Deep Learning-Based Image Analysis of Liver Steatosis in Mouse Models.

The incidence of nonalcoholic fatty liver disease is a continuously growing health problem worldwide, along with obesity. Therefore, novel methods to both efficiently study the manifestation of nonalcoholic fatty liver disease and to analyze drug efficacy in preclinical models are needed. The present study developed a deep neural network-based model to quantify microvesicular and macrovesicular steatosis in the liver on hematoxylin-eosin-stained whole slide images, using the cloud-based platform, Aiforia Create. The training data included a total of 101 whole slide images from dietary interventions of wild-type mice and from two genetically modified mouse models with steatosis. The algorithm was trained for the following: to detect liver parenchyma, to exclude the blood vessels and any artefacts generated during tissue processing and image acquisition, to recognize and differentiate the areas of microvesicular and macrovesicular steatosis, and to quantify the recognized tissue area. The results of the image analysis replicated well the evaluation by expert pathologists and correlated well with the liver fat content measured by EchoMRI ex vivo, and the correlation with total liver triglycerides was notable. In conclusion, the developed deep learning-based model is a novel tool for studying liver steatosis in mouse models on paraffin sections and, thus, can facilitate reliable quantification of the amount of steatosis in large preclinical study cohorts.

Automated image analysis of keratin 7 staining can predict disease outcome in primary sclerosing cholangitis.

BACKGROUND AND AIMS: Primary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease that obstructs the bile ducts and causes liver cirrhosis and cholangiocarcinoma. Efficient surrogate markers are required to measure disease progression. The cytokeratin 7 (K7) load in a liver specimen is an independent prognostic indicator that can be measured from digitalized slides using artificial intelligence (AI)-based models. METHODS: A K7-AI model 2.0 was built to measure the hepatocellular K7 load area of the parenchyma, portal tracts, and biliary epithelium. K7-stained PSC liver biopsy specimens (n = 295) were analyzed. A compound endpoint (liver transplantation, liver-related death, and cholangiocarcinoma) was applied in Kaplan-Meier survival analysis to measure AUC values and positive likelihood ratios for each histological variable detected by the model. RESULTS: The K7-AI model 2.0 was a better prognostic tool than plasma alkaline phosphatase, the fibrosis stage evaluated by Nakanuma classification, or K7 score evaluated by a pathologist based on the AUC values of measured variables. A combination of parameters, such as portal tract volume and area of K7-positive hepatocytes analyzed by the model, produced an AUC of 0.81 for predicting the compound endpoint. Portal tract volume measured by the model correlated with the histological fibrosis stage. CONCLUSIONS: The K7 staining of histological liver specimens in PSC provides significant information on disease outcomes through objective and reproducible data, including variables that cannot be measured by a human pathologist. The K7-AI model 2.0 could serve as a prognostic tool for clinical endpoints and as a surrogate marker in drug trials.

AI Model for Prostate Biopsies Predicts Cancer Survival.

An artificial intelligence (AI) algorithm for prostate cancer detection and grading was developed for clinical diagnostics on biopsies. The study cohort included 4221 scanned slides from 872 biopsy sessions at the HUS Helsinki University Hospital during 2016-2017 and a subcohort of 126 patients treated by robot-assisted radical prostatectomy (RALP) during 2016-2019. In the validation cohort (n = 391), the model detected cancer with a sensitivity of 98% and specificity of 98% (weighted kappa 0.96 compared with the pathologist's diagnosis). Algorithm-based detection of the grade area recapitulated the pathologist's grade group. The area of AI-detected cancer was associated with extra-prostatic extension (G5 OR: 48.52; 95% CI 1.11-8.33), seminal vesicle invasion (cribriform G4 OR: 2.46; 95% CI 0.15-1.7; G5 OR: 5.58; 95% CI 0.45-3.42), and lymph node involvement (cribriform G4 OR: 2.66; 95% CI 0.2-1.8; G5 OR: 4.09; 95% CI 0.22-3). Algorithm-detected grade group 3-5 prostate cancer depicted increased risk for biochemical recurrence compared with grade groups 1-2 (HR: 5.91; 95% CI 1.96-17.83). This study showed that a deep learning model not only can find and grade prostate cancer on biopsies comparably with pathologists but also can predict adverse staging and probability for recurrence after surgical treatment.

