This is a repository copy of Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer.

BackgroundFor 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 findingsWe 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.ConclusionsIn our retrospective study, we show that a CNN can assess the human tumor microenvironment and predict prognosis directly from histopathological images.

This is a repository copy of Pan-cancer image-based detection of clinically actionable genetic alterations.

Clinical workflows in oncology rely on predictive and prognostic molecular biomarkers. However, the growing number of these complex biomarkers tends to increase the cost and time for decision-making in routine daily oncology practice; furthermore, biomarkers often require tumour tissue on top of routine diagnostic material. Nevertheless, routinely available tumour tissue contains an abundance of clinically relevant information that is currently not fully exploited. Advances in deep learning (DL), an artificial intelligence (AI) technology, have enabled the extraction of previously hidden information directly from routine histology images of cancer, providing potentially clinically useful information. Here, we outline emerging concepts of how DL can extract biomarkers directly from histology images and summarise studies of basic and advanced image analysis for cancer histology. Basic image analysis tasks include detection, grading and subtyping of tumour tissue in histology images; they are aimed at automating pathology workflows and consequently do not immediately translate into clinical decisions. Exceeding such basic approaches, DL has also been used for advanced image analysis tasks, which have the potential of directly affecting clinical decision-making processes. These advanced approaches include inference of molecular features, prediction of survival and end-to-end prediction of therapy response. Predictions made by such DL systems could simplify and enrich clinical decision-making, but require rigorous external validation in clinical settings.

and tluedde@ukaachen.de 34 35 Precision treatment of cancer relies on genetic alterations which are diagnosed by molecular 36 biology assays. 1 These tests can be a bottleneck in oncology workflows because of high turna-37 round time, tissue usage and costs. 2 Here, we show that deep learning can predict point muta-38 tions, molecular tumor subtypes and immune-related gene expression signatures 3,4 directly 39 from routine histological images of tumor tissue. We developed and systematically optimized 40 a one-stop-shop workflow and applied it to more than 4000 patients with breast 5 , colon and 41 rectal 6 , head and neck 7 , lung 8,9 , pancreatic 10 , prostate 11 cancer, melanoma 12 and gastric 13 can-42 cer. Together, our findings show that a single deep learning algorithm can predict clinically ac-43 tionable alterations from routine histology data. Our method can be implemented on mobile 44 hardware 14 , potentially enabling point-of-care diagnostics for personalized cancer treatment 45 in individual patients. 46 Clinical guidelines recommend molecular testing of tumor tissue for most patients with advanced 47 209 The results are in part based upon data generated by the TCGA Research Network: http://can-210 cergenome.nih.gov/. Our funding sources are as follows. J.N.K.: RWTH University Aachen (START 211

Lymphoid and myeloid cells are abundant in the tumor microenvironment, can be quantified by immunohistochemistry and shape the disease course of human solid tumors. Yet, there is no comprehensive understanding of spatial immune infiltration patterns (‘topography’) across cancer entities and across various immune cell types. In this study, we systematically measure the topography of multiple immune cell types in 965 histological tissue slides from N = 177 patients in a pan-cancer cohort. We provide a definition of inflamed (‘hot’), non-inflamed (‘cold’) and immune excluded patterns and investigate how these patterns differ between immune cell types and between cancer types. In an independent cohort of N = 287 colorectal cancer patients, we show that hot, cold and excluded topographies for effector lymphocytes (CD8) and tumor-associated macrophages (CD163) alone are not prognostic, but that a bivariate classification system can stratify patients. Our study adds evidence to consider immune topographies as biomarkers for patients with solid tumors.

Colorectal cancer (CRC) is a common and lethal disease with a high therapeutic need. For most patients with metastatic CRC, chemotherapy is the only viable option. Currently, immunotherapy is restricted to the particular genetic subgroup of mismatch-repair deficient (MMRd)/microsatellite instable (MSI) CRC. Anti-PD1 therapy was recently FDA-approved as a second-line treatment in this subgroup. However, in a metastatic setting, these MMRd/MSI tumors are vastly outnumbered by mismatch-repair proficient (MMRp)/microsatellite stable (MSS) tumors. These MMRp/MSS tumors do not meaningfully respond to any traditional immunotherapy approach including checkpoint blockade, adoptive cell transfer and vaccination. This resistance to immunotherapy is due to a complex tumor microenvironment that counteracts antitumor immunity through a combination of poorly antigenic tumor cells and an immunosuppressive tumor microenvironment. To find ways of overcoming immunotherapy resistance in the majority of CRC patients, it is necessary to analyze the immunological makeup in an in-depth and personalized way and in the context of their tumor genetic makeup. Flexible, biomarker-guided early-phase immunotherapy trials are needed to optimize this workflow. In this review, we detail key mechanisms for immune evasion and emerging immune biomarkers for personalized immunotherapy in CRC. Also, we present a template for biomarker-guided clinical trials that are needed to move new immunotherapy approaches closer to clinical application.

Despite the fact that the local immunological microenvironment shapes the prognosis of colorectal cancer, immunotherapy has shown no benefit for the vast majority of colorectal cancer patients. A better understanding of the complex immunological interplay within the microenvironment is required. In this study, we utilized wet lab migration experiments and quantitative histological data of human colorectal cancer tissue samples (n ¼ 20) including tumor cells, lymphocytes, stroma, and necrosis to generate a multiagent spatial model. The resulting data accurately reflected a wide range of situations of successful and failed immune surveillance. Validation of simulated tissue outcomes on an independent set of human colorectal cancer specimens (n ¼ 37) revealed the model recapitulated the spatial layout typically found in human tumors. Stroma slowed down tumor growth in a lymphocyte-deprived environment but promoted immune escape in a lymphocyteenriched environment. A subgroup of tumors with less stroma and high numbers of immune cells showed high rates of tumor control. These findings were validated using data from colorectal cancer patients (n ¼ 261). Low-density stroma and high lymphocyte levels showed increased overall survival (hazard ratio 0.322, P ¼ 0.0219) as compared with high stroma and high lymphocyte levels. To guide immunotherapy in colorectal cancer, simulation of immunotherapy in preestablished tumors showed that a complex landscape with optimal stroma permeabilization and immune cell activation is able to markedly increase therapy response in silico. These results can help guide the rational design of complex therapeutic interventions, which target the colorectal cancer microenvironment.