PITX2 as a Sensitive and Specific Marker of Midgut Neuroendocrine Tumors: Results from a Cohort of 1157 Primary Neuroendocrine Neoplasms

As neuroendocrine tumors (NETs) often present as metastatic lesions, immunohistochemical assignment to a site of origin is one of the most important tasks in their pathologic assessment. Because a fraction of NETs eludes the typical expression profiles of their primary localization, additional sensitive and specific markers are required to improve diagnostic certainty. We investigated the expression of the transcription factor Pituitary Homeobox 2 (PITX2) in a large-scale cohort of 909 NET and 248 neuroendocrine carcinomas (NEC) according to the immunoreactive score (IRS) and correlated PITX2 expression groups with general tumor groups and primary localization. PITX2 expression (all expression groups) was highly sensitive (98.1%) for midgut-derived NET, but not perfectly specific, as non-midgut NET (especially pulmonary/duodenal) were quite frequently weak or moderately positive. The specificity rose to 99.5% for a midgut origin of NET if only a strong PITX2 expression was considered, which was found in only 0.5% (one pancreatic/one pulmonary) of non-midgut NET. In metastases of midgut-derived NET, PITX2 was expressed in all cases (87.5% strong, 12.5% moderate), whereas CDX2 was negative or only weakly expressed in 31.3% of the metastases. In NEC, a fraction of cases (14%) showed a weak or moderate PITX2 expression, which was not associated with a specific tumor localization. Our study independently validates PITX2 as a very sensitive and specific immunohistochemical marker of midgut-derived NET in a very large collective of neuroendocrine neoplasms. Therefore, our data argue toward implementation into diagnostic panels applied for NET as a firstline midgut marker.

A deep-learning workflow to predict upper tract urothelial carcinoma protein-based subtypes from H&E slides supporting the prioritization of patients for molecular testing

Upper tract urothelial carcinoma (UTUC) is a rare and aggressive, yet understudied, urothelial carcinoma (UC). The more frequent UC of the bladder comprises several molecular subtypes, associated with different targeted therapies and overlapping with protein-based subtypes. However, if and how these findings extend to UTUC remains unclear. Artificial intelligence-based approaches could help elucidate UTUC's biology and extend access to targeted treatments to a wider patient audience. Here, UTUC protein-based subtypes were identified, and a deep-learning (DL) workflow was developed to predict them directly from routine histopathological H&E slides. Protein-based subtypes in a retrospective cohort of 163 invasive tumors were assigned by hierarchical clustering of the immunohistochemical expression of three luminal (FOXA1, GATA3, and CK20) and three basal (CD44, CK5, and CK14) markers. Cluster analysis identified distinctive luminal (N = 80) and basal (N = 42) subtypes. The luminal subtype mostly included pushing, papillary tumors, whereas the basal subtype diffusely infiltrating, non-papillary tumors. DL model building relied on a transfer-learning approach by fine-tuning a pre-trained ResNet50. Classification performance was measured via three-fold repeated cross-validation. A mean area under the receiver operating characteristic curve of 0.83 (95% CI: 0.67-0.99), 0.8 (95% CI: 0.62-0.99), and 0.81 (95% CI: 0.65-0.96) was reached in the three repetitions. High-confidence DL-based predicted subtypes showed significant associations (p < 0.001) with morphological features, i.e. tumor type, histological subtypes, and infiltration type. Furthermore, a significant association was found with programmed cell death ligand 1 (PD-L1) combined positive score (p < 0.001) and FGFR3 mutational status (p = 0.002), with high-confidence basal predictions containing a higher proportion of PD-L1 positive samples and high-confidence luminal predictions a higher proportion of FGFR3-mutated samples. Testing of the DL model on an independent cohort highlighted the importance to accommodate histological subtypes. Taken together, our DL workflow can predict protein-based UTUC subtypes, associated with the presence of targetable alterations, directly from H&E slides.

Extracting structured information from unstructured histopathology reports using generative pre-trained transformer 4 (GPT-4)

Deep learning applied to whole-slide histopathology images (WSIs) has the potential to enhance precision oncology and alleviate the workload of experts. However, developing these models necessitates large amounts of data with ground truth labels, which can be both time-consuming and expensive to obtain. Pathology reports are typically unstructured or poorly structured texts, and efforts to implement structured reporting templates have been unsuccessful, as these efforts lead to perceived extra workload. In this study, we hypothesised that large language models (LLMs), such as the generative pre-trained transformer 4 (GPT-4), can extract structured data from unstructured plain language reports using a zero-shot approach without requiring any re-training. We tested this hypothesis by utilising GPT-4 to extract information from histopathological reports, focusing on two extensive sets of pathology reports for colorectal cancer and glioblastoma. We found a high concordance between LLM-generated structured data and human-generated structured data. Consequently, LLMs could potentially be employed routinely to extract ground truth data for machine learning from unstructured pathology reports in the future. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Encrypted federated learning for secure decentralized collaboration in cancer image analysis

Artificial intelligence (AI) has a multitude of applications in cancer research and oncology. However, the training of AI systems is impeded by the limited availability of large datasets due to data protection requirements and other regulatory obstacles. Federated and swarm learning represent possible solutions to this problem by collaboratively training AI models while avoiding data transfer. However, in these decentralized methods, weight updates are still transferred to the aggregation server for merging the models. This leaves the possibility for a breach of data privacy, for example by model inversion or membership inference attacks by untrusted servers. Somewhat-homomorphically-encrypted federated learning (SHEFL) is a solution to this problem because only encrypted weights are transferred, and model updates are performed in the encrypted space. Here, we demonstrate the first successful implementation of SHEFL in a range of clinically relevant tasks in cancer image analysis on multicentric datasets in radiology and histopathology. We show that SHEFL enables the training of AI models which outperform locally trained models and perform on par with models which are centrally trained. In the future, SHEFL can enable multiple institutions to co-train AI models without forsaking data governance and without ever transmitting any decryptable data to untrusted servers.

End-to-end prognostication in colorectal cancer by deep learning: a retrospective, multicentre study

BACKGROUND

Precise prognosis prediction in patients with colorectal cancer (ie, forecasting survival) is pivotal for individualised treatment and care. Histopathological tissue slides of colorectal cancer specimens contain rich prognostically relevant information. However, existing studies do not have multicentre external validation with real-world sample processing protocols, and algorithms are not yet widely used in clinical routine.

METHODS

In this retrospective, multicentre study, we collected tissue samples from four groups of patients with resected colorectal cancer from Australia, Germany, and the USA. We developed and externally validated a deep learning-based prognostic-stratification system for automatic prediction of overall and cancer-specific survival in patients with resected colorectal cancer. We used the model-predicted risk scores to stratify patients into different risk groups and compared survival outcomes between these groups. Additionally, we evaluated the prognostic value of these risk groups after adjusting for established prognostic variables.

