Image-based machine learning and deep learning in particular has recently shown expert-level accuracy in medical image classification. In this study, we combine convolutional and recurrent architectures to train a deep network to predict colorectal cancer outcome based on images of tumour tissue samples. The novelty of our approach is that we directly predict patient outcome, without any intermediate tissue classification. We evaluate a set of digitized haematoxylin-eosin-stained tumour tissue microarray (TMA) samples from 420 colorectal cancer patients with clinicopathological and outcome data available. The results show that deep learning-based outcome prediction with only small tissue areas as input outperforms (hazard ratio 2.3; CI 95% 1.79–3.03; AUC 0.69) visual histological assessment performed by human experts on both TMA spot (HR 1.67; CI 95% 1.28–2.19; AUC 0.58) and whole-slide level (HR 1.65; CI 95% 1.30–2.15; AUC 0.57) in the stratification into low- and high-risk patients. Our results suggest that state-of-the-art deep learning techniques can extract more prognostic information from the tissue morphology of colorectal cancer than an experienced human observer.
BackgroundWe conducted a phase I study with a granulocyte macrophage colony stimulating factor (GMCSF)-expressing oncolytic adenovirus, ONCOS-102, in patients with solid tumors refractory to available treatments. The objectives of the study were to determine the optimal dose for further use and to assess the safety, tolerability and adverse event (AE) profile of ONCOS-102. Further, the response rate and overall survival were evaluated as well as preliminary evidence of disease control. As an exploratory endpoint, the effect of ONCOS 102 on biological correlates was examined.MethodsThe study was conducted using a classic 3 + 3 dose escalation study design involving 12 patients. Patients were repeatedly treated intratumorally with ONCOS-102 plus daily low-dose oral cyclophosphamide (CPO). Tumor response was evaluated with diagnostic positron emission tomography (PET) and computed tomography (CT). Tumor biopsies were collected at baseline and after treatment initiation for analysis of immunological correlates. Peripheral blood mononuclear cells (PBMCs) were collected at baseline and during the study to assess antigen specificity of CD8+ T cells by interferon gamma (IFNγ) enzyme linked immunospot assay (ELISPOT).ResultsNo dose limiting toxicity (DLT) or maximum tolerated dose (MTD) was identified for ONCOS-102. Four out of ten (40 %) evaluable patients had disease control based on PET/CT scan at 3 months and median overall survival was 9.3 months. A short-term increase in systemic pro-inflammatory cytokines and a prominent infiltration of TILs to tumors was seen post-treatment in 11 out of 12 patients. Two patients showed marked infiltration of CD8+ T cells to tumors and concomitant systemic induction of tumor-specific CD8+ T cells. Interestingly, high expression levels of genes associated with activated TH1 cells and TH1 type immune profile were observed in the post-treatment biopsies of these two patients.ConclusionsONCOS-102 is safe and well tolerated at the tested doses. All three examined doses may be used in further development. There was evidence of antitumor immunity and signals of clinical efficacy. Importantly, treatment resulted in infiltration of CD8+ T cells to tumors and up-regulation of PD-L1, highlighting the potential of ONCOS-102 as an immunosensitizing agent for combinatory therapies with checkpoint inhibitors.Trial registrationNCT01598129. Registered 19/04/2012
The paradigm of molecular histopathology is shifting from a single-marker immunohistochemistry towards multiplexed detection of markers to better understand the complex pathological processes. However, there are no systems allowing multiplexed IHC (mIHC) with high-resolution whole-slide tissue imaging and analysis, yet providing feasible throughput for routine use. We present an mIHC platform combining fluorescent and chromogenic staining with automated whole-slide imaging and integrated whole-slide image analysis, enabling simultaneous detection of six protein markers and nuclei, and automatic quantification and classification of hundreds of thousands of cells in situ in formalin-fixed paraffin-embedded tissues. In the first proof-of-concept, we detected immune cells at cell-level resolution (n = 128,894 cells) in human prostate cancer, and analysed T cell subpopulations in different tumour compartments (epithelium vs. stroma). In the second proof-of-concept, we demonstrated an automatic classification of epithelial cell populations (n = 83,558) and glands (benign vs. cancer) in prostate cancer with simultaneous analysis of androgen receptor (AR) and alpha-methylacyl-CoA (AMACR) expression at cell-level resolution. We conclude that the open-source combination of 8-plex mIHC detection, whole-slide image acquisition and analysis provides a robust tool allowing quantitative, spatially resolved whole-slide tissue cytometry directly in formalin-fixed human tumour tissues for improved characterization of histology and the tumour microenvironment.
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