Next Article in Journal
Report on Late Toxicity in Head-and-Neck Tumor Patients with Long Term Survival after Radiochemotherapy
Next Article in Special Issue
Immune-Omics Networks of CD27, PD1, and PDL1 in Non-Small Cell Lung Cancer
Previous Article in Journal
Combination of Pembrolizumab with Electrochemotherapy in Cutaneous Metastases from Melanoma: A Comparative Retrospective Study from the InspECT and Slovenian Cancer Registry
Previous Article in Special Issue
TP53 Mutation as a Prognostic and Predictive Marker in Sarcoma: Pooled Analysis of MOSCATO and ProfiLER Precision Medicine Trials

PTEN and DNA Ploidy Status by Machine Learning in Prostate Cancer

Institute for Cancer Genetics and Informatics, Oslo University Hospital, NO-0424 Oslo, Norway
Department of Informatics, University of Oslo, NO-0316 Oslo, Norway
Department of Urology, Oslo University Hospital, NO-0424 Oslo, Norway
Department of Urology, Vestfold Hospital Trust, NO-3103 Tønsberg, Norway
Nuffield Division of Clinical Laboratory Sciences, University of Oxford, Oxford OX3 9DU, UK
Author to whom correspondence should be addressed.
Academic Editor: Constantin N. Baxevanis
Cancers 2021, 13(17), 4291;
Received: 3 August 2021 / Revised: 23 August 2021 / Accepted: 24 August 2021 / Published: 26 August 2021
(This article belongs to the Special Issue Biomarkers in the Era of Precision Oncology)
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. View Full-Text
Keywords: machine learning; prostate cancer; PTEN; DNA ploidy; tumor heterogeneity machine learning; prostate cancer; PTEN; DNA ploidy; tumor heterogeneity
Show Figures

Figure 1

MDPI and ACS Style

Cyll, K.; Kleppe, A.; Kalsnes, J.; Vlatkovic, L.; Pradhan, M.; Kildal, W.; Tobin, K.A.R.; Reine, T.M.; Wæhre, H.; Brennhovd, B.; Askautrud, H.A.; Skaaheim Haug, E.; Hveem, T.S.; Danielsen, H.E. PTEN and DNA Ploidy Status by Machine Learning in Prostate Cancer. Cancers 2021, 13, 4291.

AMA Style

Cyll K, Kleppe A, Kalsnes J, Vlatkovic L, Pradhan M, Kildal W, Tobin KAR, Reine TM, Wæhre H, Brennhovd B, Askautrud HA, Skaaheim Haug E, Hveem TS, Danielsen HE. PTEN and DNA Ploidy Status by Machine Learning in Prostate Cancer. Cancers. 2021; 13(17):4291.

Chicago/Turabian Style

Cyll, Karolina, Andreas Kleppe, Joakim Kalsnes, Ljiljana Vlatkovic, Manohar Pradhan, Wanja Kildal, Kari Anne R. Tobin, Trine M. Reine, Håkon Wæhre, Bjørn Brennhovd, Hanne A. Askautrud, Erik Skaaheim Haug, Tarjei S. Hveem, and Håvard E. Danielsen. 2021. "PTEN and DNA Ploidy Status by Machine Learning in Prostate Cancer" Cancers 13, no. 17: 4291.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop