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N-myc downstream-regulated gene 1 (NDRG1) is induced by cellular stress such as hypoxia and DNA damage, and in humans, germ line mutations cause Charcot-Marie-Tooth disease. However, the cellular roles of NDRG1 are not fully understood. Previously, NDRG1 was shown to mediate doxorubicin resistance under hypoxia, suggesting a role for NDRG1 in cell survival under these conditions. We found decreased apoptosis in doxorubicin-treated cells expressing NDRG1 shRNAs under normoxia, demonstrating a requirement for NDRG1 in apoptosis in breast epithelial cells under normal oxygen pressure. Also, different cellular stress regimens, such as hypoxia and doxorubicin treatment, induced NDRG1 through different stress signalling pathways. We further compared expression profiles in human breast epithelial cells ectopically over-expressing NDRG1 with cells expressing NDRG1 shRNAs in order to identify biological pathways where NDRG1 is involved. The results suggest that NDRG1 may have roles connected to vesicle transport. [ABSTRACT FROM AUTHOR]

Simple Summary: Molecular tissue-based prognostic biomarkers are anticipated to complement the current risk stratification systems in prostate cancer, but their manual assessment is subjective and time-consuming. Objective assessment of such biomarkers by machine learning-based methods could advance their adoption in a clinical workflow. PTEN and DNA ploidy status are well-studied biomarkers, which can provide clinically relevant information in prostate cancer at a low cost. Using a cohort of 253 patients who received radical prostatectomy, we developed a novel, fully-automated PTEN scoring in immunohistochemically-stained tissue slides, which could be used to assess PTEN status in a reliable and reproducible manner. In an independent validation cohort of 259 patients, automatically assessed PTEN status was significantly associated with time to biochemical recurrence after radical prostatectomy, and the combination of PTEN and DNA ploidy status further improved risk stratification. These results demonstrate the utility of machine learning in biomarker assessment. Machine learning (ML) is expected to improve biomarker assessment. Using convolution neural networks, we developed a fully-automated method for assessing PTEN protein status in immunohistochemically-stained slides using a radical prostatectomy (RP) cohort (n = 253). It was validated according to a predefined protocol in an independent RP cohort (n = 259), alone and by measuring its prognostic value in combination with DNA ploidy status determined by ML-based image cytometry. In the primary analysis, automatically assessed dichotomized PTEN status was associated with time to biochemical recurrence (TTBCR) (hazard ratio (HR) = 3.32, 95% CI 2.05 to 5.38). Patients with both non-diploid tumors and PTEN-low had an HR of 4.63 (95% CI 2.50 to 8.57), while patients with one of these characteristics had an HR of 1.94 (95% CI 1.15 to 3.30), compared to patients with diploid tumors and PTEN-high, in univariable analysis of TTBCR in the validation cohort. Automatic PTEN scoring was strongly predictive of the PTEN status assessed by human experts (area under the curve 0.987 (95% CI 0.968 to 0.994)). This suggests that PTEN status can be accurately assessed using ML, and that the combined marker of automatically assessed PTEN and DNA ploidy status may provide an objective supplement to the existing risk stratification factors in prostate cancer. [ABSTRACT FROM AUTHOR]