20 research outputs found
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Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study
BACKGROUND: For virtually every patient with colorectal cancer (CRC), hematoxylin-eosin (HE)-stained tissue slides are available. These images contain quantitative information, which is not routinely used to objectively extract prognostic biomarkers. In the present study, we investigated whether deep convolutional neural networks (CNNs) can extract prognosticators directly from these widely available images.
METHODS AND FINDINGS: We hand-delineated single-tissue regions in 86 CRC tissue slides, yielding more than 100,000 HE image patches, and used these to train a CNN by transfer learning, reaching a nine-class accuracy of >94% in an independent data set of 7,180 images from 25 CRC patients. With this tool, we performed automated tissue decomposition of representative multitissue HE images from 862 HE slides in 500 stage I-IV CRC patients in the The Cancer Genome Atlas (TCGA) cohort, a large international multicenter collection of CRC tissue. Based on the output neuron activations in the CNN, we calculated a "deep stroma score," which was an independent prognostic factor for overall survival (OS) in a multivariable Cox proportional hazard model (hazard ratio [HR] with 95% confidence interval [CI]: 1.99 [1.27-3.12], p = 0.0028), while in the same cohort, manual quantification of stromal areas and a gene expression signature of cancer-associated fibroblasts (CAFs) were only prognostic in specific tumor stages. We validated these findings in an independent cohort of 409 stage I-IV CRC patients from the "Darmkrebs: Chancen der Verhütung durch Screening" (DACHS) study who were recruited between 2003 and 2007 in multiple institutions in Germany. Again, the score was an independent prognostic factor for OS (HR 1.63 [1.14-2.33], p = 0.008), CRC-specific OS (HR 2.29 [1.5-3.48], p = 0.0004), and relapse-free survival (RFS; HR 1.92 [1.34-2.76], p = 0.0004). A prospective validation is required before this biomarker can be implemented in clinical workflows.
CONCLUSIONS: In our retrospective study, we show that a CNN can assess the human tumor microenvironment and predict prognosis directly from histopathological images
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Large-scale database mining reveals hidden trends and future directions for cancer immunotherapy
LLC Cancer immunotherapy has fundamentally changed the landscape of oncology in recent years and significant resources are invested into immunotherapy research. It is in the interests of researchers and clinicians to identify promising and less promising trends in this field in order to rationally allocate resources. This requires a quantitative large-scale analysis of cancer immunotherapy related databases. We developed a novel tool for text mining, statistical analysis and data visualization of scientific literature data. We used this tool to analyze 72002 cancer immunotherapy publications and 1469 clinical trials from public databases. All source codes are available under an open access license. The contribution of specific topics within the cancer immunotherapy field has markedly shifted over the years. We show that the focus is moving from cell-based therapy and vaccination towards checkpoint inhibitors, with these trends reaching statistical significance. Rapidly growing subfields include the combination of chemotherapy with checkpoint blockade. Translational studies have shifted from hematological and skin neoplasms to gastrointestinal and lung cancer and from tumor antigens and angiogenesis to tumor stroma and apoptosis. This work highlights the importance of unbiased large-scale database mining to assess trends in cancer research and cancer immunotherapy in particular. Researchers, clinicians and funding agencies should be aware of quantitative trends in the immunotherapy field, allocate resources to the most promising areas and find new approaches for currently immature topics
Intestinal BMP-9 locally upregulates FGF19 and is down-regulated in obese patients with diabetes
believed to be mainly produced in the liver. The serum levels of BMP-9 were reported to be reduced in newly diagnosed diabetic patients and BMP-9 overexpression ameliorated steatosis in the high fat diet-induced obesity mouse model. Furthermore, injection of BMP-9 in mice enhanced expression of fibroblast growth factor (FGF)21. However, whether BMP-9 also regulates the expression of the related FGF19 is not clear. Because both FGF21 and 19 were described to protect the liver from steatosis, we have further investigated the role of BMP-9 in this context.
We first analyzed BMP-9 levels in the serum of streptozotocin (STZ)-induced diabetic rats (a model of type I diabetes) and confirmed that BMP-9 serum levels decrease during diabetes. Microarray analyses of RNA samples from hepatic and intestinal tissue from BMP-9 KO- and wild-type mice (C57/Bl6 background) pointed to basal expression of BMP-9 in both organs and revealed a down-regulation of hepatic Fgf21 and intestinal Fgf19 in the KO mice. Next, we analyzed BMP-9 levels in a cohort of obese patients with or without diabetes. Serum BMP-9 levels did not correlate with diabetes, but hepatic BMP-9 mRNA expression negatively correlated with steatosis in those patients that did not yet develop diabetes. Likewise, hepatic BMP-9 expression also negatively correlated with serum LPS levels. In situ hybridization analyses confirmed intestinal BMP-9 expression. Intestinal (but not hepatic) BMP-9 mRNA levels were decreased with diabetes and positively correlated with intestinal E-Cadherin expression. In vitro studies using organoids demonstrated that BMP-9 directly induces FGF19 in gut but not hepatocyte organoids, whereas no evidence of a direct induction of hepatic FGF21 by BMP-9 was found. Consistent with the in vitro data, a correlation between intestinal BMP-9 and FGF19 mRNA expression was seen in the patients’ samples.
In summary, our data confirm that BMP-9 is involved in diabetes development in humans and in the control of the FGF-axis. More importantly, our data imply that not only hepatic but also intestinal BMP-9 associates with diabetes and steatosis development and controls FGF19 expression. The data support the conclusion that increased levels of BMP-9 would most likely be beneficial under pre-steatotic conditions, making supplementation of BMP-9 an interesting new approach for future therapies aiming at prevention of the development of a metabolic syndrome and liver steatosis