Lung cancer is the leading cause of cancer related death worldwide; despite recent treatment developments survival rates remain poor and are closely related to the patient’s clinical stage. Even among patients with early-stage lung cancer, which is amenable to surgical resection, prognosis is highly variable; some go on to live disease-free for many years whereas others quickly recur. Although post-operative chemotherapy is available it has associated morbidities and it is unclear which patients would benefit; therefore, there is a need for more effective stratification of patients. The adenocarcinoma sub-type of lung cancer is known to be morphologically heterogeneous however the majority of observed growth patterns, assessed by light microscopy, can be characterised into one of five formations: lepidic, papillary, acinar, solid and micropapillary. The morphology of each tumour has been proposed as a marker of prognosis and several studies have published a link between the most prevalent growth pattern and prognosis; suggesting those with predominantly solid or micropapillary tumours to have the least favourable outcomes. Indeed, it is now recommended that the proportion of each growth pattern and the predominant growth pattern should be reported for all resected lung adenocarcinomas; although no differential treatments have been recommended based on this assessment. The aim of this study was to determine whether combining the analysis of clinicopathological; morphological; and candidate protein, molecular genetic and transcriptomic characteristics in a single cohort of 208 early-stage, resected, adenocarcinomas with clinical follow-up could be used to identify a subset of patients at high risk of recurrence. Comprehensive morphological analysis was carried out including the presence, proportion and number of individual growth patterns; the predominant growth pattern as well as features previously associated with tumour grade (the presence of large numbers of mitotic figures, apoptotic bodies, inflammatory cells, prominent nucleoli, pleomorphic tumour cells, dyscohesive tumour cells and large amounts of necrosis and scar tissue within the tumour). In addition, gene expression was assessed using a panel of 31 cell-cycle related genes, EGFR and KRAS mutation status was determined, and EGFR and TTF1 protein expression investigated. In this study the predominant growth pattern defined by histopathology showed no ability to identify a group of patients with a poorer prognosis either in univariable or multivariable analysis. Univariable analysis identified nodal status [hazard ratio of N1 compared to N0 was 2.16 (95% CI 1.48 to 3.16, p< 0.0005)], clinical stage [hazard ratios of stage IIa and IIb compared to stage Ia were 3.15 (95% CI 1.73 to 5.73, p< 0.0005) and 2.22 (95% CI 1.10 to 4.48, p= 0.025) respectively], the presence of a significant amount of the papillary growth pattern [the hazard ratio of those with less than 8.5% papillary pattern was 0.657 (95% CI 0.44 to 0.98, p= 0.035)], and overall tumour grade score (including an assessment of necrosis, mitosis, apoptosis, nucleoli, scar tissue and inflammatory cells) [hazard ratio 1.71 (95% CI 1.14 to 2.56, p= 0.008)] as significantly associated with prognosis. Multivariable analysis using Cox’s proportional hazards model identified clinical stage (p< 0.0005), the presence of a significant amount of the papillary growth pattern (p= 0.048) and the presence of large numbers of mitotic figures (p=0.029) and apoptotic bodies (p= 0.015) as independently associated with disease specific survival; although after correction for type I errors only clinical stage remained significantly associated with prognosis with patients with stage Ia disease having a significantly better outcomes [hazard ratio 0.418 (95% CI 0.20 to 0.86)]. Classification and regression tree analysis (CART) was used to further explore the data and to develop decision trees for the prognostication of early-stage lung adenocarcinoma patients. Receiver operating characteristic analysis based on 5- year survival showed a minimal improvement in the area under the curve between a model utilizing currently available clinicopathologic characteristics only [nodal status and lesion size, (area under the curve 0.704, 95% CI 0.631 to 0.777)] and one including growth pattern characteristics [area under the curve 0.725, 95% CI 0.654 to 0.796]. The greatest improvement in prognostic accuracy was observed when gene expression analysis was included in the analysis [area under the curve 0.749, 95% CI 0.673 to 0.825]; however even this showed very little impact compared to routinely used clinicopathologic variables. This analysis suggests that the recommended characterisation of lung adenocarcinoma histology is not a robust predictor of patient outcomes; even a broader model which also included indicators of tumour grade and molecular characteristics was unable to identify a model sufficiently robust to implement into clinical practice and thereby potentially alter patient treatment. Currently routinely collected clinical characteristics; including nodal status, size and clinical stage; continue to provide the most robust method of prognostication and detailed and time-consuming morphological analysis offers no significant benefit to the patient.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:738957 |
| Date | January 2018 |
| Creators | Walsh, Kathryn Jane |
| Contributors | Harrison, David ; Wallace, William ; Stirling, David |
| Publisher | University of Edinburgh |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/1842/28992 |
Page generated in 0.0021 seconds