Stromal FAP Expression is Associated with MRI Visibility and Patient Survival in Prostate Cancer.

Some clinically significant prostate cancers are missed by MRI. We asked whether the tumor stroma in surgically treated localized prostate cancer lesions positive or negative with MRI are different in their cellular and molecular properties, and whether the differences are reflected to the clinical course of the disease. We profiled the stromal and immune cell composition of MRI-classified tumor lesions by applying multiplexed fluorescence IHC (mfIHC) and automated image analysis in a clinical cohort of 343 patients (cohort I). We compared stromal variables between MRI-visible lesions, invisible lesions, and benign tissue and assessed the predictive significance for biochemical recurrence (BCR) and disease-specific survival (DSS) using Cox regression and log-rank analysis. Subsequently, we carried out a prognostic validation of the identified biomarkers in a population-based cohort of 319 patients (cohort II). MRI true-positive lesions are different from benign tissue and MRI false-negative lesions in their stromal composition. CD163+ cells (macrophages) and fibroblast activation protein (FAP)+ cells were more abundant in MRI true-positive than in MRI false-negative lesions or benign areas. In MRI true-visible lesions, a high proportion of stromal FAP+ cells was associated with PTEN status and increased immune infiltration (CD8+, CD163+), and predicted elevated risk for BCR. High FAP phenotype was confirmed to be a strong indicator of poor prognosis in two independent patient cohorts using also conventional IHC. The molecular composition of the tumor stroma may determine whether early prostate lesions are detectable by MRI and associates with survival after surgical treatment. Significance: These findings may have a significant impact on clinical decision making as more radical treatments may be recommended for men with a combination of MRI-visible primary tumors and FAP+ tumor stroma.

Automated assessment of Ki-67 proliferation index in neuroendocrine tumors by deep learning.

The Ki-67 proliferation index (PI) is a prognostic factor in neuroendocrine tumors (NETs) and defines tumor grade. Analysis of Ki-67 PI requires calculation of Ki-67-positive and Ki-67-negative tumor cells, which is highly subjective. To overcome this, we developed a deep learning-based Ki-67 PI algorithm (KAI) that objectively calculates Ki-67 PI. Our study material consisted of NETs divided into training (n = 39), testing (n = 124), and validation (n = 60) series. All slides were digitized and processed in the Aiforia® Create (Aiforia Technologies, Helsinki, Finland) platform. The ICC between the pathologists and the KAI was 0.89. In 46% of the tumors, the Ki-67 PIs calculated by the pathologists and the KAI were the same. In 12% of the tumors, the Ki-67 PI calculated by the KAI was 1% lower and in 42% of the tumors on average 3% higher. The DL-based Ki-67 PI algorithm yields results similar to human observers. While the algorithm cannot replace the pathologist, it can assist in the laborious Ki-67 PI assessment of NETs. In the future, this approach could be useful in, for example, multi-center clinical trials where objective estimation of Ki-67 PI is crucial.

Development and validation of a supervised deep learning algorithm for automated whole-slide programmed death-ligand 1 tumour proportion score assessment in non-small cell lung cancer.

AIMS: Immunohistochemical programmed death-ligand 1 (PD-L1) staining to predict responsiveness to immunotherapy in patients with advanced non-small cell lung cancer (NSCLC) has several drawbacks: a robust gold standard is lacking, and there is substantial interobserver and intraobserver variance, with up to 20% discordance around cutoff points. The aim of this study was to develop a new deep learning-based PD-L1 tumour proportion score (TPS) algorithm, trained and validated on a routine diagnostic dataset of digitised PD-L1 (22C3, laboratory-developed test)-stained samples. METHODS AND RESULTS: We designed a fully supervised deep learning algorithm for whole-slide PD-L1 assessment, consisting of four sequential convolutional neural networks (CNNs), using aiforia create software. We included 199 whole slide images (WSIs) of 'routine diagnostic' histology samples from stage IV NSCLC patients, and trained the algorithm by using a training set of 60 representative cases. We validated the algorithm by comparing the algorithm TPS with the reference score in a held-out validation set. The algorithm had similar concordance with the reference score (79%) as the pathologists had with one another (75%). The intraclass coefficient was 0.96 and Cohen's κ coefficient was 0.69 for the algorithm. Around the 1% and 50% cutoff points, concordance was also similar between pathologists and the algorithm. CONCLUSIONS: We designed a new, deep learning-based PD-L1 TPS algorithm that is similarly able to assess PD-L1 expression in daily routine diagnostic cases as pathologists. Successful validation on routine diagnostic WSIs and detailed visual feedback show that this algorithm meets the requirements for functioning as a 'scoring assistant'.