FINDINGS

We trained and validated our model on a total of 4428 patients. We found that patients could be divided into high-risk and low-risk groups on the basis of the deep learning-based risk score. On the internal test set, the group with a high-risk score had a worse prognosis than the group with a low-risk score, as reflected by a hazard ratio (HR) of 4·50 (95% CI 3·33-6·09) for overall survival and 8·35 (5·06-13·78) for disease-specific survival (DSS). We found consistent performance across three large external test sets. In a test set of 1395 patients, the high-risk group had a lower DSS than the low-risk group, with an HR of 3·08 (2·44-3·89). In two additional test sets, the HRs for DSS were 2·23 (1·23-4·04) and 3·07 (1·78-5·3). We showed that the prognostic value of the deep learning-based risk score is independent of established clinical risk factors.

INTERPRETATION

Our findings indicate that attention-based self-supervised deep learning can robustly offer a prognosis on clinical outcomes in patients with colorectal cancer, generalising across different populations and serving as a potentially new prognostic tool in clinical decision making for colorectal cancer management. We release all source codes and trained models under an open-source licence, allowing other researchers to reuse and build upon our work.

FUNDING

The German Federal Ministry of Health, the Max-Eder-Programme of German Cancer Aid, the German Federal Ministry of Education and Research, the German Academic Exchange Service, and the EU.

[Multistain deep learning as a prognostic and predictive biomarker in colorectal cancer]

The tumor immune microenvironment (TIME) plays a crucial prognostic and predictive role in solid malignancies such as colorectal cancer (CRC). Nevertheless, scoring systems based on TIME such as the Immunoscore (IS) are rarely used in clinical practice. Among other reasons, this might be due to the additional time required for manual quantification of tumor-associated immune cells or costs associated with proprietary/commercial solutions. To address these issues, we developed a multistain deep learning model (MSDLM) and trained, validated, and tested it on immunohistochemical image data of different immune cell subtypes from over 1000 patients with CRC. Our model showed high prognostic accuracy and outperformed other clinical, molecular, and immune cell-based parameters. It might also be used for therapy response prediction in rectal cancer patients undergoing neoadjuvant therapy. Leveraging artificial intelligence interpretability/explainability methods, we ascertained that the MSDLM's predictions align with recognized antitumor immune response patterns. Consequently, the AImmunoscore (AIS) could emerge as a potential TIME-based decision-making tool for clinicians.

CD15 is a risk predictor and a novel target in clear cell renal cell carcinoma

INTRODUCTION

Tumor cells use adhesion molecules like CD15 or sialylCD15 (sCD15) for metastatic spreading. We analyzed the expression of CD15 and sCD15 in clear cell renal cell carcinoma (ccRCC) regarding prognosis.

METHODS

A tissue microarray containing tissue specimens of 763 patients with ccRCC was immunohistochemically stained for CD15 and sCD15, their expression quantified using digital image analysis and the impact on patients' survival analyzed. The cell lines 769p and 786o were stimulated with CD15 or control antibody in vitro and the effects on pathways activating AP-1 and tumor cell migration examined.

RESULTS

ccRCC showed a broad range of CD15 and sCD15 expression. A high CD15 expression was significantly associated with favorable outcome (p<0.01) and low-grade tumor differentiation (p<0.001), whereas sCD15 had no significant prognostic value. Tumors with synchronous distant metastasis had a significantly lower CD15 expression compared to tumors without any (p<0.001) or with metachronous metastasis (p<0.01). Tumor cell migration was significantly reduced after CD15 stimulation in vitro, but there were no major effects on activating pathways of AP-1.

CONCLUSION

CD15, but not sCD15, qualifies as a biomarker for risk stratification and as an interesting novel target in ccRCC. Moreover, the data indicates a contribution of CD15 to metachronous metastasis. Further research is warranted to decipher the intracellular pathways of CD15 signaling in ccRCC in order to characterize the CD15 effects on ccRCC more precisely.

High expression of insulinoma-associated protein 1 (INSM1) distinguishes colorectal mixed and pure neuroendocrine carcinomas from conventional adenocarcinomas with diffuse expression of synaptophysin

Complementary to synaptophysin and chromogranin A, insulinoma-associated protein 1 (INSM1) has emerged as a sensitive marker for the diagnosis of neuroendocrine neoplasms. Since there are no comparative data regarding INSM1 expression in conventional colorectal adenocarcinomas (CRCs) and colorectal mixed adenoneuroendocrine carcinomas/neuroendocrine carcinomas (MANECs/NECs), we examined INSM1 in a large cohort of conventional CRCs and MANECs/NECs. In conventional CRC, we put a special focus on conventional CRC with diffuse expression of synaptophysin, which carry the risk of being misinterpreted as a MANEC or a NEC. We investigated INSM1 according to the immunoreactive score in our main cohort of 1,033 conventional CRCs and 21 MANECs/NECs in comparison to the expression of synaptophysin and chromogranin A and correlated the results with clinicopathological parameters and patient survival. All MANECs/NECs expressed INSM1, usually showing high or moderate expression (57% high, 34% moderate, and 9% low), which distinguished them from conventional CRCs, which were usually INSM1 negative or low, even if they diffusely expressed synaptophysin. High expression of INSM1 was not observed in conventional CRCs. Chromogranin A was negative/low in most conventional CRCs (99%), but also in most MANECs/NECs (66%). Comparable results were observed in our independent validation cohorts of conventional CRC (n = 274) and MANEC/NEC (n = 19). Similar to synaptophysin, INSM1 expression had no prognostic relevance in conventional CRCs, while true MANEC/NEC showed a highly impaired survival in univariate and multivariate analyses (e.g. disease-specific survival: p < 0.001). MANECs/NECs are a highly aggressive variant of colorectal cancer, which must be reliably identified. High expression of INSM1 distinguishes MANEC/NEC from conventional CRCs with diffuse expression of the standard neuroendocrine marker synaptophysin, which do not share the same dismal prognosis. Therefore, high INSM1 expression is a highly specific/sensitive marker that is supportive for the diagnosis of true colorectal MANEC/NEC.

Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study

Deep learning (DL) can accelerate the prediction of prognostic biomarkers from routine pathology slides in colorectal cancer (CRC). However, current approaches rely on convolutional neural networks (CNNs) and have mostly been validated on small patient cohorts. Here, we develop a new transformer-based pipeline for end-to-end biomarker prediction from pathology slides by combining a pre-trained transformer encoder with a transformer network for patch aggregation. Our transformer-based approach substantially improves the performance, generalizability, data efficiency, and interpretability as compared with current state-of-the-art algorithms. After training and evaluating on a large multicenter cohort of over 13,000 patients from 16 colorectal cancer cohorts, we achieve a sensitivity of 0.99 with a negative predictive value of over 0.99 for prediction of microsatellite instability (MSI) on surgical resection specimens. We demonstrate that resection specimen-only training reaches clinical-grade performance on endoscopic biopsy tissue, solving a long-standing diagnostic problem.

