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BackgroundConstitutive Wnt activation is essential for colorectal cancer (CRC) initiation but also underlies the cancer stem cell phenotype, metastasis and chemosensitivity. Importantly Wnt activity is still modulated as evidenced by higher Wnt activity at the invasive front of clonal tumours termed the β-catenin paradox. SMAD4 and p53 mutation status and the bone morphogenetic protein (BMP) pathway are known to affect Wnt activity. The combination of SMAD4 loss, p53 mutations and BMP signalling may integrate to influence Wnt signalling and explain the β-catenin paradox.MethodsWe analysed the expression patterns of SMAD4, p53 and β-catenin at the invasive front of CRCs using immunohistochemistry. We activated BMP signalling in CRC cells in vitro and measured BMP/Wnt activity using luciferase reporters. MTT assays were performed to study the effect of BMP signalling on CRC chemosensitivity.ResultsEighty-four percent of CRCs with high nuclear β-catenin staining are SMAD4 negative and/or p53 aberrant. BMP signalling inhibits Wnt signalling in CRC only when p53 and SMAD4 are unaffected. In the absence of SMAD4, BMP signalling activates Wnt signalling. When p53 is lost or mutated, BMP signalling no longer influences Wnt signalling. The cytotoxic effects of 5-FU are influenced in a similar manner.ConclusionsThe BMP signalling pathway differentially modulates Wnt signalling dependent on the SMAD4 and p53 status. The use of BMPs in cancer therapy, as has been proposed by previous studies, should be targeted to individual cancers based on the mutational status of p53 and SMAD4.

A letter to the editor is presented in response to article about the prognostic importance of tumour-stroma ratio in oestrogen receptor-positive breast cancer.

Abstract Background Large number of patients with colorectal liver metastasis show recurrent disease after curative surgical resection. Identification of these high-risk patients may guide therapeutic strategies. The aim of this study was to evaluate whether the presence of disseminated tumor cells in bone marrow from patients undergoing surgical resection of colorectal liver metastases can predict clinical outcome. Methods Sixty patients with colorectal liver metastases were planned for a curative resection between 2001 and 2007. All patients underwent bone marrow aspiration before surgery. Detection of tumor cells was performed using immunocytochemical staining for cytokeratin (CK-ICC) combined with automated microscopy or indirectly using reverse transcription-polymerase chain reaction (RT-PCR). Results Disseminated tumor cells were found in 15 of the 46 patients (33%) using CK-ICC and in 9 of 44 of the patients (20%) using RT-PCR. Patients with negative results for RT-PCR had a significant better disease-free survival after resection of their liver metastases (p = 0.02). This group also showed significant better overall survival (p = 0.002). CK-ICC did not predict a worse clinical outcome. Conclusions The presence of disseminated tumor cells in bone marrow detected using RT-PCR did predict a worse clinical outcome. The presence of cells detected with CK-ICC did not correlate with poor prognosis.

Background: The TNM (tumor-node-metastasis) Evaluation Committee of Union for International Cancer Control (UICC) and College of American Pathologists (CAP) recommended to prospectively validate the cost-effective and robust tumor-stroma ratio (TSR) as an independent prognostic parameter, since high intratumor stromal percentages have previously predicted poor patient-related outcomes., Patients and Methods: The 'Uniform Noting for International application of Tumor-stroma ratio as Easy Diagnostic tool' (UNITED) study enrolled patients in 27 participating centers in 12 countries worldwide. The TSR, categorized as stroma-high (>50%) or stroma-low (≤50%), was scored through standardized microscopic assessment by certified pathologists, and effect on disease-free survival (DFS) was evaluated with 3-year median follow-up. Secondary endpoints were benefit assessment of adjuvant chemotherapy (ACT) and overall survival (OS)., Results: A total of 1537 patients were included, with 1388 eligible stage II/III patients curatively operated between 2015 and 2021. DFS was significantly shorter in stroma-high (n = 428) than in stroma-low patients (n = 960) (3-year rates 70% versus 83%; P < 0.001). In multivariate analysis, TSR remained an independent prognosticator for DFS (P < 0.001, hazard ratio 1.49, 95% confidence interval 1.17-1.90). As secondary outcome, DFS was also worse in stage II and III stroma-high patients despite adjuvant treatment (3-year rates stage II 73% versus 92% and stage III 66% versus 80%; P = 0.008 and P = 0.011, respectively). In stage II patients not receiving ACT (n = 322), the TSR outperformed the American Society of Clinical Oncology (ASCO) criteria in identifying patients at risk of events (event rate 21% versus 9%), with a higher discriminatory 3-year DFS rate (stroma-high 80% versus ASCO high risk 91%). A trend toward worse 5-year OS in stroma-high was noticeable (74% versus 83% stroma-low; P = 0.102)., Conclusion: The multicenter UNITED study unequivocally validates the TSR as an independent prognosticator, confirming worse outcomes in stroma-high patients. The TSR improved current selection criteria for patients at risk of events, and stroma-high patients potentially experienced chemotherapy resistance. TSR implementation in pathology diagnostics and international guidelines is highly recommended as aid in personalized treatment., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

