Molecular Pathology of Cancers/Models

The Cancer Genome Atlas (TCGA) project has generated abundant genomic data for human cancers of various histopathology types and enabled exploring cancer molecular pathology per big data approach. We developed a new algorithm based on most differentially expressed genes (DEG) per pairwise comparisons to calculate correlation coefficients to be used to quantify similarity within and between cancer types. We systematically compared TCGA cancers, demonstrating high correlation within types and low correlation between types, thus establishing molecular specificity of cancer types and an alternative diagnostic method largely equivalent to histopathology. Different coefficients for different cancers in study may reveal that the degree of the within-type homogeneity varies by cancer types. We also performed the same calculation using the TCGA-derived DEGs on patient-derived xenografts (PDX) of different histopathology types corresponding to the TCGA types, as well as on cancer cell lines. We, for the first time, demonstrated highly similar patterns for within- and between-type correlation between PDXs and patient samples in a systematic study, confirming the high relevance of PDXs as surrogate experimental models for human diseases. In contrast, cancer cell lines have drastically reduced expression similarity to both PDXs and patient samples. The studies also revealed high similarity between some types, for example, LUSC and HNSCC, but low similarity between certain subtypes, for example, LUAD and LUSC. Our newly developed algorithm seems to be a practical diagnostic method to classify and reclassify a disease, either human or xenograft, with better accuracy than traditional histopathology. Cancer Res; 76(16); 4619–26. ©2016 AACR.

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