An overview and a roadmap for artificial intelligence in hematology and oncology

BACKGROUND

Artificial intelligence (AI) is influencing our society on many levels and has broad implications for the future practice of hematology and oncology. However, for many medical professionals and researchers, it often remains unclear what AI can and cannot do, and what are promising areas for a sensible application of AI in hematology and oncology. Finally, the limits and perils of using AI in oncology are not obvious to many healthcare professionals.

METHODS

In this article, we provide an expert-based consensus statement by the joint Working Group on "Artificial Intelligence in Hematology and Oncology" by the German Society of Hematology and Oncology (DGHO), the German Association for Medical Informatics, Biometry and Epidemiology (GMDS), and the Special Interest Group Digital Health of the German Informatics Society (GI). We provide a conceptual framework for AI in hematology and oncology.

RESULTS

First, we propose a technological definition, which we deliberately set in a narrow frame to mainly include the technical developments of the last ten years. Second, we present a taxonomy of clinically relevant AI systems, structured according to the type of clinical data they are used to analyze. Third, we show an overview of potential applications, including clinical, research, and educational environments with a focus on hematology and oncology.

CONCLUSION

Thus, this article provides a point of reference for hematologists and oncologists, and at the same time sets forth a framework for the further development and clinical deployment of AI in hematology and oncology in the future.

Denoising diffusion probabilistic models for 3D medical image generation

Recent advances in computer vision have shown promising results in image generation. Diffusion probabilistic models have generated realistic images from textual input, as demonstrated by DALL-E 2, Imagen, and Stable Diffusion. However, their use in medicine, where imaging data typically comprises three-dimensional volumes, has not been systematically evaluated. Synthetic images may play a crucial role in privacy-preserving artificial intelligence and can also be used to augment small datasets. We show that diffusion probabilistic models can synthesize high-quality medical data for magnetic resonance imaging (MRI) and computed tomography (CT). For quantitative evaluation, two radiologists rated the quality of the synthesized images regarding "realistic image appearance", "anatomical correctness", and "consistency between slices". Furthermore, we demonstrate that synthetic images can be used in self-supervised pre-training and improve the performance of breast segmentation models when data is scarce (Dice scores, 0.91 [without synthetic data], 0.95 [with synthetic data]).

AI in Computational Pathology of Cancer: Improving Diagnostic Workflows and Clinical Outcomes?

Histopathology plays a fundamental role in the diagnosis and subtyping of solid tumors and has become a cornerstone of modern precision oncology. Histopathological evaluation is typically performed manually by expert pathologists due to the complexity of visual data. However, in the last ten years, new artificial intelligence (AI) methods have made it possible to train computers to perform visual tasks with high performance, reaching similar levels as experts in some applications. In cancer histopathology, these AI tools could help automate repetitive tasks, making more efficient use of pathologists’ time. In research studies, AI methods have been shown to have an astounding ability to predict genetic alterations and identify prognostic and predictive biomarkers directly from routine tissue slides. Here, we give an overview of these recent applications of AI in computational pathology, focusing on new tools for cancer research that could be pivotal in identifying clinical biomarkers for better treatment decisions.

Expected final online publication date for the Annual Review of Cancer Biology, Volume 7 is April 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Generalizable biomarker prediction from cancer pathology slides with self-supervised deep learning: A retrospective multi-centric study

Deep learning (DL) can predict microsatellite instability (MSI) from routine histopathology slides of colorectal cancer (CRC). However, it is unclear whether DL can also predict other biomarkers with high performance and whether DL predictions generalize to external patient populations. Here, we acquire CRC tissue samples from two large multi-centric studies. We systematically compare six different state-of-the-art DL architectures to predict biomarkers from pathology slides, including MSI and mutations in BRAF, KRAS, NRAS, and PIK3CA. Using a large external validation cohort to provide a realistic evaluation setting, we show that models using self-supervised, attention-based multiple-instance learning consistently outperform previous approaches while offering explainable visualizations of the indicative regions and morphologies. While the prediction of MSI and BRAF mutations reaches a clinical-grade performance, mutation prediction of PIK3CA, KRAS, and NRAS was clinically insufficient.

Multistain deep learning for prediction of prognosis and therapy response in colorectal cancer

Although it has long been known that the immune cell composition has a strong prognostic and predictive value in colorectal cancer (CRC), scoring systems such as the immunoscore (IS) or quantification of intraepithelial lymphocytes are only slowly being adopted into clinical routine use and have their limitations. To address this we established and evaluated a multistain deep learning model (MSDLM) utilizing artificial intelligence (AI) to determine the AImmunoscore (AIS) in more than 1,000 patients with CRC. Our model had high prognostic capabilities and outperformed other clinical, molecular and immune cell-based parameters. It could also be used to predict the response to neoadjuvant therapy in patients with rectal cancer. Using an explainable AI approach, we confirmed that the MSDLM's decisions were based on established cellular patterns of anti-tumor immunity. Hence, the AIS could provide clinicians with a valuable decision-making tool based on the tumor immune microenvironment.

Neutrophils prevent rectal bleeding in ulcerative colitis by peptidyl-arginine deiminase-4-dependent immunothrombosis

OBJECTIVE

Bleeding ulcers and erosions are hallmarks of active ulcerative colitis (UC). However, the mechanisms controlling bleeding and mucosal haemostasis remain elusive.

DESIGN

We used high-resolution endoscopy and colon tissue samples of active UC (n = 36) as well as experimental models of physical and chemical mucosal damage in mice deficient for peptidyl-arginine deiminase-4 (PAD4), gnotobiotic mice and controls. We employed endoscopy, histochemistry, live-cell microscopy and flow cytometry to study eroded mucosal surfaces during mucosal haemostasis.

RESULTS

Erosions and ulcerations in UC were covered by fresh blood, haematin or fibrin visible by endoscopy. Fibrin layers rather than fresh blood or haematin on erosions were inversely correlated with rectal bleeding in UC. Fibrin layers contained ample amounts of neutrophils coaggregated with neutrophil extracellular traps (NETs) with detectable activity of PAD. Transcriptome analyses showed significantly elevated PAD4 expression in active UC. In experimentally inflicted wounds, we found that neutrophils underwent NET formation in a PAD4-dependent manner hours after formation of primary blood clots, and remodelled clots to immunothrombi containing citrullinated histones, even in the absence of microbiota. PAD4-deficient mice experienced an exacerbated course of dextrane sodium sulfate-induced colitis with markedly increased rectal bleeding (96 % vs 10 %) as compared with controls. PAD4-deficient mice failed to remodel blood clots on mucosal wounds eliciting impaired healing. Thus, NET-associated immunothrombi are protective in acute colitis, while insufficient immunothrombosis is associated with rectal bleeding.

CONCLUSION

Our findings uncover that neutrophils induce secondary immunothrombosis by PAD4-dependent mechanisms. Insufficient immunothrombosis may favour rectal bleeding in UC.

pT3 colorectal cancer revisited: a multicentric study on the histological depth of invasion in more than 1000 pT3 carcinomas-proposal for a new pT3a/pT3b subclassification

Background

Pathological TNM staging (pTNM) is the strongest prognosticator in colorectal carcinoma (CRC) and the foundation of its post-operative clinical management. Tumours that invade pericolic/perirectal adipose tissue generally fall into the pT3 category without further subdivision.

