This study reports on probing the utility of in situ chromatin texture features such as nuclear DNA methylation and chromatin condensation patterns — visualized by fluorescent staining and evaluated by dedicated three-dimensional (3D) quantitative and high-throughput cell-by-cell image analysis — in assessing the proliferative capacity, i.e. growth behavior of cells: to provide a more dynamic picture of a cell population with potential implications in basic science, cancer diagnostics/prognostics and therapeutic drug development. Two types of primary cells and four different cancer cell lines were propagated and subjected to cell-counting, flow cytometry, confocal imaging, and 3D image analysis at various points in culture. Additionally a subset of primary and cancer cells was accelerated into senescence by oxidative stress. DNA methylation and chromatin condensation levels decreased with declining doubling times when primary cells aged in culture with the lowest levels reached at the stage of proliferative senescence. In comparison, immortal cancer cells with constant but higher doubling times mostly displayed lower and constant levels of the two in situ-derived features. However, stress-induced senescent primary and cancer cells showed similar levels of these features compared with primary cells that had reached natural growth arrest. With regards to global DNA methylation and chromatin condensation levels, aggressively growing cancer cells seem to take an intermediate level between normally proliferating and senescent cells. Thus, normal cells apparently reach cancer-cell equivalent stages of the two parameters at some point in aging, which might challenge phenotypic distinction between these two types of cells. Companion high-resolution molecular profiling could provide information on possible underlying differences that would explain benign versus malign cell growth behaviors.

BackgroundThe prognosis of patients with pancreatic neuroendocrine tumors (PanNET), the second most common type of pancreatic cancer, varies significantly, and up to 15% of patients develop metastasis. Although certain morphological characteristics of PanNETs have been associated with patient outcome, there are no available morphology-based prognostic markers. Given that current clinical histopathology markers are unable to identify high-risk PanNET patients, the development of accurate prognostic biomarkers is needed. Here, we describe a novel machine learning, multiclassification pipeline to predict the risk of metastasis using morphological information from whole tissue slides.MethodsDigital images from surgically resected tissues from 89 PanNET patients were used. Pathologist-annotated regions were extracted to train a convolutional neural network (CNN) to identify tiles consisting of PanNET, stroma, normal pancreas parenchyma, and fat. Computationally annotated cancer or stroma tiles and patient metastasis status were used to train CNN to calculate a region based metastatic risk score. Aggregation of the metastatic probability scores across the slide was performed to predict the risk of metastasis.ResultsThe ability of CNN to discriminate different tissues was high (per-tile accuracy >95%; whole slide cancer regions Jaccard index = 79%). Cancer and stromal tiles with high evaluated probability provided F1 scores of 0.82 and 0.69, respectively, when we compared tissues from patients who developed metastasis and those who did not. The final model identified low-risk (n = 76) and high-risk (n = 13) patients, as well as predicted metastasis-free survival (hazard ratio: 4.71) after adjusting for common clinicopathological variables, especially in grade I/II patients.ConclusionUsing slides from surgically resected PanNETs, our novel, multiclassification, deep learning pipeline was able to predict the risk of metastasis in PanNET patients. Our results suggest the presence of prognostic morphological patterns in PanNET tissues, and that these patterns may help guide clinical decision making.

The limited clinical success of anti‐HGF/MET drugs can be attributed to the lack of predictive biomarkers that adequately select patients for treatment. We demonstrate here that quantitative digital imaging of formalin fixed paraffin embedded tissues stained by immunohistochemistry can be used to measure signals from weakly staining antibodies and provides new opportunities to develop assays for detection of MET receptor activity. To establish a biomarker panel of MET activation, we employed seven antibodies measuring protein expression in the HGF/MET pathway in 20 cases and up to 80 cores from 18 human cancer types. The antibodies bind to epitopes in the extra (EC)‐ and intracellular (IC) domains of MET (MET4EC, SP44_METIC, D1C2_METIC), to MET‐pY1234/pY1235, a marker of MET kinase activation, as well as to HGF, pSFK or pMAPK. Expression of HGF was determined in tumour cells (T_HGF) as well as in stroma surrounding cancer (St_HGF). Remarkably, MET4EC correlated more strongly with pMET (r = 0.47) than SP44_METIC (r = 0.21) or D1C2_METIC (r = 0.08) across 18 cancer types. In addition, correlation coefficients of pMET and T_HGF (r = 0.38) and pMET and pSFK (r = 0.56) were high. Prediction models of MET activation reveal cancer‐type specific differences in performance of MET4EC, SP44_METIC and anti‐HGF antibodies. Thus, we conclude that assays to predict the response to HGF/MET inhibitors require a cancer‐type specific antibody selection and should be developed in those cancer types in which they are employed clinically.