Artificial intelligence-based image analysis can predict outcome in high-grade serous carcinoma via histology alone.

High-grade extrauterine serous carcinoma (HGSC) is an aggressive tumor with high rates of recurrence, frequent chemotherapy resistance, and overall 5-year survival of less than 50%. Beyond determining and confirming the diagnosis itself, pathologist review of histologic slides provides no prognostic or predictive information, which is in sharp contrast to almost all other carcinoma types. Deep-learning based image analysis has recently been able to predict outcome and/or identify morphology-based representations of underlying molecular alterations in other tumor types, such as colorectal carcinoma, lung carcinoma, breast carcinoma, and melanoma. Using a carefully stratified HGSC patient cohort consisting of women (n = 30) with similar presentations who experienced very different treatment responses (platinum free intervals of either ≤ 6 months or ≥ 18 months), we used whole slide images (WSI, n = 205) to train a convolutional neural network. The neural network was trained, in three steps, to identify morphologic regions (digital biomarkers) that are highly associating with one or the other treatment response group. We tested the classifier using a separate 22 slide test set, and 18/22 slides were correctly classified. We show that a neural network based approach can discriminate extremes in patient response to primary platinum-based chemotherapy with high sensitivity (73%) and specificity (91%). These proof-of-concept results are novel, because for the first time, prospective prognostic information is identified specifically within HGSC tumor morphology.

Chronic cholestasis detection by a novel tool: automated analysis of cytokeratin 7-stained liver specimens.

BACKGROUND: The objective was to build a novel method for automated image analysis to locate and quantify the number of cytokeratin 7 (K7)-positive hepatocytes reflecting cholestasis by applying deep learning neural networks (AI model) in a cohort of 210 liver specimens. We aimed to study the correlation between the AI model's results and disease progression. The cohort of liver biopsies which served as a model of chronic cholestatic liver disease comprised of patients diagnosed with primary sclerosing cholangitis (PSC). METHODS: In a cohort of patients with PSC identified from the PSC registry of the University Hospital of Helsinki, their K7-stained liver biopsy specimens were scored by a pathologist (human K7 score) and then digitally analyzed for K7-positive hepatocytes (K7%area). The digital analysis was by a K7-AI model created in an Aiforia Technologies cloud platform. For validation, values were human K7 score, stage of disease (Metavir and Nakunuma fibrosis score), and plasma liver enzymes indicating clinical cholestasis, all subjected to correlation analysis. RESULTS: The K7-AI model results (K7%area) correlated with the human K7 score (0.896; p < 2.2e- 16). In addition, K7%area correlated with stage of PSC (Metavir 0.446; p < 1.849e- 10 and Nakanuma 0.424; p < 4.23e- 10) and with plasma alkaline phosphatase (P-ALP) levels (0.369, p < 5.749e- 5). CONCLUSIONS: The accuracy of the AI-based analysis was comparable to that of the human K7 score. Automated quantitative image analysis correlated with stage of PSC and with P-ALP. Based on the results of the K7-AI model, we recommend K7 staining in the assessment of cholestasis by means of automated methods that provide fast (9.75 s/specimen) quantitative analysis.

Immune profiles in acute myeloid leukemia bone marrow associate with patient age, T-cell receptor clonality, and survival.