Methods

The histological depth of invasion into the pericolic/perirectal fat was digitally and conventionally measured in a training cohort of 950 CRCs (Munich). We biostatistically calculated the optimal cut-off to stratify pT3 CRCs into novel pT3a (≤3 mm)/pT3b (>3 mm) subgroups, which were then validated in two independent cohorts (447 CRCs, Bayreuth/542 CRCs, Mainz).

Results

Compared to pT3a tumours, pT3b CRCs showed significantly worse disease-specific survival, including in pN0 vs pN+ and colonic vs. rectal cancers (DSS: P < 0.001, respectively, pooled analysis of all cohorts). Furthermore, the pT3a/pT3b subclassification remained an independent predictor of survival in multivariate analyses (e.g. DSS: P < 0.001, hazard ratio: 4.41 for pT3b, pooled analysis of all cohorts). While pT2/pT3a CRCs showed similar survival characteristics, pT3b cancers remained a distinct subgroup with dismal survival.

Discussion

The delineation of pT3a/pT3b subcategories of CRC based on the histological depth of adipose tissue invasion adds valuable prognostic information to the current pT3 classification and implementation into current staging practices of CRC should be considered.

Senescence-Associated Molecules and Tumor-Immune-Interactions as Prognostic Biomarkers in Colorectal Cancer

Background and Aims

The initiation of cellular senescence in response to protumorigenic stimuli counteracts malignant progression in (pre)malignant cells. Besides arresting proliferation, cells entering this terminal differentiation state adopt a characteristic senescence-associated secretory phenotype (SASP) which initiates alterations to their microenvironment and effects immunosurveillance of tumorous lesions. However, some effects mediated by senescent cells contribute to disease progression. Currently, the exploration of senescent cells' impact on the tumor microenvironment and the evaluation of senescence as possible target in colorectal cancer (CRC) therapy demand reliable detection of cellular senescence in vivo. Therefore, specific immunohistochemical biomarkers are required. Our aim is to analyze the clinical implications of senescence detection in colorectal carcinoma and to investigate the interactions of senescent tumor cells and their immune microenvironment in vitro and in vivo.

Methods

Senescence was induced in CRC cell lines by low-dose-etoposide treatment and confirmed by Senescence-associated β-galactosidase (SA-β-GAL) staining and fluorescence activated cell sorting (FACS) analysis. Co-cultures of senescent cells and immune cells were established. Multiple cell viability assays, electron microscopy and live cell imaging were conducted. Immunohistochemical (IHC) markers of senescence and immune cell subtypes were studied in a cohort of CRC patients by analyzing a tissue micro array (TMA) and performing digital image analysis. Results were compared to disease-specific survival (DSS) and progression-free survival (PFS).

Results

Varying expression of senescence markers in tumor cells was associated with in- or decreased survival of CRC patients. Proximity analysis of p21-positive senescent tumor cells and cytotoxic T cells revealed a significantly better prognosis for patients in which these cell types have the possibility to directly interact. In vitro, NK-92 cells (mimicking natural killer T cells) or TALL-104 cells (mimicking both cytotoxic T cells and natural killer T cells) led to dose-dependent specific cytotoxicity in >75 % of the senescent CRC cells but <20 % of the proliferating control CRC cells. This immune cell-mediated senolysis seems to be facilitated via direct cell-cell contact inducing apoptosis and granule exocytosis.

Conclusion

Counteracting tumorigenesis, cellular senescence is of significant relevance in CRC. We show the dual role of senescence bearing both beneficial and malignancy-promoting potential in vivo. Absence as well as exceeding expression of senescence markers are associated with bad prognosis in CRC. The antitumorigenic potential of senescence induction is determined by tumor micromilieu and immune cell-mediated elimination of senescent cells.

The future of artificial intelligence in digital pathology - results of a survey across stakeholder groups

AIMS

Artificial intelligence (AI) provides a powerful tool to extract information from digitized histopathology whole slide images. In the last five years, academic and commercial actors have developed new technical solutions for a diverse set of tasks, including tissue segmentation, cell detection, mutation prediction, prognostication and prediction of treatment response. In the light of limited overall resources, it is presently unclear for researchers, practitioners and policymakers which of these topics are stable enough for clinical use in the near future and which topics are still experimental, but worth investing time and effort into.

METHODS

To identify potentially promising applications of AI in pathology, we performed an anonymous online survey of 75 computational pathology domain experts from academia and industry. Participants enrolled in 2021 were queried about their subjective opinion on promising and appealing sub-fields of computational pathology with a focus on solid tumors.

RESULTS

The results of this survey indicate that the prediction of treatment response directly from routine pathology slides is regarded as the most promising future application. This item was ranked highest in the overall analysis and in sub-groups by age and professional background. Furthermore, prediction of genetic alterations, gene expression and survival directly from routine pathology images scored consistently high across subgroups.

CONCLUSIONS

Together, these data demonstrate a possible direction for the development of computational pathology systems in clinical, academic and industrial research in the near future.

Artificial intelligence to identify genetic alterations in conventional histopathology

Precision oncology relies on the identification of targetable molecular alterations in tumor tissues. In many tumor types, a limited set of molecular tests is currently part of standard diagnostic workflows. However, universal testing for all targetable alterations, especially rare ones, is limited by the cost and availability of molecular assays. From 2017 to 2021, multiple studies have shown that artificial intelligence (AI) methods can predict the probability of specific genetic alterations directly from conventional hematoxylin and eosin (H&E) tissue slides. Although these methods are currently less accurate than gold-standard testing (e.g. immunohistochemistry, polymerase chain reaction or next-generation sequencing), they could be used as pre-screening tools to reduce the workload of genetic analyses. In this systematic literature review, we summarize the state of the art in predicting molecular alterations from H&E using AI. We found that AI methods perform reasonably well across multiple tumor types, although few algorithms have been broadly validated. In addition, we found that genetic alterations in FGFR, IDH, PIK3CA, BRAF, TP53 and DNA repair pathways are predictable from H&E in multiple tumor types, while many other genetic alterations have rarely been investigated or were only poorly predictable. Finally, we discuss the next steps for the implementation of AI-based surrogate tests in diagnostic workflows. This article is protected by copyright. All rights reserved.

Multimodal Deep Learning for Prognosis Prediction in Renal Cancer

BACKGROUND

Clear-cell renal cell carcinoma (ccRCC) is common and associated with substantial mortality. TNM stage and histopathological grading have been the sole determinants of a patient's prognosis for decades and there are few prognostic biomarkers used in clinical routine. Management of ccRCC involves multiple disciplines such as urology, radiology, oncology, and pathology and each of these specialties generates highly complex medical data. Here, artificial intelligence (AI) could prove extremely powerful to extract meaningful information to benefit patients.

OBJECTIVE

In the study, we developed and evaluated a multimodal deep learning model (MMDLM) for prognosis prediction in ccRCC.

