Khaw, Chuen Ryan; (2025) Tumour Growth Kinetics and Genomics In Screen-Detected Lung Cancer. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Lung cancer screening with low-dose computed tomography (LDCT) reduces mortality by enabling early detection. However, challenges persist, including the overdiagnosis of indolent cancers and the undertreatment of potentially aggressive cancers. Despite complete resection, five-year survival rates for stage I non-small cell lung cancer (NSCLC) can be as low as 73%, raising questions about whether adjuvant therapies or closer postoperative surveillance are warranted. Current management strategies primarily rely on size-based thresholds from single timepoint measurements to guide decisions, particularly in early-stage disease. Emerging evidence suggests that incorporating radiological growth rate from serial CT scans may provide deeper insights into tumour behaviour and prognosis. This thesis combines tumour growth rate tracking from serial CT scans in the SUMMIT study with molecular data from the ASCENT study to investigate the clinical implications and molecular mechanisms driving variable tumour growth rates. Data from this thesis establish mass as a robust metric for tracking tumour growth rate and demonstrates that most screen-detected lung cancers follow an exponential growth model. Growth rates are evaluated for their clinical utility in predicting progression to invasive adenocarcinoma, with decision curve analysis highlighting their value as an adjunct to existing size-based thresholds. Growth rates have significant prognostic value, with fast- growing tumours associated with aggressive histological features and worse outcomes, independent of stage. Incorporating growth rates into tumour staging improves the prediction of survival in Stage I NSCLC. The molecular landscape of fast-growing tumours is distinctly characterised by the concurrent upregulation of proliferative pathways and those associated with invasion and metastasis, suggesting an aggressive phenotype shaped by stronger selection for driver mutations. Immune profiling reveals significant heterogeneity, highlighting potential mechanisms by which these tumours evade immune surveillance. The findings of this thesis offer valuable insights for advancing diagnostic and prognostic risk assessment in screen-detected lung cancer.

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