The immunologic microenvironment in various solid tumors is aberrant and correlates with clinical survival. Here, we present a comprehensive analysis of the immune environment of acute myeloid leukemia (AML) bone marrow (BM) at diagnosis. We compared the immunologic landscape of formalin-fixed paraffin-embedded BM trephine samples from AML (n = 69), chronic myeloid leukemia (CML; n = 56), and B-cell acute lymphoblastic leukemia (B-ALL) patients (n = 52) at diagnosis to controls (n = 12) with 30 immunophenotype markers using multiplex immunohistochemistry and computerized image analysis. We identified distinct immunologic profiles specific for leukemia subtypes and controls enabling accurate classification of AML (area under the curve [AUC] = 1.0), CML (AUC = 0.99), B-ALL (AUC = 0.96), and control subjects (AUC = 1.0). Interestingly, 2 major immunologic AML clusters differing in age, T-cell receptor clonality, and survival were discovered. A low proportion of regulatory T cells and pSTAT1+cMAF- monocytes were identified as novel biomarkers of superior event-free survival in intensively treated AML patients. Moreover, we demonstrated that AML BM and peripheral blood samples are dissimilar in terms of immune cell phenotypes. To conclude, our study shows that the immunologic landscape considerably varies by leukemia subtype suggesting disease-specific immunoregulation. Furthermore, the association of the AML immune microenvironment with clinical parameters suggests a rationale for including immunologic parameters to improve disease classification or even patient risk stratification.

Fibroblast as a critical stromal cell type determining prognosis in prostate cancer.

BACKGROUND: Tumor stroma associates with prostate cancer (PCa) progression, but its specific cellular composition and association to patient survival outcome have not been characterized. METHODS: We analyzed stromal composition in human PCa using multiplex immunohistochemistry and quantitative, high-resolution image analysis in two retrospective, formalin-fixed paraffin embedded observational clinical cohorts (Cohort I, n = 117; Cohort II, n = 340) using PCa-specific mortality as outcome measurement. RESULTS: A high proportion of fibroblasts associated with aggressive disease and castration-resistant prostate cancer (CRPC). In a multivariate analysis, increase in fibroblast proportion predicted poor cancer-specific outcome independently in the two clinical cohorts studied. CONCLUSIONS: Fibroblasts were the most important cell type in determining prognosis in PCa and associated with CRPC. Thus, the stromal composition could be critically important in developing diagnostic and therapeutic approaches to aggressive prostate cancer.

Immune cell constitution in bone marrow microenvironment predicts outcome in adult ALL.

As novel immunological treatments are gaining a foothold in the treatment of acute lymphoblastic leukemia (ALL), it is elemental to examine ALL immunobiology in more detail. We used multiplexed immunohistochemistry (mIHC) to study the immune contexture in adult precursor B cell ALL bone marrow (BM). In addition, we developed a multivariate risk prediction model that stratified a poor survival group based on clinical parameters and mIHC data. We analyzed BM biopsy samples of ALL patients (n = 52) and healthy controls (n = 14) using mIHC with 30 different immunophenotype markers and computerized image analysis. In ALL BM, the proportions of M1-like macrophages, granzyme B+CD57+CD8+ T cells, and CD27+ T cells were decreased, whereas the proportions of myeloid-derived suppressor cells and M2-like macrophages were increased. Also, the expression of checkpoint molecules PD1 and CTLA4 was elevated. In the multivariate model, age, platelet count, and the proportion of PD1+TIM3+ double-positive CD4+ T cells differentiated a poor survival group. These results were validated by flow cytometry in a separate cohort (n = 31). In conclusion, the immune cell contexture in ALL BM differs from healthy controls. CD4+PD1+TIM3+ T cells were independent predictors of poor outcome in our multivariate risk model, suggesting that PD1 might serve as an attractive immuno-oncological target in B-ALL.

Combined epithelial marker analysis of tumour budding in stage II colorectal cancer.

