with invasive non-mucinous LUAD and RNA sequencing of 96 matched tumor-normal tissue pairs to systematically characterize the molecular features across grades, including coding driver events, mutational signatures, non-coding regulatory disruptions, and transcriptional programs. RESULTS: Compared with Grade 1-2 tumors, Grade 3 LUADs exhibited heightened invasive potential, manifested by more advanced stage, more frequent spread through air spaces, and independently worse survival. Grade 3 tumors had elevated tumor mutational burden and were enriched for alterations in genome maintenance and cell-cycle genes, including TP53, as well as genes implicated in DNA damage response, including ZFHX4. APOBEC-associated mutagenesis was selectively enriched in Grade 3 tumors independent of smoking status, consistent with an instability-associated phenotype. Recurrent non-coding regulatory disruptions affected lung lineage-defining genes, particularly surfactant-associated genes, and were correlated with reduced expression. Transcriptomic profiling revealed epithelial dedifferentiation, loss of pulmonary homeostatic programs, and activation of proliferative and stress-related pathways. Notably, MUC16 emerged as a convergent event linking genomic and transcriptional dysregulation, with coding mutations associated with higher expression and increased expression in Grade 3 tumors correlating with the proportion of high-grade histologic patterns. CONCLUSIONS: These findings provide a molecular framework for the IASLC grading spectrum and identify Grade 3 LUAD as a distinct instability-associated and dedifferentiated biological state.

IASLC grading

Genomic profiling of aggressive pathologic features in lung adenocarcinoma.

INTRODUCTION: Pathologic features involving LVI (lympho-vascular invasion), PNI (perineural invasion), STAS (spread through air spaces), and Grade 3 pattern (from the International Association for the Study of Lung Cancer grading system) are related to having an aggressive phenotype and linked to poor prognosis. However, few studies have conducted in-depth analyses of these features simultaneously with genomic profiling. METHODS: A total of 1559 sequencing of adenocarcinoma samples were included in the common driver mutations analysis, 1306 samples were brought into genomic mapping analysis. OncoSG's East Asian ancestry dataset was implemented for Tumor-Node-Metastasis-Biomarker (TNMB) classification and prognostic assessment. RESULTS: EGFR was more significantly prevalent in LVI negativity (P&#xa0;=&#xa0;0.021), STAS negativity (P&#xa0;=&#xa0;0.002), and moderate grade (P&#xa0;<&#xa0;0.001). ALK was significantly interrelated with LVI (P&#xa0;=&#xa0;0.028), STAS (P&#xa0;<&#xa0;0.001), and poor grade (P&#xa0;<&#xa0;0.001); ROS1 and STAS positivity (P&#xa0;=&#xa0;0.031), poor grade (P&#xa0;=&#xa0;0.016) were significantly related. KRAS (P&#xa0;=&#xa0;0.003) and BRAF-V600E (P&#xa0;=&#xa0;0.002) were only significantly intertwined with poor grade. Apart from common driver mutations, TP53, CHEK2, KEAP1, PTEN, RB1, NF1 were significantly enriched in LVI samples (P&#xa0;<&#xa0;0.05). TP53, PTEN, CTNNB1, HGF, NF1 were more prominent in STAS (P&#xa0;<&#xa0;0.01). TP53, LRP1B, NF1 were significantly more prevalent in Grade 3 pattern (P&#xa0;<&#xa0;0.001). The mixture of STK11, PTEN, and TOP2A generated by exclusive mutations may be a potential predictor of TNMB categorization towards survival. The HR of stage II compared I of TNMB was 2.28 (95&#xa0;% CI 1.36-3.86, P&#xa0;<&#xa0;0.001), while stage III compared II was 1.95 (95&#xa0;% CI 1.04-3.21, P&#xa0;=&#xa0;0.031). CONCLUSIONS: This analysis demonstrated the correlation of pathologic features with common driver mutations, key mutations and canonical oncogenic signaling pathways. The data highlighted the similarities and differences among these features horizontally, and provide new insights in TNMB classification and prognostic assessment.

Humans

IASLC Update on Classification of Pulmonary Neuroendocrine Neoplasms.

Since the publication of the 2021 WHO classification of thoracic tumors, our knowledge of pulmonary neuroendocrine neoplasms (NENs) has expanded significantly, particularly through the elucidation of molecular pathways and proposals to refine histopathologic classification. This expanded knowledge across all aspects of pulmonary NENs holds promise for more precise stratification of neuroendocrine tumors (NETs) and the potential development of novel, subtype-specific therapeutic strategies for all NENs. Based on our comprehensive review of the current pulmonary NEN landscape, our multidisciplinary expert panel has deliberated on the modification and updating of the 2021 classification, resulting in the proposal of a new pulmonary carcinoid/NET classification presented in this position paper, which incorporates the following three major points: (1) The proposed framework continues the shift from the traditional carcinoid terminology toward broader adoption of the "NET" nomenclature as found in other organ systems while retaining the term "carcinoid" as the primary diagnostic term to ensure clear communication with thoracic clinical providers. (2) Ki-67 has been incorporated as a diagnostic criterion, aligning with practices in other NET classifications. (3) There is formal recognition of the concept of "carcinoid/NET G3," a rare subset of lung carcinoids characterized by increased proliferative activity but with molecular features more aligned with pulmonary NETs than with high-grade neuroendocrine carcinomas. This position paper on the current knowledge of pulmonary NENs, including the proposed carcinoid/NET classification, will aid in accurate tumor categorization and guide treatment strategies.

Carcinoid tumor