Figure 3. Correlation and prediction of morphologic and molecular tumor phenotypes.

(A) Example ROIs corresponding to four tumor morphologies used for training and non-adjacent regions predicted with high confidence. kNN classifiers were trained and validated separately for each section to evaluate model reproducibility. (B) Prediction confidence for assignment of kNN classes as measured by Shannon entropy (0 corresponds to perfect certainty; 2 indicates random assignment (equal mixing). (C) Posterior probability that each CK⁺ cell belongs to the given tumor class. Annotation reflects classifier gradients corresponding to morphologic phenotype. (D) Left: Sample tumor region that transitions from normal to abnormal glandular features coinciding with transition from E-cadherin expression to PCNA (CyCIF, bottom). Contours describe averaged local epithelial cell expression of PCNA. Center and right: Additional examples of transition regions. (E) PCA of 31 spatially-resolved GeoMx transcriptomics regions (areas in Figure S1A). (F) Cumulative distribution of single-cell classification entropy of CRC1–17. Patients with only two classes had only normal epithelial and a tumor morphology class. Different CRC1 sections used different markers for classification. (G) Examples of marker gradients; whole tumor sections. White circles denote TMA cored regions.