Tumour budding predicts survival of stage II colorectal cancer (CRC) and has been suggested to be associated with epithelial-to-mesenchymal transition (EMT). However, the underlying molecular changes of tumour budding remain poorly understood. Here, we performed multiplex immunohistochemistry (mIHC) to phenotypically profile tumours using known EMT-associated markers: E-cadherin (adherence junctions), integrin ß4 (ITGB4; basement membrane), ZO-1 (tight junctions), and pan-cytokeratin. A subpopulation of patients showed high ITGB4 expression in tumour buds, and this coincided with a switch of ITGB4 localisation from the basal membrane of intact epithelium to the cytoplasm of budding cells. Digital image analysis demonstrated that tumour budding with high ITGB4 expression in tissue microarray (TMA) cores correlated with tumour budding assessed from haematoxylin and eosin (H&E) whole sections and independently predicted poor disease-specific survival in two independent stage II CRC cohorts (hazard ratio [HR] = 4.50 (95% confidence interval [CI] = 1.50-13.5), n = 232; HR = 3.52 (95% CI = 1.30-9.53), n = 72). Furthermore, digitally obtained ITGB4-high bud count in random TMA cores was better associated with survival outcome than visual tumour bud count in corresponding H&E-stained samples. In summary, the mIHC-based phenotypic profiling of human tumour tissue shows strong potential for the molecular characterisation of tumour biology and for the discovery of novel prognostic biomarkers.

Implementation of deep neural networks to count dopamine neurons in substantia nigra.

Unbiased estimates of neuron numbers within substantia nigra are crucial for experimental Parkinson's disease models and gene-function studies. Unbiased stereological counting techniques with optical fractionation are successfully implemented, but are extremely laborious and time-consuming. The development of neural networks and deep learning has opened a new way to teach computers to count neurons. Implementation of a programming paradigm enables a computer to learn from the data and development of an automated cell counting method. The advantages of computerized counting are reproducibility, elimination of human error and fast high-capacity analysis. We implemented whole-slide digital imaging and deep convolutional neural networks (CNN) to count substantia nigra dopamine neurons. We compared the results of the developed method against independent manual counting by human observers and validated the CNN algorithm against previously published data in rats and mice, where tyrosine hydroxylase (TH)-immunoreactive neurons were counted using unbiased stereology. The developed CNN algorithm and fully cloud-embedded Aiforia™ platform provide robust and fast analysis of dopamine neurons in rat and mouse substantia nigra.

Immune cell contexture in the bone marrow tumor microenvironment impacts therapy response in CML.

Increasing evidence suggests that the immune system affects prognosis of chronic myeloid leukemia (CML), but the detailed immunological composition of the leukemia bone marrow (BM) microenvironment is unknown. We aimed to characterize the immune landscape of the CML BM and predict the current treatment goal of tyrosine kinase inhibitor (TKI) therapy, molecular remission 4.0 (MR4.0). Using multiplex immunohistochemistry (mIHC) and automated image analysis, we studied BM tissues of CML patients (n = 56) and controls (n = 14) with a total of 30 immunophenotype markers essential in cancer immunology. CML patients' CD4+ and CD8+ T-cells expressed higher levels of putative exhaustion markers PD1, TIM3, and CTLA4 when compared to control. PD1 expression was higher in BM compared to paired peripheral blood (PB) samples, and decreased during TKI therapy. By combining clinical parameters and immune profiles, low CD4+ T-cell proportion, high proportion of PD1+TIM3-CD8+ T cells, and high PB neutrophil count were most predictive of lower MR4.0 likelihood. Low CD4+ T-cell proportion and high PB neutrophil counts predicted MR4.0 also in a validation cohort (n = 52) analyzed with flow cytometry. In summary, the CML BM is characterized by immune suppression and immune biomarkers predicted MR4.0, thus warranting further testing of immunomodulatory drugs in CML treatment.

ITGB1-dependent upregulation of Caveolin-1 switches TGFß signalling from tumour-suppressive to oncogenic in prostate cancer.

Caveolin-1 (CAV1) is over-expressed in prostate cancer (PCa) and is associated with adverse prognosis, but the molecular mechanisms linking CAV1 expression to disease progression are poorly understood. Extensive gene expression correlation analysis, quantitative multiplex imaging of clinical samples, and analysis of the CAV1-dependent transcriptome, supported that CAV1 re-programmes TGFß signalling from tumour suppressive to oncogenic (i.e. induction of SLUG, PAI-1 and suppression of CDH1, DSP, CDKN1A). Supporting such a role, CAV1 knockdown led to growth arrest and inhibition of cell invasion in prostate cancer cell lines. Rationalized RNAi screening and high-content microscopy in search for CAV1 upstream regulators revealed integrin beta1 (ITGB1) and integrin associated proteins as CAV1 regulators. Our work suggests TGFß signalling and beta1 integrins as potential therapeutic targets in PCa over-expressing CAV1, and contributes to better understand the paradoxical dual role of TGFß in tumour biology.

