Nodule IMmunophenotyping Biomarker for Lung Cancer Early Diagnosis Study

Lung Cancer Clinical Trial

Official title:

Nodule IMmunophenotyping Biomarker for Lung Cancer Early Diagnosis Study

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05432739
• Other study ID #: NIMBLE
• Status: Recruiting
• Start date: April 7, 2021
• Est. completion date: January 1, 2031

Study information

• Verified date: January 2023
• Source: Royal Marsden NHS Foundation Trust
• Contact: Sejal Jain
• Phone: 02078082603
• Email: sejal.jain[@]rmh.nhs.uk
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

NIMBLE is a prospective study for blood biomarker study of lung nodules alongside analysing data which has been collected routinely as part of patient care. The primary aim of NIMBLE is to assess whether artificial intelligence and machine learning based radiomics approaches can be used to distinguish between benign disease and malignancy in a new lung nodule after a previously treated cancer, and where malignant to differentiate between metastatic recurrence or a new primary lung cancer.

Description:

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. 2. 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. 3. 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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 500
• Est. completion date: January 1, 2031
• Est. primary completion date: January 1, 2026
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Patients under active investigation or surveillance for incidental lung nodules
• Age > 18.

Exclusion Criteria:

• Active or previous diagnosis of malignancy (within 5 years preceding baseline scan).
• Inability to give informed consent.
• Active infection (including tuberculosis or fungal infection).
• Clinician-suspected or confirmed active or recent COVID-19 infection (less than 4 weeks before CT scan or required blood sampling date).

Related Conditions & MeSH terms

• Lung Cancer
• Lung Neoplasms

Locations

Country	Name	City	State
United Kingdom	Barking Havering and Redbridge University Hospitals NHS Trust	Goodmayes	Essex
United Kingdom	Calderdale and Huddersfield NHS Foundation Trust	Huddersfield
United Kingdom	Guy's and St Thomas' NHS Foundation Trust	London
United Kingdom	Princess Alexandra Hospital	London
United Kingdom	Royal Marsden Hospital	London
United Kingdom	University College London Hospitals NHS Foundation Trust	London
United Kingdom	Whittington Health NHS Trust	London
United Kingdom	Northumbria NHS Foundation Trust	Newcastle Upon Tyne
United Kingdom	Nottinghamshire Healthcare NHS Foundation Trust	Nottingham

Sponsors (13)

Lead Sponsor

Collaborator

Royal Marsden NHS Foundation Trust

Epsom and St Helier University Hospitals NHS Trust, Francis Crick Institute, Guy's and St Thomas' NHS Foundation Trust, Imperial College London, Institute of Cancer Research, United Kingdom, King's College Hospital NHS Trust, Lewisham and Greenwich NHS Trust, Royal Brompton & Harefield NHS Foundation Trust, Royal Marsden Partners West London Cancer Alliance, Royal Sussex County Hospital, University College London (UCL) Cancer Institute, University College London Hospitals

Country where clinical trial is conducted

United Kingdom, 

References & Publications (22)

Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, Hoebers F, Rietbergen MM, Leemans CR, Dekker A, Quackenbush J, Gillies RJ, Lambin P. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014 Jun 3;5:4006. doi: 10.1038/ncomms5006. Erratum In: Nat Commun. 2014;5:4644. Cavalho, Sara [corrected to Carvalho, Sara]. — View Citation

Aoki K. Excess incidence of lung cancer among pulmonary tuberculosis patients. Jpn J Clin Oncol. 1993 Aug;23(4):205-20. — View Citation

Ardila D, Kiraly AP, Bharadwaj S, Choi B, Reicher JJ, Peng L, Tse D, Etemadi M, Ye W, Corrado G, Naidich DP, Shetty S. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med. 2019 Jun;25(6):954 — View Citation

Baldwin DR, Callister ME; Guideline Development Group. The British Thoracic Society guidelines on the investigation and management of pulmonary nodules. Thorax. 2015 Aug;70(8):794-8. doi: 10.1136/thoraxjnl-2015-207221. Epub 2015 Jul 1. — View Citation

Beane JE, Mazzilli SA, Campbell JD, Duclos G, Krysan K, Moy C, Perdomo C, Schaffer M, Liu G, Zhang S, Liu H, Vick J, Dhillon SS, Platero SJ, Dubinett SM, Stevenson C, Reid ME, Lenburg ME, Spira AE. Molecular subtyping reveals immune alterations associated with progression of bronchial premalignant lesions. Nat Commun. 2019 Apr 23;10(1):1856. doi: 10.1038/s41467-019-09834-2. — View Citation

