Ki67 is a Graded Rather than a Binary Marker of Proliferation versus Quiescence.
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ABSTRACT:
SUBMITTER: Miller I
PROVIDER: S-EPMC6108547 | biostudies-literature | 2018 Jul
REPOSITORIES: biostudies-literature
Publications
Ki67 is a Graded Rather than a Binary Marker of Proliferation versus Quiescence.
Miller Iain I Min Mingwei M Yang Chen C Tian Chengzhe C Gookin Sara S Carter Dylan D Spencer Sabrina L SL
Cell reports 20180701 5
Ki67 staining is widely used as a proliferation indicator in the clinic, despite poor understanding of this protein's function or dynamics. Here, we track Ki67 levels under endogenous control in single cells over time and find that Ki67 accumulation occurs only during S, G2, and M phases. Ki67 is degraded continuously in G1 and G0 phases, regardless of the cause of entry into G0/quiescence. Consequently, the level of Ki67 during G0 and G1 in individual cells is highly heterogeneous and depends o ...[more]
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Project description:Uncontrolled proliferation is a hallmark of cancer and can be assessed by labelling breast tissue using immunohistochemistry for Ki67, a protein associated with cell proliferation. Accurate measurement of Ki67-positive tumour nuclei is of critical importance, but requires annotation of the tumour regions by a pathologist. This manual annotation process is highly subjective, time-consuming and subject to inter- and intra-annotator experience. To address this challenge, we have developed Proliferation Tumour Marker Network (PTM-NET), a deep learning model that objectively annotates the tumour regions in Ki67-labelled breast cancer digital pathology images using a convolution neural network. Our custom designed deep learning model was trained on 45 immunohistochemical Ki67-labelled whole slide images to classify tumour and non-tumour regions and was validated on 45 whole slide images from two different sources that were stained using different protocols. Our results show a Dice coefficient of 0.74, positive predictive value of 70% and negative predictive value of 88.3% against the manual ground truth annotation for the combined dataset. There were minimal differences between the images from different sources and the model was further tested in oestrogen receptor and progesterone receptor-labelled images. Finally, using an extension of the model, we could identify possible hotspot regions of high proliferation within the tumour. In the future, this approach could be useful in identifying tumour regions in biopsy samples and tissue microarray images.
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