In colorectal cancer (CRC), artificial intelligence (AI) can alleviate the laborious task of characterization and reporting on resected biopsies, including polyps, the numbers of which are increasing as a result of CRC population screening programs ongoing in many countries all around the globe. Here, we present an approach to address two major challenges in the automated assessment of CRC histopathology whole-slide images. We present an AI-based method to segment multiple ([Formula: see text]) tissue compartments in the H &E-stained whole-slide image, which provides a different, more perceptible picture of tissue morphology and composition. We test and compare a panel of state-of-the-art loss functions available for segmentation models, and provide indications about their use in histopathology image segmentation, based on the analysis of (a) a multi-centric cohort of CRC cases from five medical centers in the Netherlands and Germany, and (b) two publicly available datasets on segmentation in CRC. We used the best performing AI model as the basis for a computer-aided diagnosis system that classifies colon biopsies into four main categories that are relevant pathologically. We report the performance of this system on an independent cohort of more than 1000 patients. The results show that with a good segmentation network as a base, a tool can be developed which can support pathologists in the risk stratification of colorectal cancer patients, among other possible uses. We have made the segmentation model available for research use on https://grand-challenge.org/algorithms/colon-tissue-segmentation/ ., (© 2023. The Author(s).)

The choice for adjuvant chemotherapy in stage II colorectal cancer is controversial as many patients are cured by surgery alone and it is difficult to identify patients with high risk of recurrence of the disease. There is a need for better stratification of this group of patients. Mass spectrometry imaging could identify patients at risk. We report here the N-glycosylation signatures of the different cell populations in a group of stage II colorectal cancer tissue samples. The cancer cells, compared with normal epithelial cells, have increased levels of sialylation and high-mannose glycans, as well as decreased levels of fucosylation and highly branched N-glycans. When looking at the interface between cancer and its microenvironment, it seems that the cancer N-glycosylation signature spreads into the surrounding stroma at the invasive front of the tumor. This finding was more outspoken in patients with a worse outcome within this sample group., Competing Interests: Conflict of interest The authors declare no (financial) competing interests., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Background and Purpose: With currently available techniques, the prediction of pathologic complete response after neoadjuvant chemoradiotherapy is insufficient. The tumor-stroma ratio (TSR) has proven to be a predictor of survival for several types of cancer, including esophageal. The aim of this study was to investigate the value of TSR in predicting pathologic response after neoadjuvant chemoradiotherapy in esophageal cancer patients., Materials and Methods: Patients with esophageal adenocarcinoma or squamous cell carcinoma who received neoadjuvant chemoradiotherapy followed by a resection were selected. Haematoxylin and eosin (H&E) stained sections of diagnostic biopsies were collected and TSR was independently assessed by two investigators. Patients were categorized in stroma-low (≤50% stroma) and stroma-high (>50% stroma) groups for further analyses. The tumor regression grade (TRG) was assessed on H&E stained sections of the resected primary tumor to determine pathologic response., Results: A total of 94 patients were included in this study, of which 76 patients were categorized as stroma-low and 18 as stroma-high. Forty-two (45%) patients had a major pathologic response (TRG 1-2), whereas 52 (55%) were considered non-responders. After adjustment for gender, tumor type, cT-status and differentiation grade, patients with a stroma-high tumor showed a higher chance of no response compared to patients with a stroma-low tumor (OR 3.57, 95%CI 1.03-12.31, P  = 0.04)., Conclusion: TSR showed to have the potential to aid in the prediction of pathologic response in esophageal cancer patients receiving neoadjuvant chemoradiotherapy. Larger validation studies are necessary before implementing this method in daily practice., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2019 The Author(s).)

Purpose: Intra-tumoral stroma has become increasingly important in understanding tumor biology, tumor progression and clinical outcome. The amount itself, quantified as the tumor-stroma ratio (TSR), has proven to be prognostic in stage I-III colon cancer. Also, alterations in stromal organization have been found to provide prognostic and predictive information in certain cancers. Here, we evaluated the predictive value of stromal organization in high-risk stage II and III colon cancer with respect to adjuvant bevacizumab and chemotherapy., Methods: In a post-hoc analysis, stromal organization was microscopically determined in hematoxylin and eosin-stained primary tumor tissue samples of 1226 patients enrolled in the AVANT trial., Results: We found that patients with tumors with a disorganized stroma showed different survival rates after the addition of bevacizumab compared to standard oxaliplatin-based chemotherapy regimens. However, overall this difference was not significant with a HR of 0.94 (95% CI 0.57-1.55; p = 0.80) for disease-free survival (DFS) and 1.01 (95% CI 0.51-1.99; p = 0.99) for overall survival (OS). Subgroup analysis, however, revealed that stromal organization combined with TSR allowed the identification of stroma-high patients with absolute cumulative survival benefits up to 15% when bevacizumab was added to oxaliplatin-based chemotherapy regimens., Conclusions: In high-risk stage II and stage III colon cancer, we found that subgroup analysis of the combined parameters stromal organization and TSR allows for the identification of patients with absolute cumulative DFS and OS benefits of up to 15%, when adding bevacizumab to the currently recommended oxaliplatin-based chemotherapy. Stromal organization itself does, however, not serve as an independent prognostic or predictive parameter.

