Diagnosing Thyroid Cancer Using a Blood Test
Conditions
Differentiated Thyroid Cancer
Conditions: official terms
Thyroid Diseases - Thyroid Neoplasms
Conditions: Keywords
thyroid cancer, papillary, follicular, proteomics, genetic markers
Study Type
Observational
Study Phase
N/A
Study Design
Observational Model: Case Control, Time Perspective: Prospective
Overall Status
Recruiting
Summary
Thyroid cancer is a relatively rare disease but its incidence is increasing in many countries.. Early and accurate diagnosis leading to earlier treatment and intervention is recognised as a major factor in determining a good outcomes. This study will investigate new ways of diagnosing thyroid cancer from blood samples using proteomic and genetic markers. The study will take samples from patients with differentiated thyroid cancer and measure relative quantities of 1000s of proteins within the blood. These measures will be explored to see if, when used in combination they can accurately diagnose thyroid cancer. If successful this technique could be extended to routine screening and could replace more invasive tests currently used. Participants will be required to supply a small sample of blood, answer questions on their medical history and also consent for their medical records to be examined. A lifestyle questionnaire will also be supplied to each participant. In the case where a diagnosis is predicted for a condition the participant was not aware of the medical team will discuss the best interests of the patient with their GP and if required refer them to a suitable specialist. The study will run for 24 months and will routinely process around 15 and 20 participants with a history of thyroid cancer per month. All patient details will be kept confidential and only non identifiable information will leave the clinic. The work will be published and if successful will be validated on another site, commercialised and made available for routine clinical use.
Detailed Description
This project will utilize two powerful technologies for diagnosing endocrine diseases: proteomics and genetic (RNA and DNA) markers. Proteomics is a relatively new, rapidly expanding and exciting area of biomedical research (Robin et al, 2009, Frolich et al, 2009). Posttranslational modifications of proteins are critical for function. Modified proteins may be markers of cancer phenotypes and therefore be useful tumor markers (Narimatsu et al, 2010). Proteomic research in thyroid cancer is in its infancy (Krause et al, 2009). The available studies on thyroid cancer have utilised tissue rather than serum samples, nonetheless the results are encouraging (Brown et al, 2006, Wang et al, 2006, Netea-Maier et al, 2008, Krause et al, 2007, Moretz et al, 2008).

Genomic markers of thyroid cancer have been described and are increasingly being used on biopsy material for accurate diagnosis. Among the described markers point mutations (BRAF V600E, NRAS codon 61, HRAS codon 61), gene rearrangements (RET / PTC1, RET / PTC3, PAX8 / PPARgamma) and other polymorphisms have been found to be useful (Nikiforova and Nikiforov, 2009, Ohori et al, 2010). There is good evidence that in recurrent thyroid cancer small numbers of thyroid cancer cells can be detected in peripheral blood, in sufficient quantities to detect thyroid-specific mRNA by RT PCR (Karavitaki et al, 2005, Barbosa et al, 2008, Milas et al, 2009). Most of these studies have focused on the detection of thyroglobulin mRNA with moderate success. A significant difficulty with this approach is that detection of thyroglobulin mRNA in peripheral blood cannot distinguish between the presence of normal thyroid tissue or thyroid cancer.

The project is a collaborative venture between Newcastle Biomedicine, the NHS, and Biosignatures Ltd (a North-East based proteomics diagnostics company). Biosignatures has invested a great deal of research in optimizing sample handling and sample analysis, thus giving rise to plasma proteomic protocols that are stable and suitable for large comparative studies (Elliott et al, Jackson et al, 2010, Bramwell et al, 2007). The data generated from plasma 2D gel electrophoresis and mass spectroscopy is analysed by proprietary "supervised learning" technology. The system is given multiple examples of group classes (disease cases) and from this derives a signature pattern ('proteomic fingerprint') that allows the classes to be discriminated. This signature will then be validated against a novel patient dataset to ensure robust disease status discrimination. The combination of this research and technology can produce blood derived signatures of disease in an applied clinical setting (Cash and Argo, 2009, Borthwick et al, 2009).

Thyroid cancer affects 2000 new patients in the UK per annum (Cancer Research UK). Once the initial treatment of thyroid cancer is completed (thyroidectomy followed by radioiodine ablation), monitoring is essential to detect residual disease or recurrence. Recurrence rates in thyroid cancer are as high as 30% (Mazzaferri and Kloos, 2001) and can declare themselves decades after initial treatment, so that patients have to be monitored regularly for life. Monitoring for disease recurrence consists of iodine scans, an ultrasound scan of the neck 6-8 months after initial treatment, and 6-12 monthly blood tests thereafter for the serum marker thyroglobulin. Thyroglobulin is a valuable marker in many people with thyroid cancer (Spencer and Fatemi, 2006). Unfortunately in approximately 30% of patients antibody interference with the assay renders this test unreliable (Spencer and Fatemi, 2006). In such cases patients are subjected to repeated scans, though a negative scan has a far less predictive value than a negative thyroglobulin blood test when the analyte can be measured reliably. We have selected thyroid cancer as the primary topic of study for proof of concept for the following reasons:

- Current diagnostics technology (measurement of serum thyroglobulin) suffers from interference of measurement of the analyte in 30% of cases, rendering this tumour marker entirely unreliable when such antibodies are present. Attempts using conventional biochemical analytical technology to overcome this problem over the past 3 decades have failed. Thus a proteomics/genomics approach has only to perform with a better than 70% specificity to provide a superior diagnostic test.

- The potential cost savings to the NHS by the development of such a diagnostic test (by avoidance of expensive scans) will be considerable.

- Exposure of patients to radiation from repeated scans will be reduced with obvious safety benefits.

- The study is non-interventional, will induce no additional discomfort, and is expected to have no impact on the care received by participants at this stage.

- Extrapolation of such technology to the evaluation of thyroid nodules (present clinically in 5% of the adult population) and even screening of the population for thyroid malignancy, would have profoundly beneficial preventative and public health consequences.
Criteria for eligibility
Healthy Volunteers: No
Maximum Age: N/A
Minimum Age: 18 Years
Gender: Both
Criteria: Inclusion Criteria:

1. Age over 18 years.

2. Patient has thyroid cancer.

3. Patient is judged as being capable of understanding the information sheet and of giving informed consent (Mental Capacity Act 2005).

4. Responsible clinician is approached and is happy for the patient to be included in the study.

Exclusion Criteria:

1. Age of less than 18 years.

2. Patient has additional risk infections (HIV, Hep B/C)

3. Patient is involved in other medicinal or treatment based clinical trial at the time of recruitment or in the previous 4 months.
Locations
Sir Bobby Robson Cancer Researhc Unit
Newcastle upon Tyne, Tyne and Wear, United Kingdom
Status: Recruiting
Contact: Petros Perros, BSc, MBBS, MD - 00441912336161 - petros.perros@nuth.nhs.uk
Sir Bobby Robson Cancer Research Unit, Northern Centre for cancer care
Newcastle upon Tyne, United Kingdom
Status: Active, not recruiting
Start Date
April 2011
Sponsors
Newcastle-upon-Tyne Hospitals NHS Trust
Source
Newcastle-upon-Tyne Hospitals NHS Trust
Record processing date
ClinicalTrials.gov processed this data on July 28, 2015
ClinicalTrials.gov page