DESIGN SETTING AND PARTICIPANTS

Two mixed cohorts of non-metastatic and metastatic ccRCC patients were used: (1) The Cancer Genome Atlas cohort including 230 patients and (2) the Mainz cohort including 18 patients with ccRCC. For each of these patients, we trained the MMDLM on multiscale histopathological images, CT/MRI scans, and genomic data from whole exome sequencing.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Outcome measurements included Harrell's concordance index (C-index) and also various performance parameters for predicting the 5-year survival status (5YSS). Different visualization techniques were used to make our model more transparent.

RESULTS

The MMDLM showed great performance in predicting the prognosis of ccRCC patients with a mean C-index of 0.7791 and a mean accuracy of 83.43%. Training on a combination of data from different sources yielded significantly better results compared to when only one source was used. Furthermore, the MMDLM's prediction was an independent prognostic factor outperforming other clinical parameters.

INTERPRETATION

Multimodal deep learning can contribute to prognosis prediction in ccRCC and potentially help to improve the clinical management of this disease.

PATIENT SUMMARY

An AI-based computer program can analyze various medical data (microscopic images, CT/MRI scans, and genomic data) simultaneously and thereby predict the survival time of patients with renal cancer.

Interassay and interobserver comparability study of four programmed death-ligand 1 (PD-L1) immunohistochemistry assays in triple-negative breast cancer

Different immunohistochemical programmed death-ligand 1 (PD-L1) assays and scorings have been reported to yield variable results in triple-negative breast cancer (TNBC). We compared the analytical concordance and reproducibility of four clinically relevant PD-L1 assays assessing immune cell (IC) score, tumor proportion score (TPS), and combined positive score (CPS) in TNBC. Primary TNBC resection specimens (n = 104) were stained for PD-L1 using VENTANA SP142, VENTANA SP263, DAKO 22C3, and DAKO 28–8. PD-L1 expression was scored according to guidelines on virtual whole slide images by four trained readers.

The mean PD-L1 positivity at IC-score ≥1% and CPS ≥1 ranged between 53% and 75% with the highest positivity for SP263 and comparable levels for 22C3, 28–8, and SP142. Inter-assay agreement was good between 28–8 and 22C3 across all scores and cut-offs (kappa 0.68–0.74) and for both assays with SP142 at IC-score ≥1% and CPS ≥1 (kappa 0.61–0.67). The agreement between SP263 and all other assays was substantially lower for all scores. Inter-reader agreement for each assay was good to excellent for IC-score ≥1% (kappa 0.73–0.78) and CPS ≥1 (kappa 0.68–0.74), fair to good for CPS ≥10 (kappa 0.52–0.67) and TPS ≥1% (kappa 0.53–0.72). The percentage of overlapping cases in the positive/negative category was >90% between IC-score ≥1% and CPS ≥1 but below when comparing IC-score ≥1% with CPS ≥10. We demonstrate an overall good inter-reader agreement for all PD-L1 assays in TNBC along with assay specific differences in positivity and concordances, which may aid to select the right test strategy in routine diagnostics.

•

Different PD-L1 IHC assays and scorings may show variable results in TNBC.

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Overall good assay concordance between SP142, 22C3, and 28–8 at IC-score 1%.

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Overall good assay concordance between SP142, 22C3, and 28–8 at CPS 1.

•

SP142 is less optimal for CPS assessment at higher cut-offs.

•

SP263 assay is not interchangeable with the other three PD-L1 assays.

Comparative analysis of nuclear and mitochondrial DNA from tissue and liquid biopsies of colorectal cancer patients

The current standard for molecular profiling of colorectal cancer (CRC) is using resected or biopsied tissue specimens. However, they are limited regarding sampling frequency, representation of tumor heterogeneity, and sampling can expose patients to adverse side effects. The analysis of cell-free DNA (cfDNA) from blood plasma, which is part of a liquid biopsy, is minimally invasive and in principle enables detection of all tumor-specific mutations. Here, we analyzed cfDNA originating from nucleus and mitochondria and investigated their characteristics and mutation status in a cohort of 18 CRC patients and 10 healthy controls using targeted next-generation sequencing (NGS) and digital PCR. Longitudinal analyses of nuclear cfDNA level and size during chemotherapy revealed a decreasing cfDNA content and a shift from short to long fragments, indicating an appropriate therapy response, while shortened cfDNAs and increased cfDNA content corresponded with tumor recurrence. Comparative NGS analysis of nuclear tissue and plasma DNA demonstrated a good patient-level concordance and cfDNA revealed additional variants in three of the cases. Analysis of mitochondrial cfDNA surprisingly revealed a higher plasma copy number in healthy subjects than in CRC patients. These results highlight the potential clinical utility of liquid biopsies in routine diagnostics and surveillance of CRC patients as complementation to tissue biopsies or as an attractive alternative in cases where tissue biopsies are risky or the quantity/quality does not allow testing.

Deep learning for diagnosis and survival prediction in soft tissue sarcoma

BACKGROUND

Clinical management of soft tissue sarcoma (STS) is particularly challenging. Here, we used digital pathology and deep learning (DL) for diagnosis and prognosis prediction of STS.

PATIENTS AND METHODS

Our retrospective, multicenter study included a total of 506 histopathological slides from 291 patients with STS. The Cancer Genome Atlas cohort (240 patients) served as training and validation set. A second, multicenter cohort (51 patients) served as an additional test set. The use of the DL model (DLM) as a clinical decision support system was evaluated by nine pathologists with different levels of expertise. For prognosis prediction, 139 slides from 85 patients with leiomyosarcoma (LMS) were used. Area under the receiver operating characteristic (AUROC) and accuracy served as main outcome measures.

RESULTS

The DLM achieved a mean AUROC of 0.97 (±0.01) and an accuracy of 79.9% (±6.1%) in diagnosing the five most common STS subtypes. The DLM significantly improved the accuracy of the pathologists from 46.3% (±15.5%) to 87.1% (±11.1%). Furthermore, they were significantly faster and more certain in their diagnosis. In LMS, the mean AUROC in predicting the disease-specific survival status was 0.91 (±0.1) and the accuracy was 88.9% (±9.9%). Cox regression showed the DLM's prediction to be a significant independent prognostic factor (P = 0.008, hazard ratio 5.5, 95% confidence interval 1.56-19.7) in these patients, outperforming other risk factors.

CONCLUSIONS

DL can be used to accurately diagnose frequent subtypes of STS from conventional histopathological slides. It might be used for prognosis prediction in LMS, the most prevalent STS subtype in our cohort. It can also help pathologists to make faster and more accurate diagnoses. This could substantially improve the clinical management of STS patients.

