Automated Quantification of p53 in Tumor Cells

Preprocessing steps were applied as described above. QuPath’s Cell detection command was then used to identify cells across all cores based upon nuclear staining. This command additionally estimates the full extent of each cell based upon a constrained expansion of the nucleus region, and calculates up to 33 measurements of intensity and morphology, including nucleus area, circularity, staining intensity for hematoxylin and DAB, and nucleus/cell area ratio. Because not all of these measurements are expected to provide independent or useful information with regard to cell classification, a subset of 16 measurements was chosen empirically and supplemented for each cell by measuring the local density of cells, and taking a Gaussian-weighted sum of the corresponding measurements within neighboring cells using QuPath’s Add smoothed features command. A two-way random trees classifier was then interactively trained to distinguish tumor epithelial cells from all other detections (comprising non-epithelial cells, necrosis, or any artefacts misidentified as cells) and applied across all slides (see Supplementary Video 2). Intensity thresholds were set to further subclassify tumor cells as being negative, weak, moderate or strongly positive for p53 staining based upon mean nuclear DAB optical densities. An H-score was calculated for each tissue core by adding 3x% strongly stained tumor nuclei, 2x% moderately stained tumor nuclei, and 1x% weakly stained tumor nuclei^{32 (link)}, giving results in the range 0 (all tumor nuclei negative) to 300 (all tumor nuclei strongly positive).