Systems pathology by multiplexed immunohistochemistry and whole-slide digital image analysis.

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.

Protocols and characterization data for 2D, 3D, and slice-based tumor models from the PREDECT project.

Two-dimensional (2D) culture of cancer cells in vitro does not recapitulate the three-dimensional (3D) architecture, heterogeneity and complexity of human tumors. More representative models are required that better reflect key aspects of tumor biology. These are essential studies of cancer biology and immunology as well as for target validation and drug discovery. The Innovative Medicines Initiative (IMI) consortium PREDECT (www.predect.eu) characterized in vitro models of three solid tumor types with the goal to capture elements of tumor complexity and heterogeneity. 2D culture and 3D mono- and stromal co-cultures of increasing complexity, and precision-cut tumor slice models were established. Robust protocols for the generation of these platforms are described. Tissue microarrays were prepared from all the models, permitting immunohistochemical analysis of individual cells, capturing heterogeneity. 3D cultures were also characterized using image analysis. Detailed step-by-step protocols, exemplary datasets from the 2D, 3D, and slice models, and refined analytical methods were established and are presented.

Cell of Origin Links Histotype Spectrum to Immune Microenvironment Diversity in Non-small-Cell Lung Cancer Driven by Mutant Kras and Loss of Lkb1.

Lung cancers exhibit pronounced functional heterogeneity, confounding precision medicine. We studied how the cell of origin contributes to phenotypic heterogeneity following conditional expression of KrasG12D and loss of Lkb1 (Kras;Lkb1). Using progenitor cell-type-restricted adenoviral Cre to target cells expressing surfactant protein C (SPC) or club cell antigen 10 (CC10), we show that Ad5-CC10-Cre-infected mice exhibit a shorter latency compared with Ad5-SPC-Cre cohorts. We further demonstrate that CC10+ cells are the predominant progenitors of adenosquamous carcinoma (ASC) tumors and give rise to a wider spectrum of histotypes that includes mucinous and acinar adenocarcinomas. Transcriptome analysis shows ASC histotype-specific upregulation of pro-inflammatory and immunomodulatory genes. This is accompanied by an ASC-specific immunosuppressive environment, consisting of downregulated MHC genes, recruitment of CD11b+ Gr-1+ tumor-associated neutrophils (TANs), and decreased T cell numbers. We conclude that progenitor cell-specific etiology influences the Kras;Lkb1-driven tumor histopathology spectrum and histotype-specific immune microenvironment.

Capturing tumor complexity in vitro: Comparative analysis of 2D and 3D tumor models for drug discovery.

Two-dimensional (2D) cell cultures growing on plastic do not recapitulate the three dimensional (3D) architecture and complexity of human tumors. More representative models are required for drug discovery and validation. Here, 2D culture and 3D mono- and stromal co-culture models of increasing complexity have been established and cross-comparisons made using three standard cell carcinoma lines: MCF7, LNCaP, NCI-H1437. Fluorescence-based growth curves, 3D image analysis, immunohistochemistry and treatment responses showed that end points differed according to cell type, stromal co-culture and culture format. The adaptable methodologies described here should guide the choice of appropriate simple and complex in vitro models.

Capturing complex tumour biology in vitro: histological and molecular characterisation of precision cut slices.

Precision-cut slices of in vivo tumours permit interrogation in vitro of heterogeneous cells from solid tumours together with their native microenvironment. They offer a low throughput but high content in vitro experimental platform. Using mouse models as surrogates for three common human solid tumours, we describe a standardised workflow for systematic comparison of tumour slice cultivation methods and a tissue microarray-based method to archive them. Cultivated slices were compared to their in vivo source tissue using immunohistochemical and transcriptional biomarkers, particularly of cellular stress. Mechanical slicing induced minimal stress. Cultivation of tumour slices required organotypic support materials and atmospheric oxygen for maintenance of integrity and was associated with significant temporal and loco-regional changes in protein expression, for example HIF-1α. We recommend adherence to the robust workflow described, with recognition of temporal-spatial changes in protein expression before interrogation of tumour slices by pharmacological or other means.