Lung Cancer GeCIP Domain


Lung cancer is caused by uncontrolled cell growth in lung tissue. There are two main types – small-cell lung carcinoma, and non-small cell lung carcinoma. Worldwide, it is the most common cause of cancer-related death in men, and the second most-common in women. The vast majority of cases (85%) are caused by long term tobacco smoking. The remaining cases are due to air pollution, such as asbestos, second-hand smoke, or rare inherited types of lung cancer.

The lung cancer GeCIP domain will study the DNA of patient’s normal non-cancer tissue (germline) and tumour (somatic). They aim to better understand what changes occur in the DNA as the cancer spreads. They aim to find out if these DNA changes can help us to better characterise the tumour, and decide on more effective treatments to use.

Below are the current subdomains for this domain. You can find the full details of the research proposed by this domain in the Lung Cancer Detailed research Plan.

Tumour Evolution and Adaptation SubdomainSwanton, Le Quesne, Middleton, Janes, Campbell, Fennell, Van Loo, Luscombea. Identifying how lung cancers (NSCLC/SCLC/Mesothelioma) adapt and evolve through treatment and over time: ideally focus on tumour material where primary and matched paired metastatic biopsies and germline DNA are available with in depth clinical annotation.
b. Identifying the origins of the lethal subclone and how does therapy affect the subclonal composition of the relapsed tumour
c. Identification of drivers of cancer progression, diversification and adaptation/drug resistance; identification of combinatorial driver events that canalise tumours through distinct histological trajectories.
Rare Lung Cancer Cohort SubdomainHoulston, Swanton, Campbell, Fennella. Address the preponderance of distinct molecular cohorts of lung cancers within individual patient groups e.g. why are female non-smokers particularly susceptible to the acquisition of EGFR activating mutations or ALK rearrangements?
b. Address the transformation of particular NSCLC entities- eg NSCLC to Small cell transformation and the genomic drivers of adenosquamous carcinoma
c. Decipher therapeutically tractable events in malignant mesothelioma
Immunobiology SubdomainQuezada, Peggs, Swanton, Middletona. Address how the immune microenvironment edits and constrains tumour evolution?
b. Identify and validate new genomic markers predictive of benefit from immunotherapy
Radiation Oncology SubdomainFaivre Finn, Hileya. Address influence of radiation therapy on emergent resistant disease genomic landscape
b. Identify somatic and structural variants associated with disease relapse following radical radiation which could serve as potential biomarkers for dose escalation.
c. Identify genomic signatures associated with deficiencies in pathways of DNA damage repair and correlation with clinical outcome (e.g HR & NHEJ).
Circulating Biomarker SubdomainDive, Shaw, Swanton, Cookson, Moffata. Through warm autopsy programs the GeCIP will address how well circulating free tumour DNA and the CTC fraction reflects tumour diversity in the primary and metastatic tumour.
b. Generate new approaches to single cell genomics and early diagnosis
Germline SubdomainHoulston, Bowcock, Turnbulla. Discovery of novel genomic variants conferring risk of lung cancer (case- control analysis against remainder of 100KGP cohort), identifying new germline variants predisposing to risk of lung cancer and chronic lung disease
b. Characterisation of lung cancer cohort by germline findings and analysis of somatic mutational profile in relation to germline mutational status
c. Analysis of germline data for mutations in genes included in (looked for) secondary findings gene list (MLH1, MSH2, MSH6, PMS2, EPCAM, APC, MutYH, BRCA1 and BRCA2, VHL, MEN1, RET, RB1)
d. Lung cancer risk prediction modelling utilising germline findings. Selection of high risk individuals for lung cancer CT screening programmes.
NHS Infrastructure SubdomainMedical/Clinical Oncologists/Respiratory Physicians/Pathologists and Health Informatics and Bioinformaticsa. Adapt GMC research and clinical infrastructure to the changing nature of research brought about by 100K Genome project. Integrate projects within biobanking procedures for all new cancer patients treated within the NHS.
b. Adapt clinical electronic medical records to integrate with genomics medicine (FARR institute).
c. Standardise clinical data collection from patients treated with standard of care and clinical trial cohorts for integration within the Lung GECIP
Clinical, Informatics and Research training Subdomain Jamal-Hanjani, Mulatero, Hiley.
Functional Genomics SubdomainDownward, Sahai, Svejstrupa. Decipher role of new genomic drivers of lung cancer biology and signal transduction pathways through which they operate
b. Understand role of the tumour microenvironment in constraining lung cancer evolution
c. Exploit lung cancer genomics datasets to understand how lung cancer genomic instability arises

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