Validation of the 9th edition of the TNM staging system for non-small cell lung cancer with lobectomy in stage IA-IIIA.

OBJECTIVES: The 9th edition of tumour-node-metastasis (TNM) staging for lung cancer was announced by Prof Hisao Asamura at the 2023 World Conference on Lung Cancer in Singapore. The purpose of this study was to externally validate and compare the latest staging of lung cancer. METHODS: We collected 19 193 patients with stage IA-IIIA non-small cell lung cancer (NSCLC) who underwent lobectomy from the Surveillance, Epidemiology and End Results database. Survival analysis by TNM stages was compared using the Kaplan-Meier method and further analysed using univariable and multivariable Cox regression analyses. Receiver operating characteristic curves were used to assess model accuracy, Akaike information criterion, Bayesian information criterion and consistency index were used to compare the prognostic, predictive ability between the current 8th and 9th edition TNM classification. RESULTS: The 9th edition of the TNM staging system can better distinguish between IB and IIA patients on the survival curve (P < 0.0001). In both univariable and multivariable regression analysis, the 9th edition of the TNM staging system can differentiate any 2 adjacent staging patients more evenly than the 8th edition. The 9th and the 8th edition TNM staging have similar predictive power and accuracy for the overall survival of patients with NSCLC [TNM 9th vs 8th, area under the curve: 62.4 vs 62.3; Akaike information criterion: 166 182.1 vs 166 131.6; Bayesian information criterion: 166 324.3 vs 166 273.8 and consistency index: 0.650 (0.003) vs 0.651(0.003)]. CONCLUSIONS: Our external validation demonstrates that the 9th edition of TNM staging for NSCLC is reasonable and valid. The 9th edition of TNM staging for NSCLC has near-identical prognostic accuracy to the 8th edition.

Is there a prognostic difference among stage I lung adenocarcinoma patients with different BRAF-mutation status?

BACKGROUND: The data of the prognostic role of V-Raf murine sarcoma viral oncogene homolog B1 (BRAF) mutations in early-stage lung adenocarcinoma (LUAD) patients is scarce. This study aimed to investigate the proportion, clinicopathological features, and prognostic significance of patients with stage I LUAD carrying BRAF mutations. METHODS: We collected 431 patients with pathological stage I LUAD from cBioPortal for Cancer Genomics and 1604 LUAD patients tested for BRAF V600E and epidermal growth factor receptor (EGFR) mutations from Shanghai Pulmonary Hospital. Survival curves were drawn by the Kaplan-Meier method and compared by log-rank test. Cox proportional hazard models, propensity-score matching (PSM), and overlap weighting (OW) were performed in this study. The primary endpoint was recurrence-free survival (RFS). RESULTS: The proportion of BRAF mutations was estimated at 5.6% in a Caucasian cohort. BRAF V600E mutations were detected in six (1.4%) patients in Caucasian populations and 16 (1.0%) patients in Chinese populations. Two BRAF V600E-mutant patients were detected to have concurrent EGFR mutations, one for 19-del and one for L858R. For pathological stage I LUAD patients, BRAF mutations were not significantly associated with worse RFS than wild-type BRAF patients (HR = 1.111; p = 0.885). After PSM and OW, similar results were presented (HR = 1.352; p = 0.742 and HR = 1.246; p = 0.764, respectively). BRAF V600E mutation status also lacked predictive significance for RFS (HR, 1.844; p = 0.226; HR = 1.144; p = 0.831 and HR = 1.466; p = 0.450, respectively). CONCLUSIONS: In this study, we demonstrated that BRAF status may not be capable of predicting prognosis in stage I LUAD patients. There is a need for more data to validate our findings.

Integration of machine learning for developing a prognostic signature related to programmed cell death in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) presents a significant global health burden, characterized by a heterogeneous molecular landscape and various genetic and epigenetic alterations. Programmed cell death (PCD) plays a critical role in CRC, offering potential targets for therapy by regulating cell elimination processes that can suppress tumor growth or trigger cancer cell resistance. Understanding the complex interplay between PCD mechanisms and CRC pathogenesis is crucial. This study aims to construct a PCD-related prognostic signature in CRC using machine learning integration, enhancing the precision of CRC prognosis prediction. METHOD: We retrieved expression data and clinical information from the Cancer Genome Atlas and Gene Expression Omnibus (GEO) datasets. Fifteen forms of PCD were identified, and corresponding gene sets were compiled. Machine learning algorithms, including Lasso, Ridge, Enet, StepCox, survivalSVM, CoxBoost, SuperPC, plsRcox, random survival forest (RSF), and gradient boosting machine, were integrated for model construction. The models were validated using six GEO datasets, and the programmed cell death score (PCDS) was established. Further, the model's effectiveness was compared with 109 transcriptome-based CRC prognostic models. RESULT: Our integrated model successfully identified differentially expressed PCD-related genes and stratified CRC samples into four subtypes with distinct prognostic implications. The optimal combination of machine learning models, RSF + Ridge, showed superior performance compared with traditional methods. The PCDS effectively stratified patients into high-risk and low-risk groups, with significant survival differences. Further analysis revealed the prognostic relevance of immune cell types and pathways associated with CRC subtypes. The model also identified hub genes and drug sensitivities relevant to CRC prognosis. CONCLUSION: The current study highlights the potential of integrating machine learning models to enhance the prediction of CRC prognosis. The developed prognostic signature, which is related to PCD, holds promise for personalized and effective therapeutic interventions in CRC.