Becker N, Motsch E, Gross ML, Eigentopf A, Heussel CP, Dienemann H, Schnabel PA, Eichinger M, Optazaite DE, Puderbach M, Wielputz M, Kauczor HU, Tremper J, Delorme S. Randomized Study on Early Detection of Lung Cancer with MSCT in Germany: Results of the First 3 Years of Follow-up After Randomization. J Thorac Oncol. 2015 Jun;10(6):890-6. doi: 10.1097/JTO.0000000000000530. — View Citation

Field JK, Duffy SW, Baldwin DR, Brain KE, Devaraj A, Eisen T, Green BA, Holemans JA, Kavanagh T, Kerr KM, Ledson M, Lifford KJ, McRonald FE, Nair A, Page RD, Parmar MK, Rintoul RC, Screaton N, Wald NJ, Weller D, Whynes DK, Williamson PR, Yadegarfar G, Hansell DM. The UK Lung Cancer Screening Trial: a pilot randomised controlled trial of low-dose computed tomography screening for the early detection of lung cancer. Health Technol Assess. 2016 May;20(40):1-146. doi: 10.3310/hta20400. — View Citation

Henschke CI, Yankelevitz DF, Mirtcheva R, McGuinness G, McCauley D, Miettinen OS; ELCAP Group. CT screening for lung cancer: frequency and significance of part-solid and nonsolid nodules. AJR Am J Roentgenol. 2002 May;178(5):1053-7. doi: 10.2214/ajr.178.5.1781053. — View Citation

Horeweg N, van Rosmalen J, Heuvelmans MA, van der Aalst CM, Vliegenthart R, Scholten ET, ten Haaf K, Nackaerts K, Lammers JW, Weenink C, Groen HJ, van Ooijen P, de Jong PA, de Bock GH, Mali W, de Koning HJ, Oudkerk M. Lung cancer probability in patients with CT-detected pulmonary nodules: a prespecified analysis of data from the NELSON trial of low-dose CT screening. Lancet Oncol. 2014 Nov;15(12):1332-41. doi: 10.1016/S1470-2045(14)70389-4. Epub 2014 Oct 1. — View Citation

Infante M, Cavuto S, Lutman FR, Brambilla G, Chiesa G, Ceresoli G, Passera E, Angeli E, Chiarenza M, Aranzulla G, Cariboni U, Errico V, Inzirillo F, Bottoni E, Voulaz E, Alloisio M, Destro A, Roncalli M, Santoro A, Ravasi G; DANTE Study Group. A randomized study of lung cancer screening with spiral computed tomography: three-year results from the DANTE trial. Am J Respir Crit Care Med. 2009 Sep 1;180(5):445-53. doi: 10.1164/rccm.200901-0076OC. Epub 2009 Jun 11. — View Citation

International Agency for Research on Cancer.

Lu H, Arshad M, Thornton A, Avesani G, Cunnea P, Curry E, Kanavati F, Liang J, Nixon K, Williams ST, Hassan MA, Bowtell DDL, Gabra H, Fotopoulou C, Rockall A, Aboagye EO. A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecular-phenotypes of epithelial ovarian cancer. Nat Commun. 2019 Feb 15;10(1):764. doi: 10.1038/s41467-019-08718-9. — View Citation

Malhotra J, Malvezzi M, Negri E, La Vecchia C, Boffetta P. Risk factors for lung cancer worldwide. Eur Respir J. 2016 Sep;48(3):889-902. doi: 10.1183/13993003.00359-2016. Epub 2016 May 12. — View Citation

Mizuguchi S, Izumi N, Tsukioka T, Komatsu H, Nishiyama N. Neutrophil-lymphocyte ratio predicts recurrence in patients with resected stage 1 non-small cell lung cancer. J Cardiothorac Surg. 2018 Jun 27;13(1):78. doi: 10.1186/s13019-018-0763-0. — View Citation

Musolf AM, Simpson CL, de Andrade M, Mandal D, Gaba C, Yang P, Li Y, You M, Kupert EY, Anderson MW, Schwartz AG, Pinney SM, Amos CI, Bailey-Wilson JE. Familial Lung Cancer: A Brief History from the Earliest Work to the Most Recent Studies. Genes (Basel). 2017 Jan 17;8(1):36. doi: 10.3390/genes8010036. — View Citation

