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The combination of DNA ploidy and automatically estimated stroma fraction has been shown to correlate with recurrence and cancer death in colorectal cancer. We aimed to extend this observation and evaluate the prognostic importance of this combined marker in prostate cancer. DNA ploidy status was determined by image cytometry and the stroma fraction was estimated automatically on hematoxylin and eosin stained sections in three tumor samples from each patient to account for tumor heterogeneity. The optimal threshold for low (≤56%) and high (>56%) stroma fraction was identified in a discovery cohort (n = 253). The combined marker was validated in an independent patient cohort (n = 259) with biochemical recurrence as endpoint. The combined marker predicted biochemical recurrence independently in the validation cohort. Multivariable analysis showed that the highest risk of recurrence was observed for patients with samples that had both non‐diploid ploidy status and a high stroma fraction (hazard ratio: 2.51, 95% confidence interval: 1.18–5.34). In conclusion, we suggest the combination of DNA ploidy and automatically estimated stroma fraction as a prognostic marker for the risk stratification of prostate cancer patients. It may also be a potential generic marker as concurrent results have been described in colorectal cancer. What's new? The stroma that surrounds tumor cells can have significant effects on tumor growth and behavior. Another hallmark of cancer is aneuploidy caused by genomic instability, which offers an important prognostic marker in several cancer types. In this study, the authors found that combining these two markers—stromal fraction and DNA ploidy—provided a significant predictor of biochemical recurrence in prostate cancer. The low cost, high throughput, and accuracy of digital analysis using this combined method thus promise to provide improved prognostic biomarkers in prostate cancer. [ABSTRACT FROM AUTHOR]

Background: The clinical outcome for the individual prostate cancer patient is often difficult to predict, due to lack of reliable independent prognostic biomarkers. We tested DNA ploidy as a prognostic factor for clinical outcome in 186 patients treated with radical prostatectomy. Methods: DNA ploidy was measured using an automatic image cytometry system and correlated with preoperative PSA, age at surgery, Mostofi grade, surgical margins and Gleason score. Results: The mean follow up time after operation was 73.3 months (range 2–176 months). Of the 186 prostatectomies, 96 were identified as diploid, 61 as tetraploid and 29 as aneuploid. Twenty-three per cent, 36% and 62% of the diploid, tetraploid and aneuploid cases respectively, suffered from relapse during the observation time. DNA ploidy, Gleason score, Mostofi grading, surgical margins and preoperative PSA were all significant predictors of relapse in a univariate analysis. On multivariate analysis, only Gleason score and DNA ploidy proved to be independently predictors of disease recurrence. Furthermore, among the 68 cases identified with Gleason score 7, DNA ploidy was the only significant predictor of disease recurrence. Conclusions: Our data suggest that DNA ploidy should be included as an important additive prognostic factor for prostate cancer, especially for patients identified with Gleason score 7 tumours. [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]