Pre-treatment tumour perfusion parameters and initial RECIST response do not predict long-term survival outcomes for patients with head and neck squamous cell carcinoma treated with induction chemotherapy

<p>Tumour plasma perfusion (F_p) in relation to overall survival (a), disease specific survival (b) and locoregional control (c). Nodal plasma perfusion in relation to survival (d) disease specific survival (e) and locoregional control (f).</p

Exon Array Analysis of Head and Neck Cancers Identifies a Hypoxia Related Splice Variant of LAMA3 Associated with a Poor Prognosis

The identification of alternatively spliced transcript variants specific to particular biological processes in tumours should increase our understanding of cancer. Hypoxia is an important factor in cancer biology, and associated splice variants may present new markers to help with planning treatment. A method was developed to analyse alternative splicing in exon array data, using probeset multiplicity to identify genes with changes in expression across their loci, and a combination of the splicing index and a new metric based on the variation of reliability weighted fold changes to detect changes in the splicing patterns. The approach was validated on a cancer/normal sample dataset in which alternative splicing events had been confirmed using RT-PCR. We then analysed ten head and neck squamous cell carcinomas using exon arrays and identified differentially expressed splice variants in five samples with high versus five with low levels of hypoxia-associated genes. The analysis identified a splice variant of LAMA3 (Laminin α 3), LAMA3-A, known to be involved in tumour cell invasion and progression. The full-length transcript of the gene (LAMA3-B) did not appear to be hypoxia-associated. The results were confirmed using qualitative RT-PCR. In a series of 59 prospectively collected head and neck tumours, expression of LAMA3-A had prognostic significance whereas LAMA3-B did not. This work illustrates the potential for alternatively spliced transcripts to act as biomarkers of disease prognosis with improved specificity for particular tissues or conditions over assays which do not discriminate between splice variants

Establishing a large prospective clinical cohort in people with head and neck cancer as a biomedical resource: head and neck 5000

BACKGROUND: Head and neck cancer is an important cause of ill health. Survival appears to be improving but the reasons for this are unclear. They could include evolving aetiology, modifications in care, improvements in treatment or changes in lifestyle behaviour. Observational studies are required to explore survival trends and identify outcome predictors. METHODS: We are identifying people with a new diagnosis of head and neck cancer. We obtain consent that includes agreement to collect longitudinal data, store samples and record linkage. Prior to treatment we give participants three questionnaires on health and lifestyle, quality of life and sexual history. We collect blood and saliva samples, complete a clinical data capture form and request a formalin fixed tissue sample. At four and twelve months we complete further data capture forms and send participants further quality of life questionnaires. DISCUSSION: This large clinical cohort of people with head and neck cancer brings together clinical data, patient-reported outcomes and biological samples in a single co-ordinated resource for translational and prognostic research

Voice Quality Assessment by Simulating GRBAS Scoring

This paper is about the assessment of voice quality as required routinely in hospital voice clinics. It describes a computer application capable of analysing recordings of a patient's voice and producing quantitative assessments of its quality, simulating those traditionally made by trained speech and language therapists (SLTs). Adopting a machine learning approach based on a database of recordings and assessments by a team of SLTs required measurements of consistency to be taken into account. The means of doing this, details of the machine learning approaches and the performance of the resulting algorithms are presented