National Lung Screening Trial Research Team; Aberle DR, Adams AM, Berg CD, Black WC, Clapp JD, Fagerstrom RM, Gareen IF, Gatsonis C, Marcus PM, Sicks JD. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011 Aug 4;365(5):395-409. doi: 10.1056/NEJMoa1102873. Epub 2011 Jun 29. — View Citation

Paci E, Puliti D, Lopes Pegna A, Carrozzi L, Picozzi G, Falaschi F, Pistelli F, Aquilini F, Ocello C, Zappa M, Carozzi FM, Mascalchi M; the ITALUNG Working Group. Mortality, survival and incidence rates in the ITALUNG randomised lung cancer screening trial. Thorax. 2017 Sep;72(9):825-831. doi: 10.1136/thoraxjnl-2016-209825. Epub 2017 Apr 4. — View Citation

Patz EF Jr, Campa MJ, Gottlin EB, Trotter PR, Herndon JE 2nd, Kafader D, Grant RP, Eisenberg M. Biomarkers to help guide management of patients with pulmonary nodules. Am J Respir Crit Care Med. 2013 Aug 15;188(4):461-5. doi: 10.1164/rccm.201210-1760OC. — View Citation

Rosenberger A, Bickeboller H, McCormack V, Brenner DR, Duell EJ, Tjonneland A, Friis S, Muscat JE, Yang P, Wichmann HE, Heinrich J, Szeszenia-Dabrowska N, Lissowska J, Zaridze D, Rudnai P, Fabianova E, Janout V, Bencko V, Brennan P, Mates D, Schwartz AG, Cote ML, Zhang ZF, Morgenstern H, Oh SS, Field JK, Raji O, McLaughlin JR, Wiencke J, LeMarchand L, Neri M, Bonassi S, Andrew AS, Lan Q, Hu W, Orlow I, Park BJ, Boffetta P, Hung RJ. Asthma and lung cancer risk: a systematic investigation by the International Lung Cancer Consortium. Carcinogenesis. 2012 Mar;33(3):587-97. doi: 10.1093/carcin/bgr307. Epub 2011 Dec 22. — View Citation

Sverzellati N, Silva M, Calareso G, Galeone C, Marchiano A, Sestini S, Sozzi G, Pastorino U. Low-dose computed tomography for lung cancer screening: comparison of performance between annual and biennial screen. Eur Radiol. 2016 Nov;26(11):3821-3829. doi: 10.1007/s00330-016-4228-3. Epub 2016 Feb 11. — View Citation

van Klaveren RJ, Oudkerk M, Prokop M, Scholten ET, Nackaerts K, Vernhout R, van Iersel CA, van den Bergh KA, van 't Westeinde S, van der Aalst C, Thunnissen E, Xu DM, Wang Y, Zhao Y, Gietema HA, de Hoop BJ, Groen HJ, de Bock GH, van Ooijen P, Weenink C, Verschakelen J, Lammers JW, Timens W, Willebrand D, Vink A, Mali W, de Koning HJ. Management of lung nodules detected by volume CT scanning. N Engl J Med. 2009 Dec 3;361(23):2221-9. doi: 10.1056/NEJMoa0906085. — View Citation

Wille MM, Dirksen A, Ashraf H, Saghir Z, Bach KS, Brodersen J, Clementsen PF, Hansen H, Larsen KR, Mortensen J, Rasmussen JF, Seersholm N, Skov BG, Thomsen LH, Tonnesen P, Pedersen JH. Results of the Randomized Danish Lung Cancer Screening Trial with Focus on High-Risk Profiling. Am J Respir Crit Care Med. 2016 Mar 1;193(5):542-51. doi: 10.1164/rccm.201505-1040OC. — View Citation

* Note: There are 22 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Other	Exploratory Outcome	To develop pilot data that would indicate whether such an assay could demonstrate a reduction in signal alongside a post-surgical course or radiological evidence of regression that would suggest utility in the early detection of recurrence.; To explore whether blood metabolomics or DNA methylation analysis differs between cancerous and non-cancerous lung nodules.; To provide a cohort of patients whose specimens would be accessible for future development of other specific novel biomarker technologies in surplus blood and potentially other biological specimens (e.g. biopsies/tissue samples, breath, sputum or urine) at the discretion of the TMG and after further HRA approval by protocol amendment.	10 years
Primary	Primary Outcome	To discover an immunophenotyping predictive classifier, to distinguish patients with benign versus malignant lung nodules.	10 Years
Secondary	Secondary Outcome	To discover a composite predictive classifier incorporating radiomics and immunophenotyping data, to distinguish patients with benign versus malignant lung nodules.	10 Years