Growth Factor Receptor Expression in Oropharyngeal Squamous Cell Cancer: Her1-4 and c-Met in Conjunction with the Clinical Features and Human Papillomavirus (p16) Status

Simple Summary

Growth factor expression is a negative prognostic factor in head and neck squamous cell carcinoma (HNSCC). Targeted therapy has a limited effect on the treatment of advanced stages due to evolving resistance mechanisms. The aim of this study was to assess the distribution of growth factor receptors in oropharyngeal squamous cell cancer (OPSCC) and evaluate their role in the context of the human papillomavirus status, prognosis and possible relevance for targeted therapy. Tissue microarrays of 78 primary OPSCC, 35 related lymph node metastasis, 6 distant metastasis and 9 recurrent tumors were manufactured to evaluate the expression of human epidermal growth factor receptor (EGFR/erbB/Her)1–4 and c-Met by immunohistochemistry. EGFR and c-Met are relevant negative prognostic factors especially in noxae-induced OPSCC. Thus, dual targeting of EGFR and c-Met could be a promising prospective target in OPSCC treatment. Frequent coexpression of assessed receptors represents a possible intrinsic resistance mechanism in targeted therapy.

Abstract

This study aimed to assess the distribution of growth factor receptors in oropharyngeal squamous cell cancer (OPSCC) and evaluate their role in the context of human papillomavirus (HPV) status, prognosis and potential relevance for targeted therapy. The protein expression of human epidermal growth factor receptor (Her)1–4 and c-Met were retrospectively assessed using semiquantitative immunohistochemistry on tissue microarrays and analyzed for correlations as well as differences in the clinicopathological criteria. Her1–4 and c-met were overexpressed compared to normal mucosa in 46%, 4%, 17%, 27% and 23%, respectively. Interestingly, most receptors were coexpressed. Her1 and c-Met were inversely correlated with p16 (p = 0.04; p = 0.02). Her2 and c-Met were associated with high tobacco consumption (p = 0.016; p = 0.04). High EGFR, Her3, Her4 and c-Met expression were associated with worse overall and disease-free survival (p ≤ 0.05). Furthermore, EGFR and c-Met expression showed raised hazard ratios of 2.53 (p = 0.02; 95% CI 1.24–5.18) and 2.45 (p = 0.02; 95% CI 1.13–5.35), respectively. Her4 was expressed less in distant metastases than in corresponding primary tumors and was correlated to a higher T category. EGFR and c-Met are relevant negative prognostic factors in OPSCC, independent of known clinicopathological parameters. We suggest dual targeting of EGFR and c-Met as a promising strategy for OPSCC treatment.

Tumor Lipids of Pediatric Papillary Renal Cell Carcinoma Stimulate Unconventional T Cells

Papillary renal cell carcinoma (PRCC) is a rare entity in children with no established therapy protocols for advanced diseases. Immunotherapy is emerging as an important therapeutic tool for childhood cancer. Tumor cells can be recognized and killed by conventional and unconventional T cells. Unconventional T cells are able to recognize lipid antigens presented via CD1 molecules independently from major histocompatibility complex, which offers new alternatives for cancer immunotherapies. The nature of those lipids is largely unknown and α-galactosylceramide is currently used as a synthetic model antigen. In this work, we analyzed infiltrating lymphocytes of two pediatric PRCCs using flow cytometry, immunohistochemistry and qRT-PCR. Moreover, we analyzed the CD1d expression within both tumors. Tumor lipids of PRCC samples and three normal kidney samples were fractionated and the recognition of tumor own lipid fractions by unconventional T cells was analyzed in an in vitro assay. We identified infiltrating lymphocytes including γδ T cells and iNKT cells, as well as CD1d expression in both samples. One lipid fraction, containing ceramides and monoacylglycerides amongst others, was able to induce the proliferation of iNKT cells isolated from peripheral blood mononuclear cells (PBMCs) of healthy donors and of one matched PRCC patient. Furthermore, CD1d tetramer stainings revealed that a subset of iNKT cells is able to bind lipids being present in fraction 2 via CD1d. We conclude that PRCCs are infiltrated by conventional and unconventional T cells and express CD1d. Moreover, certain lipids, present in pediatric PRCC, are able to stimulate unconventional T cells. Manipulating these lipids and T cells may open new strategies for therapy of pediatric PRCCs.

Deep Learning Predicts Molecular Subtype of Muscle-invasive Bladder Cancer from Conventional Histopathological Slides

BACKGROUND

Muscle-invasive bladder cancer (MIBC) is the second most common genitourinary malignancy, and is associated with high morbidity and mortality. Recently, molecular subtypes of MIBC have been identified, which have important clinical implications.

OBJECTIVE

In the current study, we tried to predict the molecular subtype of MIBC samples from conventional histomorphology alone using deep learning.

DESIGN, SETTING, AND PARTICIPANTS

Two cohorts of patients with MIBC were used: (1) The Cancer Genome Atlas Urothelial Bladder Carcinoma dataset including 407 patients and (2) our own cohort including 16 patients with treatment-naïve, primary resected MIBC. This resulted in a total of 423 digital whole slide images of tumor tissue to train, validate, and test the deep learning algorithm to predict the molecular subtype.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Various accuracy measurements including the area under the receiver operating characteristic curves were used to evaluate the deep learning model. A sliding window approach to visualize classification was used. Class activation maps were used to identify image features that are most relevant to call a specific class.

RESULTS AND LIMITATIONS

The deep learning model showed great performance in the prediction of the molecular subtype of MIBC patients from hematoxylin and eosin (HE) slides alone-similar to or better than pathology experts. Using different visualization techniques, we identified new histopathological features that were most relevant to our model.

CONCLUSIONS

Deep learning can be used to predict important molecular features in MIBC patients from HE slides alone, potentially improving the clinical management of this disease significantly.

PATIENT SUMMARY

In patients with bladder cancer, a computer program found changes in the appearance of tumor tissue under the microscope and used these to predict genetic alterations. This could potentially benefit patients.

Prognostic and Predictive Value of Tumor-infiltrating Leukocytes and of Immune Checkpoint Molecules PD1 and PDL1 in Clear Cell Renal Cell Carcinoma

INTRODUCTION: Immune checkpoint inhibitors (ICI) have been approved for patients with clear cell renal cell carcinoma (ccRCC), but not all patients benefit from ICI. One reason is the tumor microenvironment (TME) that has substantial influence on patient's prognosis and therapy response. Thus, we comprehensively analyzed the TME of ccRCC regarding prognostic and predictive properties. METHODS: Tumor-infiltrating CD3-positive T-cells, CD8-positive cytotoxic T-lymphocytes (CTLs), regulatory T-cells, B-cells, plasma cells, macrophages, granulocytes, programmed cell death receptor 1 (PD-1), and its ligand PD-L1 were examined in a large hospital-based series of ccRCC with long-term follow-up information (n = 756) and in another patient collective with information on response to nivolumab therapy (n = 8). Tissue microarray technique and digital image analysis were used. Relationship between immune cell infiltration and tumor characteristics, cancer-specific survival (CSS), or response to ICI was examined. RESULTS: Univariate survival analysis revealed that increased tumor-infiltrating B-cells, T-cells, and PD-1-positive cells were significantly associated with favorable CSS and high levels of intratumoral granulocytes, macrophages, cytotoxic T-cells, and PD-L1 significantly with poor CSS. High CTL or B-cell infiltration and high PD-L1 expression of ccRCC tumor cells qualified as independent prognostic biomarkers for patients' CSS. Significantly higher densities of intratumoral T-cells, CTLs, and PD-1-positive immune cells were observed in ccRCC with response to ICI compared with patients with mixed or no response (CD3: p = 0.003; CD8: p = 0.006; PD-1: p = 0.01). DISCUSSION: This study shows that subsets of tumor-infiltrating leukocytes in the TME and also PD-1/PD-L1 provide prognostic and predictive information for patients with ccRCC.

