The majority of global incidences of oral cancer occur in Asia, and the aetiology of oral cancer is different in Asia as it is in the West. However, whereas there is a growing understanding of the molecular mechanisms of oral cancer progression in the West, there is little progress in this understanding in Asia. In particular, the role of the p53 pathway in modulating cancer progression in Asian oral cancer remains unclear. In this study, we micro-dissected and analysed 20 well-differentiated oral squamous cell carcinoma specimens for alterations in the p53 pathway. We found that 6/20 samples contained mutations in the p53 gene which occurred in three hotspots, at codon 203, 218 and 296. Furthermore, 6/20 samples had a homozygous deletion of p14ARF, but notably p14ARF deletion and p53 mutation events were often independent and mutually exclusive. Strikingly, MDM2 was upregulated in 20/20 samples, but not in 3/3 normal tissue specimens. Taken together, these data suggest that inactivation of the p53 pathway is a frequent event in oral squamous cell carcinoma, which occurs by an aberration in one of a number of players in the p53 pathway.
Matched MeSH terms: Tumor Suppressor Protein p53/metabolism*
We present a computer aided diagnostic workflow focusing on two diagnostic branch points in neuropathology (intraoperative consultation and p53 status in tumor biopsy specimens) by means of texture analysis via discrete wavelet frames decomposition. For intraoperative consultation, our methodology is capable of classifying glioblastoma versus metastatic cancer by extracting textural features from the non-nuclei region of cytologic preparations based on the imaging characteristics of glial processes, which appear as anisotropic thin linear structures. For metastasis, these are homogeneous in appearance, thus suitable and extractable texture features distinguish the two tissue types. Experiments on 53 images (29 glioblastomas and 24 metastases) resulted in average accuracy as high as 89.7 % for glioblastoma, 87.5 % for metastasis and 88.7 % overall. For p53 interpretation, we detect and classify p53 status by classifying staining intensity into strong, moderate, weak and negative sub-classes. We achieved this by developing a novel adaptive thresholding for detection, a two-step rule based on weighted color and intensity for the classification of positively and negatively stained nuclei, followed by texture classification to classify the positively stained nuclei into the strong, moderate and weak intensity sub-classes. Our detection method is able to correctly locate and distinguish the four types of cells, at 85 % average precision and 88 % average sensitivity rate. These classification methods on the other hand recorded 81 % accuracy in classifying the positive and negative cells, and 60 % accuracy in further classifying the positive cells into the three intensity groups, which is comparable with neuropathologists' markings.
Matched MeSH terms: Tumor Suppressor Protein p53/metabolism