Like most human epithelial cancers, oral cancer originates form an accumulation of molecular, genetic and biological events through a series of precancerous lesions. The earlier we detect the lesions during its natural history, the better the outcome.
Thanks to rapid development and explosion in informatics, electronics, optics, and microscopy, new technologies can now be used to detect these early lesions. Unfortunately, even with the exponential quantity and resolution of data generated by the "omics" sciences (genomics, proteomics, digital pathology, etc..), it is still difficult to identify lesions that are likely to become cancer , i.e. high-risk precancerous lesions, in order to treat them before they become fully malignant. To transform this data to knowledge, other approaches are needed.
We propose the development of a modeling platform for the spatio-temporal analysis of oral cancer, that will combine 3D in silico modeling and statistical modeling approaches.
As part of a pan-canadian Terry Fox grant project and the British Columbia Oral Cancer Prevention Program (BC OCPP), directed by Dr Miriam Rosin (BCCRC), we have been collecting for the last 15 years a unique collection of oral specimens from patients involved in different clinical trials. In the present COOL trial, we have been collecting cytological and histological specimens from high-risk patients at different time for a 2 years period with the following characteristics:
A. Clinical information and risk factors are available for each patient (age, sex, smoking, viral infection, alcohol, etc.. ).
B. Sampling and analyses are repeated every 6 months for 2 years (temporal level)
C. For each patient, different histological specimens are collected from the oral cavity: from the worst abnormal area as well as from areas sampled at different distances from the former, and/or from randomly selected areas (spatial level 1)
D. For each specimen, pathologists identify different biologically or pathologically distinct unit areas(spatial level 2)
E. Genetic markers and several phenotypic biomarkers are measured on each unit area by Quantitative Tissue Phenotypic Analysis (using high-resolution image algorithms) that extracts quantitative information at three levels:
o Cell level : the basic unit of analysis: about 200 features measured on each cell (nuclear morphology, DNA content , DNA chromatin texture, etc..), with few hundred to thousand nuclei per unit (spatial level 3);
o Tissue level : the overall organization of the unit. Using graph-theory based tools (Voronoi diagram, MST, Gabriel Graph, RNG,etc.. ) (spatial level 4);
o Neighbourhood level : spatial arrangement of different groups of cells sharing molecular or phenotypic characteristics (mutated cells, proliferating cells, etc.) (using local graph, Ulam tree, k Nearest neighbours, etc..) (spatial level 5);
We will first present the correlation between phenotypic features, genetic markers and pathological diagnosis. We will show the potential of these features to identify high-risk lesions more likely to progress to cancer. We will then present some preliminary simulations of our in silico static and dynamic 3D model (Idefics). Finally we will introduce the concepts of a new spatio-temporal statistical model of oral cancer progression.