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
