<i>Timp3</i> Deficient Mice Show Resistance to Developing Breast Cancer

<div><p><i>Timp3</i> is commonly silenced in breast cancer, but mechanistic studies have identified both tumor promotion and suppression effects of this gene. We have taken a genetic approach to determine the impact of <i>Timp3</i> loss on two mouse models of breast cancer. Interestingly, MMTV-PyMT <i>Timp3^−⁄−</i> mice have delayed tumor onset and 36% of MMTV-Neu <i>Timp3^−⁄−</i> mice remain tumor free. TIMP3 is a regulator of TNF signaling and similar to <i>Timp3</i>, <i>Tnf</i> or <i>Tnfr1</i> loss delays early tumorigenesis. The tumor suppression in <i>Timp3</i> null mice requires <i>Tnfr1</i>, but does not result in alterations in the local immune compartment. In the mammary gland, <i>Timp</i>s are highly expressed in the stroma and through the transplantation of tumor cells we observe that <i>Timp3</i> deficiency in the host is sufficient to delay the growth of early, but not advanced tumor cells. Together our data is the first to identify a tumor promoting role of endogenous <i>Timp3 in vivo</i>, the spatial and temporal windows of this effect, and its dependence on <i>Tnfr1</i>.</p></div

<i>Tnfr1</i> is required for tumor suppression in the absence of <i>Timp3</i>.

<p><b>a</b>) Kaplan-Meier curve of the age at first detection of MMTV-PyMT positive WT (<i>Timp3</i>^<i>+/+</i>, n = 15), <i>Timp3</i>^{<i>−⁄−</i>} (n = 16), <i>Tnf</i>^{<i>−⁄−</i>} (n = 12) and <i>Timp3</i>^{<i>−⁄−</i>}<i>Tnf</i>^{<i>−⁄−</i>} (n = 5) mammary tumors. <b>b</b>) Kaplan-Meier curve of the age at first detection of MMTV-PyMT positive WT (<i>Timp3</i>^<i>+/+</i>), <i>Timp3</i>^{<i>−⁄−</i>}, <i>Tnfr1</i>^{<i>−⁄−</i>} (n = 23) and <i>Timp3</i>^{<i>−⁄−</i>}<i>Tnfr1</i>^{<i>−⁄−</i>} (n = 7) mammary tumors. <b>c</b>) Histogram of the number of days to first palpation and from first palpation to tumor endpoint. <b>d</b>) Representative H&E images of endpoint MMTV-PyMT mammary tumors.</p

Loss of <i>Timp3</i> suppresses mammary tumorigenesis.

<p><b>a</b>) Kaplan-Meier curve of the age at first detection of MMTV-PyMT positive <i>Timp3</i>^<i>+/+</i> (n = 25), <i>Timp3</i>^<i>+/-</i>(n = 17), and <i>Timp3</i>^{<i>−⁄−</i>} (n = 21) mammary tumors. <b>b</b>) Kaplan-Meier curve of the age at first detection of MMTV-Neu positive <i>Timp3</i>^<i>+/+</i>(n = 21), <i>Timp3</i>^<i>+/−</i> (n = 20), and <i>Timp3</i>^{<i>−⁄−</i>} (n = 22) mammary tumors. <b>c</b>) Kaplan-Meier curve of the age at first detection of MMTV-PyMT positive <i>Timp3</i>^<i>+/+</i>, <i>Timp3</i>^{<i>−⁄−</i>}, and <i>Timp1</i>^{<i>−⁄−</i>}<i>Timp2</i>^{<i>−⁄−</i>}<i>Timp4</i>^{<i>−⁄−</i>} mammary tumors. Tumor burden at Day 80 depicted by mammary gland to body weight ratios (<b>d</b>, n = 17, mean ± s.e.m), the total number of palpable tumors (<b>e</b>, n = 17, mean ± s.e.m), representative images of mammary wholemounts (<b>f</b>, scale bar 5mm), and representative images of Bouin’s stained lungs with quantification of macroscopic metastasis (<b>g</b>, line = median). *p<0.05, **p>0.01, ***p<0.001.</p

<i>Timp3</i> deficiency in the host delays tumor progression.

<p><b>a</b>) Representative flow plots of sorted luminal, basal and stromal mammary populations. <b>b</b>) RT-PCR of <i>Timp</i> gene expression in sorted populations. <b>c</b>) Kaplan-Meier curve of the age at first detection of transplanted MMTV-PyMT positive Day 40 (straight) and Day 60 (dotted) <i>Timp3</i>^<i>+/+</i> cells into <i>Timp3</i>^<i>+/+</i> (white) and <i>Timp3</i>^<i>-/-</i> (black) hosts.</p

The loss of <i>Timp3</i> does not alter the mammary gland immune compartment.

<p>Representative flow plots and quantification of (<b>a</b>) epithelial luminal and basal populations, (<b>b</b>) CD45+ immune cells, (<b>c</b>) NK and T cells, (<b>d</b>) CD4⁺ and CD8⁺ T cell subsets, (<b>e</b>) B cells, and (<b>f</b>) macrophages; n = 7, mean ± s.e.m.</p

piNET–An Automated Proliferation Index Calculator Framework for Ki67 Breast Cancer Images

