Correlating Changes in the Epithelial Gland Tissue With Advancing Colorectal Cancer Histologic Grade, Using IHC Stained for AIB1 Expression Biopsy Material

Employing machine learning and microscopy images of AIB1-stained biopsy material to assess the 5-year survival of patients with colorectal cancer

Our purpose was to employ microscopy images of amplified in breast cancer 1 (AIB1)-stained biopsy material of patients with colorectal cancer (CRC) to: (a) find statistically significant differences (SSDs) in the texture and color of the epithelial gland tissue, between 5-year survivors and non-survivors after the first diagnosis and (b) employ machine learning (ML) methods for predicting the CRC-patient 5-year survival. We collected biopsy material from 54 patients with diagnosed CRC from the archives of the University Hospital of Patras, Greece. Twenty-six of the patients had survived 5 years after the first diagnosis. We selected regions of interest containing the epithelial gland at different microscope lens magnifications. We computed 69 textural and color features. Furthermore, we identified features with SSDs between the two groups of patients and we designed a supervised ML system for predicting the CRC-patient 5-year survival. Additionally, we employed the VGG16 pretrained convolution neural network to extract deep learning (DL) features, the support vector machines classifier, and the bootstrap cross-validation method for boosting the accuracy of predicting 5-year survival. Fourteen features sustained SSDs between the two groups of patients. The supervised ML system achieved 87% accuracy in predicting 5-year survival. In comparison, the DL system, using images from all magnifications, gave 97% classification accuracy. Glandular texture in 5-year non-survivors appeared to be of lower contrast, coarseness, roughness, local pixel correlation, and lower AIB1 variation, all indicating loss of textural definition. The supervised ML system revealed useful information regarding features that discriminate between 5-year survivors and non-survivors while the DL system displayed superior accuracy by employing DL features.

Elevated GCN5 expression confers tamoxifen resistance by upregulating AIB1 expression in ER-positive breast cancer

Approximately 70% of breast cancers are estrogen receptor (ER)-positive and treated with endocrine therapy. A commonly used treatment agent, tamoxifen, shows high efficacy for improving prognosis. However, approximately one-third of patients treated with tamoxifen develop resistance to this drug. Here, we investigated the function of general control non-derepressible 5 (GCN5) and its downstream effectors in tamoxifen-resistant (TamR) breast cancer. TamR-MCF7 breast cancer cells maintained high GCN5 levels due to its attenuated proteasomal degradation. GCN5 overexpression upregulated amplified in breast cancer 1 (AIB1) expression, resulting in decreased p53 stability and tamoxifen resistance. Conversely, the sensitivity of GCN5-AIB1-overexpressing MCF7 cells to tamoxifen was restored by forced p53 expression. An in vivo study demonstrated a positive correlation between GCN5 and AIB1 and their contribution to tamoxifen resistance. We concluded that GCN5 promotes AIB1 expression and tamoxifen resistance in breast cancer by reducing p53 levels, suggesting the utility of GCN5 and its downstream effectors as therapeutic targets to either prevent or overcome tamoxifen resistance in breast cancer.