Relationship between EGFR expression and subcellular localization with cancer development and clinical outcome

Epidermal growth factor receptor (EGFR) as a prevalent oncogene regulates proliferation, apoptosis and differentiation and thereby contributes to carcinogenesis. Even though, the documentation on its clinical relevance is surprisingly heterogeneous in the scientific literature. Here, we systematically investigated the correlation of mRNA to survival time and pathological parameters by analyzing 30 datasets in silico. Furthermore, the prognostic value of membrane-bound, cytoplasmic (mcEGFR) and nuclear expression (nEGFR) of EGFR was experimentally analyzed by immunohistochemical staining of 502 biopsies from 27 tumor types. We found that protein expression of EGFR showed better prognostic efficiency compared to mRNA, and that mcEGFR expression was positively correlated with nEGFR expression (p < 0.001). Unexpectedly, both mcEGFR and nEGFR expression were associated with low T stage (p < 0.001 and p = 0.004; respectively). Moreover, positive mcEGFR was significantly related to high differentiation (p = 0.027). No significant correlation was found with any other pathological parameters. Collectively, our results imply that the oncogenic function of EGFR may be more related to nascent stages of carcinogenesis than to advanced and progressive tumors, which may as well explain at least partially the occurrence of secondary resistance against EGFR-directed therapy.

Appendiceal goblet cell carcinoids and adenocarcinomas ex-goblet cell carcinoid are genetically distinct from primary colorectal-type adenocarcinoma of the appendix

The appendix gives rise to goblet cell carcinoids, which represent special carcinomas with distinct biological and histological features. Their genetic background and molecular relationship to colorectal adenocarcinoma is largely unknown. We therefore performed a next-generation sequencing analysis of 25 appendiceal carcinomas including 11 goblet cell carcinoids, 7 adenocarcinomas ex-goblet cell carcinoid, and 7 primary colorectal-type adenocarcinomas, using a modified Colorectal Cancer specific Panel comprising 32 genes linked to colorectal and neuroendocrine tumorigenesis. The mutational profiles of these neoplasms were compared with those of conventional adenocarcinomas, mixed adenoneuroendocrine carcinomas, and neuroendocrine carcinomas of the colorectum. In addition, a large-scale pan-cancer sequencing panel covering 409 genes was applied to selected cases of goblet cell carcinoid/adenocarcinoma ex-goblet cell carcinoid (n=2, respectively). Mutations in colorectal cancer-related genes (eg, TP53, KRAS, APC) were rare to absent in both, goblet cell carcinoids and adenocarcinomas ex-goblet cell carcinoid, but frequent in primary colorectal-type adenocarcinomas of the appendix. Additional large-scale sequencing of selected goblet cell carcinoids and adenocarcinomas ex-goblet cell carcinoid revealed mutations in Wnt-signaling-associated genes (USP9X, NOTCH1, CTNNA1, CTNNB1, TRRAP). These data suggest that appendiceal goblet cell carcinoids and adenocarcinomas ex-goblet cell carcinoid constitute a morphomolecular entity, histologically and genetically distinct from appendiceal colorectal-type adenocarcinomas and its colorectal counterparts. Altered Wnt-signaling associated genes, apart from APC, may act as potential drivers of these neoplasms. The absence of KRAS/NRAS mutations might render some of these tumors eligible for anti-EGFR directed therapy regimens.

[Cellular senescence and colorectal cancer]

Apart from proliferation and cell death, cellular senescence is an important response to numerous stress-associated stimuli. Originally described as an in vitro phenomenon, it is involved in various physiological and pathophysiological processes. For example, during the development of solid and generalized tumors, senescence induction poses an important barrier against disease progression. This could be demonstrated for malignant lymphomas, melanomas and various carcinomas using sophisticated animal models. However, senescent cells remain highly secretorically active and have a profound effect on neighboring cells as well as the entire tissue network. This article tries to provide insight into the current literature and discusses clinical implications and future applications.

Programming of Intestinal Epithelial Differentiation by IL-33 Derived from Pericryptal Fibroblasts in Response to Systemic Infection

The intestinal epithelium constitutes an efficient barrier against the microbial flora. Here, we demonstrate an unexpected function of IL-33 as a regulator of epithelial barrier functions. Mice lacking IL-33 showed decreased Paneth cell numbers and lethal systemic infection in response to Salmonella typhimurium. IL-33 was produced upon microbial challenge by a distinct population of pericryptal fibroblasts neighboring the intestinal stem cell niche. IL-33 programmed the differentiation of epithelial progenitors toward secretory IEC including Paneth and goblet cells. Finally, IL-33 suppressed Notch signaling in epithelial cells and induced expression of transcription factors governing differentiation into secretory IEC. In summary, we demonstrate that gut pericryptal fibroblasts release IL-33 to translate bacterial infection into an epithelial response to promote antimicrobial defense.

VEGFR2 Signaling Prevents Colorectal Cancer Cell Senescence to Promote Tumorigenesis in Mice With Colitis

BACKGROUND & AIMS

Senescence prevents cellular transformation. We investigated whether vascular endothelial growth factor (VEGF) signaling via its receptor, VEGFR2, regulates senescence and proliferation of tumor cells in mice with colitis-associated cancer (CAC).

METHODS

CAC was induced in VEGFR2(ΔIEC) mice, which do not express VEGFR2 in the intestinal epithelium, and VEGFR2(fl/fl) mice (controls) by administration of azoxymethane followed by dextran sodium sulfate. Tumor development and inflammation were determined by endoscopy. Colorectal tissues were collected for immunoblot, immunohistochemical, and quantitative polymerase chain reaction analyses. Findings from mouse tissues were confirmed in human HCT116 colorectal cancer cells. We analyzed colorectal tumor samples from patients before and after treatment with bevacizumab.

RESULTS

After colitis induction, VEGFR2(ΔIEC) mice developed significantly fewer tumors than control mice. A greater number of intestinal tumor cells from VEGFR2(ΔIEC) mice were in senescence than tumor cells from control mice. We found VEGFR2 to activate phosphatidylinositol-4,5-bisphosphate-3-kinase and AKT, resulting in inactivation of p21 in HCT116 cells. Inhibitors of VEGFR2 and AKT induced senescence in HCT116 cells. Tumor cell senescence promoted an anti-tumor immune response by CD8(+) T cells in mice. Patients whose tumor samples showed an increase in the proportion of senescent cells after treatment with bevacizumab had longer progression-free survival than patients in which the proportion of senescent tumor cells did not change before and after treatment.