In this work, a novel proliferation index (PI) calculator for Ki67 images called piNET is proposed. It is successfully tested on four datasets, from three scanners comprised of patches, tissue microarrays (TMAs) and whole slide images (WSI), representing a diverse multi-centre dataset for evaluating Ki67 quantification. Compared to state-of-the-art methods, piNET consistently performs the best over all datasets with an average PI difference of 5.603%, PI accuracy rate of 86% and correlation coefficient R = 0.927. The success of the system can be attributed to several innovations. Firstly, this tool is built based on deep learning, which can adapt to wide variability of medical images—and it was posed as a detection problem to mimic pathologists’ workflow which improves accuracy and efficiency. Secondly, the system is trained purely on tumor cells, which reduces false positives from non-tumor cells without needing the usual pre-requisite tumor segmentation step for Ki67quantification. Thirdly,theconceptoflearningbackgroundregionsthroughweaksupervisionis introduced, by providing the system with ideal and non-ideal (artifact) patches that further reduces false positives. Lastly, a novel hotspot analysis is proposed to allow automated methods to score patches from WSI that contain “significant” activity.</p

IHC Color Histograms for Unsupervised Ki67 Proliferation Index Calculation

Automated image analysis tools for Ki67 breast cancer digital pathology images would have significant value if integrated into diagnostic pathology workflows. Such tools would reduce the workload of pathologists, while improving efficiency, and accuracy. Developing tools that are robust and reliable to multicentre data is challenging, however, differences in staining protocols, digitization equipment, staining compounds, and slide preparation can create variabilities in image quality and color across digital pathology datasets. In this work, a novel unsupervised color separation framework based on the IHC color histogram (IHCCH) is proposed for the robust analysis of Ki67 and hematoxylin stained images in multicentre datasets. An “overstaining” threshold is implemented to adjustforbackgroundoverstaining,andanautomatednucleiradiusestimatorisdesigned to improve nuclei detection. Proliferation index and F1 scores were compared between the proposed method and manually labeled ground truth data for 30 TMA cores that have ground truths for Ki67+ and Ki67− nuclei. The method accurately quantified the PI over the dataset, with an average proliferation index difference of 3.25%. To ensure the method generalizes to new, diverse datasets, 50 Ki67 TMAs from the Protein Atlas were used to test the validated approach. As the ground truth for this dataset is PI ranges, the automated result was compared to the PI range. The proposed method correctly classified 74 out of 80 TMA images, resulting in a 92.5% accuracy. In addition to these validations experiments, performance was compared to two color-deconvolution based methods, and to six machine learning classifiers. In all cases, the proposed work maintained more consistent (reproducible) results, and higher PI quantification accuracy. </p

The POZ-ZF Transcription Factor Kaiso (ZBTB33) Induces Inflammation and Progenitor Cell Differentiation in the Murine Intestine

<div><p>Since its discovery, several studies have implicated the POZ-ZF protein Kaiso in both developmental and tumorigenic processes. However, most of the information regarding Kaiso’s function to date has been gleaned from studies in <i>Xenopus laevis</i> embryos and mammalian cultured cells. To examine Kaiso’s role in a relevant, mammalian organ-specific context, we generated and characterized a Kaiso transgenic mouse expressing a murine Kaiso transgene under the control of the intestine-specific <i>villin</i> promoter. Kaiso transgenic mice were viable and fertile but pathological examination of the small intestine revealed distinct morphological changes. Kaiso transgenics (<i>Kaiso^Tg/+</i>) exhibited a crypt expansion phenotype that was accompanied by increased differentiation of epithelial progenitor cells into secretory cell lineages; this was evidenced by increased cell populations expressing Goblet, Paneth and enteroendocrine markers. Paradoxically however, enhanced differentiation in <i>Kaiso^Tg/+</i> was accompanied by reduced proliferation, a phenotype reminiscent of Notch inhibition. Indeed, expression of the Notch signalling target HES-1 was decreased in <i>Kaiso^Tg/+</i> animals. Finally, our Kaiso transgenics exhibited several hallmarks of inflammation, including increased neutrophil infiltration and activation, villi fusion and crypt hyperplasia. Interestingly, the Kaiso binding partner and emerging anti-inflammatory mediator p120^ctn is recruited to the nucleus in <i>Kaiso^Tg/+</i> mice intestinal cells suggesting that Kaiso may elicit inflammation by antagonizing p120^ctn function.</p></div

Generation of transgenic mouse lines ectopically expressing <i>villin</i>-Kaiso.

<p>(<b>A</b>) Myc-tagged murine <i>Kaiso</i> cDNA was cloned downstream of the 9 kb v<i>illin</i> promoter sequence. (<b>B</b>) The transgene copy number in each transgenic line was evaluated via PCR. Line A transgenic animals have the greatest copy number. (<b>C</b>) RT-PCR confirmed expression of the Kaiso transgene in <i>villin</i>-expressing tissues of transgenic mice, <i>i.e.</i> the small intestine, large intestine, and kidneys. (<b>D</b>) Immunoblot analysis shows increased Kaiso expression in both small and large intestines in Kaiso transgenic (<i>Kaiso^Tg</i>^/+) Line A mice compared to non-transgenic (Non-Tg) siblings.</p

Kaiso transgenic mice exhibit inflammation of the intestinal mucosa.

<p>(<b>A</b>) <b><u>H</u></b>ematoxylin and <b><u>e</u></b>osin (H&E) stained sections were used to measure villi length (red bracket; ∼80 villi/mouse) and crypt depth (black bracket; ∼800 open crypts/mouse). <i>Kaiso^Tg</i>^/+ display increased crypt depth compared to their Non-Tg siblings, p = 0.001. (<b>B</b>) <i>Kaiso^Tg</i>^/+ mice exhibit increased immune cell infiltration of the lamina propria (yellow demarcated area) accompanied by increased MPO activity compared to their Non-Tg siblings, p = 0.014. (<b>C</b>) Line B mice do not exhibit immune cell infiltration or enhanced MPO activity compared to Non-Tg siblings. ** represents significance.</p