MANCAN - an integrated clinical and blood based multi-analyte readout

This 4 year PhD studentship offered in Professor Caroline Dive’s research group is based at the Cancer Research UK Manchester Institute, Alderley Park, Cheshire

With the development of highly sensitive molecular assays the range of blood biomarkers available for cancer research has expanded dramatically over the last decade and in June, 2016 the US FDA approved the first blood-based companion diagnostic for erlotnib (Tarceva) based on detecting EGFR gene mutations in non-small cell lung cancer (NSCLC) patients1. A broader analysis of mutations found in the tumour DNA present in patient blood has shown the potential for blood based next generation sequencing (NGS) for patient selection in clinical trials2. However, it is clear that, although patient selection is important, earlier detection is key to reducing cancer deaths as shown in a low-dose CT (LDCT) lung cancer screening pilot which successfully identified early stage NSCLC disease suitable for surgical resection3.

Use of blood based readouts for early detection of resectable cancers is challenged by the low levels of cancer derived and molecules present in a blood sample. Recently, the multi-analyte blood CancerSEEK test which combines measurements of circulating proteins and mutations in cell-free DNA (cfDNA) has demonstrated that combining readouts can improve overall levels of detection4. The overarching aim of this PhD proposal is to facilitate clinical decisions, leading ultimately to improved patient outcomes. This will be achieved by providing streamlined clinical reports based on integration of patient specific clinical information along with the multiple blood based cellular, DNA, RNA and protein readouts that have been developed in Manchester2,5-9.

Approach
To compare the integrated multi-analyte to standard single analyte readouts we will examine data obtained from blood of patients with known early stage lung cancer (NSCLC and SCLC) to equivalent data obtained from cancer free age matched individuals taking part community-based screening3. Bioinformatics analysis will help assess the utility of each dataset for providing cancer biomarkers and aim to improve the sensitivity or specificity by integrating them, leading to the creation of robust classifiers through machine learning [ML] or Bayesian approaches.

Applications are invited from exceptionally high calibre students, graduates or final year undergraduates who should hold or are expected to gain a first/upper second-class honours degree in a relevant subject as part of a University degree course.

Applicants can find full group project details, entry criteria and details on how to apply online at:
http://www.cruk.manchester.ac.uk/education/PhD-Studentships

Closing date: Friday 3 January 2020 – 2100 hours GMT

Interview date: Tuesday 18 February 2020, Alderley Park, Cheshire