CONCLUSIONS

Inhibition of VEGFR2 signaling leads to senescence of human and mouse colorectal cancer cells. VEGFR2 interacts with phosphatidylinositol-4,5-bisphosphate-3-kinase and AKT to inactivate p21. Colorectal tumor senescence and p21 level correlate with patient survival during treatment with bevacizumab.

Colitis-associated neoplasia: molecular basis and clinical translation

Crohn's disease and ulcerative colitis are both associated with an increased risk of inflammation-associated colorectal carcinoma. Colitis-associated cancer (CAC) is one of the most important causes for morbidity and mortality in patients with inflammatory bowel diseases (IBD). Colitis-associated neoplasia distinctly differs from sporadic colorectal cancer in its biology and the underlying mechanisms. This review discusses the molecular mechanisms of CAC and summarizes the most important genetic alterations and signaling pathways involved in inflammatory carcinogenesis. Then, clinical translation is evaluated by discussing new endoscopic techniques and their contribution to surveillance and early detection of CAC. Last, we briefly address different types of concepts for prevention (i.e., anti-inflammatory therapeutics) and treatment (i.e., surgical intervention) of CAC and give an outlook on this important aspect of IBD.

Innate and adaptive immunity in inflammatory bowel diseases

While barrier function and the effects of the intestinal microbiome have only recently moved into the focus of inflammatory bowel disease research, the role of the innate and the adaptive immune system in these gastrointestinal disorders has extensively been studied. Although still not completely understood, the increasing knowledge about the immune system's contribution to the pathophysiology of inflammatory bowel diseases has led to new diagnostic and therapeutic approaches. This review gives a compact overview on this important topic.

Interleukin-6--a key regulator of colorectal cancer development

Growing evidence proposes an important role for pro-inflammatory cytokines during tumor development. Several experimental and clinical studies have linked the pleiotropic cytokine interleukin-6 (IL-6) to the pathogenesis of sporadic and inflammation-associated colorectal cancer (CRC). Increased IL-6 expression has been related to advanced stage of disease and decreased survival in CRC patients. According to experimental studies, these effects are mediated through IL-6 trans-signaling promoting tumor cell proliferation and inhibiting apoptosis through gp130 activation on tumor cells with subsequent signaling through Janus kinases (JAKs) and signal transducer and activator of transcription 3 (STAT3).

During recent years, several therapeutics targeting the IL-6/STAT3 pathway have been developed and pose a promising strategy for the treatment of CRC. This review discusses the molecular mechanisms and possible therapeutic targets involved in IL-6 signaling in CRC.

Colitis and colorectal cancer

Inflammatory bowel diseases (IBD) are accompanied by an increased risk of developing colitis-associated carcinoma (CAC). These tumors are one of the most important causes of morbidity and mortality in patients with IBD and distinctly differ from sporadic colorectal cancer in their biology and underlying mechanisms. First, this review discusses risk factors for the development of CAC and summarizes some of the most important genetic alterations and molecular pathways involved in inflammatory carcinogenesis. Then, new endoscopic techniques, such as chromoendoscopy and confocal laser endomicroscopy, and their contribution to surveillance and early detection of CAC are presented. Last, we briefly address different types of concepts for prevention (i.e. anti-inflammatory agents) and treatment (i.e. surgical resection) of CAC and give an outlook on this important aspect of IBD.

Molecular imaging of VEGF in gastrointestinal cancer in vivo using confocal laser endomicroscopy

BACKGROUND

Vascular endothelial growth factor (VEGF) is a therapeutic target in gastrointestinal cancer (GiC). However, its in vivo visualisation could not be achieved to date with endoscopic techniques. Confocal laser endomicroscopy (CLE) is a novel imaging technique for gastrointestinal endoscopy providing in vivo microscopy at subcellular resolution. The aim of the study was to evaluate CLE for in vivo molecular imaging of VEGF in GiC.

METHODS

Molecular imaging of tumours in APCmin mice, in xenograft models and in surgical specimens of patients with colorectal cancer (CRC) was achieved after application of labelled antibodies. The tumour sites were scanned with the probe for the strongest specific fluorescent signal. From all tumour sites examined with CLE in vivo, targeted specimens were obtained for histology, immunohistochemistry (IHC) and fluorescence microscopy.

RESULTS

A VEGF-specific signal was visualised in vivo in 13/15 APCmin mice and in 9/10 xenograft tumours. CLE enabled the cytoplasmatic distribution of VEGF to be displayed due to its subcellular resolution. In human tissue, a VEGF-specific signal was observed in 12/13 malignant specimens and in 10/11 samples from healthy mucosa from the patients (p<0.03). CLE findings correlated well with ex vivo microscopy.

CONCLUSION

In vivo molecular imaging with specific targeting of VEGF is possible in murine tumours, human xenografts and tissue specimens using CLE. CLE with similar probes can be performed in human colonoscopy. Therefore-from a technical point of view-in vivo molecular imaging is transferable to stratification of patients with CRC during endoscopy even today. CLE could contribute to the identification of lesions at risk and potentially predict response to targeted treatment.

In vivo molecular imaging of colorectal cancer with confocal endomicroscopy by targeting epidermal growth factor receptor

BACKGROUND & AIMS

Epidermal growth factor receptor (EGFR) is a therapeutic target for colorectal cancer (CRC). However, technical challenges have limited in vivo imaging of EGFR in CRCs. Confocal laser endomicroscopy (CLE) enables accurate microscopic visualization of CRC in patients during endoscopy. We evaluated the ability to use CLE in vivo for instantaneous molecular imaging of EGFR in CRC models.

METHODS

Tumors were grown in mice (n = 68) from human CRC cell lines that expressed high (SW480 cells) or low (SW620 cells) levels of EGFR. Tumors were visualized in vivo with a handheld CLE probe after injection of fluorescently labeled EGFR antibodies. EGFR-specific fluorescence was graded from 0 to 3+. Neoplastic and non-neoplastic specimens from human colorectal mucosa were examined. In vivo findings were correlated with histopathology, immunohistochemistry, and fluorescence microscopy analyses.

RESULTS

CLE analysis of cell cultures confirmed the different expression levels of EGFR between cell lines. In living animals, CLE differentiated EGFR expression levels between tumor cell limes (mean fluorescence, 1.92 +/- 0.22 [SW480] and 0.59 +/- 0.21 [SW620], P = .0004). CLE analysis of EGFR expression in human specimens allowed distinction of neoplastic from non-neoplastic tissues (mean fluorescence, 2.0 +/- 0.37 vs 0.25 +/- 0.16, respectively, P = .0035).

CONCLUSIONS

CLE can be used for in vivo, molecular analysis of CRC and to differentiate EGFR expression patterns in xenograft tumors and human tissue samples. Because CLE can be performed during endoscopy, in vivo molecular imaging might be used in diagnosis of CRC and to predict response to targeted therapies.