1.1 Lung cancer & Indeterminate Lung Nodule Surveillance Over 46,000 cases of lung cancer are
diagnosed every year in the UK, making it the 3rd most common cancer type. Lung cancer is the
biggest cause of cancer mortality in the UK and worldwide due to late presentation in the
majority of cases. One-year survival for lung cancer ranges from 83% at stage I to 17% in
stage IV disease (CRUK data).
1.2 Incidental Lung Nodules A significant challenge posed by lung screening is the
identification of incidental lung nodules. 9.3% of all patients in the NELSON study had
indeterminate nodules, and only 10% of these were diagnosed with cancer.
Such nodules are very frequently picked up on CT scans performed for other reasons, and may
generate anxiety and uncertainty for patients and clinicians as well as using considerable
NHS CT scan capacity. Current methods of stratification are based on a combination of The
British Thoracic Society guidelines and the Brock, Herder and Fleischner risk models.
Depending on the size of the lesion, guidelines recommend surveillance CT scans at 3-12
monthly intervals for solid and sub-solid lesions. Previous studies have suggested that
persistent sub-solid nodules have a high risk of malignancy (~63%), and using Brock
guidelines, larger nodules are often referred for biopsy (Henschke, 2002). However, a
proportion of patients who score highly on these models will have negative biopsies, and
there is a definite need for improved stratification.
In the screening setting, identification of early lung cancers and nodules in 'Lung Health
Checks' - which use 'low dose' CT (LDCT) scan screening of high-risk populations (e.g. heavy
smokers) has been shown to reduce lung cancer mortality by 20-26% as observed in the National
Lung Cancer Screening Trial (NLST) and NELSON studies. A number of pilot trials within the UK
have led to a commitment by NHS England to roll-out a £70m national program in a number of
test sites. This program will lead to an expected 10% indeterminate finding rate putting
further strain on the management of indeterminate nodules. RM Partners is undertaking one of
the early lung screening pilots that led to this program across two clinical commissioning
groups (CCGs) in West London in 2018, inviting over 8000 patients for a lung health check.
This pilot has been extended in 2019-2020 and will also be incorporated in the NHS England
National program.
1.3 Imaging and blood biomarkers in lung cancer early diagnosis Recent data suggest that the
application of machine-learning approaches to the NLST trial data improves radiological
risk-stratification of nodules (Ardila et al., 2019). Through the retrospective RMH LIBRA
study, we are currently developing radiomics and Artificial Intelligence (AI) signatures to
stratify lung nodules in patients from across the London cancer alliances. There is
increasing interest in multi-model approaches, and the incorporation of 'multi-omic' data may
enhance diagnostic accuracy and risk stratification (Bakr et al., 2018; Lu et al., 2018).
Lung cancer biomarker development is a rapidly evolving field that spans genetics approaches
such as ctDNA sequencing and methylation studies, to more indirect measures of a systemic
response to active malignancy in order to indicate the presence of cancer such as metabolomic
and immunophenotyping studies. There is considerable interest in using such lung nodule
populations for development of lung cancer biomarkers where a positive result would represent
very early stage disease. The identification of non-invasive predictive and prognostic
biomarkers is therefore an important priority. This data set thus represents an important
cohort to translate discovery science to patient facing clinical assays that could facilitate
earlier cancer diagnosis.
1.4 Tumour Immunophenotyping Observations that cancer relapse is related to the
neutrophil-lymphocyte ratio, and that lung cancer development appears related to changes in
interferon signalling (Mizuguchi 2018, Beane 2019) lead us to hypothesise that immune
phenotyping may have a role to play in the early-diagnosis setting. Recent advances in flow
and mass cytometry now allow high dimensional immunophenoyping, through simultaneous
measurement of ~40 markers per cell. Hence the central challenge of this project is to
develop a more detailed understanding of the host immune phenotypes that are associated with
cancer development risk, based on longitudinal high dimensional immunophenotyping, rather
than low dimensional measurement of single markers. We hypothesise high dimensional data will
allow a more detailed, and context resolved, set of immune phenotype states to be defined,
which can be developed into accurate biomarkers to predict the risk of tumour development and
relapse. Indeed, in support of this hypothesis, high dimensional immune phenotypes have
already been discovered which can predict all-cause mortality in longitudinal studies of
heart disease. We have conducted pilot analysis of an existing CRUK cohort of early stage
lung tumour patients already recruited through the TRACERx study, to demonstrate the
feasibility of high dimensional immune phenotyping in patient samples. NIMBLE will tackle an
underlying challenge of work in this area which is a shortage of clinical pre/non-malignant
samples with longitudinal follow up.
- Rationale Incidental lung nodules are common, and may represent early cancers. Their
assessment can result in delayed diagnosis while interval imaging is performed to assess
risk.
This study will allow us to examine the potential for imaging and blood biomarkers to augment
nodule stratification, and identify high-risk patients who may benefit from more frequent
surveillance or earlier diagnostic procedures, and low risk patients suitable for reduced
surveillance intensity. This is particularly relevant for the COVID-19 era to stratify
hospital attendances and high risk interventions to those in greatest need. This project
dovetails with existing radiomics and lung biomarker research (LIBRA and Lung Health Check
Biomarker Study) within our early diagnosis research group.
- Hypothesis
Primary Hypothesis: Peripheral blood Immune phenotype differences will be present between
benign and malignant lung nodules, which can be developed into accurate biomarkers to predict
the risk of tumour development and relapse.
Secondary hypothesis: Combined use of blood and imaging biomarkers will enhance malignancy
prediction in patients with incidental lung nodules.
Exploratory hypothesis: Blood biomarkers such as immunophenotyping or metabolomics ±
radiomics vector, when measured as a continuous variable will see a decrease in risk score
following tumour resection or regression.