Background: Colon cancer treatment is dependent on the stage at diagnosis. The current Tumor-Node-Metastasis (TNM) staging for the selection of patients for adjuvant chemotherapy needs additional prognostic and predictive biomarkers. Better decision making for chemotherapy will result in reducing over- and undertreatment. We developed a new, easy-to-apply, practice-changing method to select colon cancer patients for adjuvant chemotherapy: the tumor-stroma ratio (TSR). The TSR distinguishes within stage II-III patients who will likely benefit from adjuvant chemotherapy and those who will not., Objective: The aim of the study was to add, in addition to the TNM classification, the TSR to current routine pathology evaluation. Pathologists will be instructed for scoring the TSR in combination with a quality assessment program. An international multicenter study will validate the parameter prospectively., Methods: The study is designed for future implementation of the TSR to the current TNM guidelines, using routinely Haematoxylin- and Eosin-stained tumor tissue sections. In part 1 of the study, an electronic learning (e-learning) module with a quality assessment program using the European Society of Pathology framework will be developed. This module will be used to assess the reliability and reproducibility of the TSR, conducted by national and international pathologists. Part 2 will involve the validation of the TSR in a prospective cohort of colon cancer p-stage II-III patients in a multicenter setting. In total, 1500 patients will be included., Results: The results of part 1 will be expected in the first half of 2019. For part 2, the inclusion of patients in the prospective study, which started at the end of 2018, will take 3 years with an additional follow-up after another 3 years., Conclusions: The main endpoints of this study are as follows: in part 1, trained (international) pathologists who are able to reliably score the TSR, resulting in low intra- and interobserver variation; in part 2, confirmation of significant survival differences for patients with a stroma-high tumor versus patients with a stroma-low tumor. On the basis of these findings, a modification in current treatment guidelines will be suggested., Trial Registration: Netherlands Trial Register NTR7270; https://www.trialregister.nl/trial/7072., International Registered Report Identifier (irrid): DERR1-10.2196/13464., (©Marloes Smit, Gabi van Pelt, Annet Roodvoets, Elma Meershoek-Klein Kranenbarg, Hein Putter, Rob Tollenaar, J Han van Krieken, Wilma Mesker. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 14.06.2019.)

At present, limiting factors in the use of tissue microarrays (TMAs) for high-throughput analysis relate to the visual evaluation of the staining patterns of each of the individual cores in the array and to the subsequent input of the results into a database. Such a database is essential to correlate the data with tumor type and outcome, and to evaluate the performance against other markers achieved in separate experiments. So far, these steps are mostly performed by hand, and consequently are time-consuming and potentially prone to bias and errors, respectively. This paper describes the use of a high-resolution flat-bed scanner for digitization of TMAs with a resolution of about 5 x 5 micro m(2). The arrays are acquired, the positions of the tissue cores are automatically determined, and measurement data including the images of the individual cores are archived. The program provides digital zooming of arrays for interactive verification of the results and rapid linkage of individual core images to data sets of other markers derived from the same array. Performance of the system was compared to manual classification for a representative set of arrays containing colorectal tumors stained with different markers.

This article explores the feasibility of the use of automated microscopy and image analysis to detect the presence of rare fetal nucleated red blood cells (NRBCs) circulating in maternal blood. The rationales for enrichment and for automated image analysis for "rare-event" detection are reviewed. We also describe the application of automated image analysis to 42 maternal blood samples, using a protocol consisting of one-step enrichment followed by immunocytochemical staining for fetal hemoglobin (HbF) and FISH for X- and Y-chromosomal sequences. Automated image analysis consisted of multimode microscopy and subsequent visual evaluation of image memories containing the selected objects. The FISH results were compared with the results of conventional karyotyping of the chorionic villi. By use of manual screening, 43% of the slides were found to be positive (>=1 NRBC), with a mean number of 11 NRBCs (range 1-40). By automated microscopy, 52% were positive, with on average 17 NRBCs (range 1-111). There was a good correlation between both manual and automated screening, but the NRBC yield from automated image analysis was found to be superior to that from manual screening (P=.0443), particularly when the NRBC count was >15. Seven (64%) of 11 XY fetuses were correctly diagnosed by FISH analysis of automatically detected cells, and all discrepancies were restricted to the lower cell-count range. We believe that automated microscopy and image analysis reduce the screening workload, are more sensitive than manual evaluation, and can be used to detect rare HbF-containing NRBCs in maternal blood.

Forty-five patients with advanced ovarian cancer were studied with both DNA flow cytometry (FCM) and automatic DNA image cytometry carried out with the Leiden Television Analysis System (Leytas). There was a significant difference in survival between the diploid and nondiploid cases as determined by FCM. Furthermore, the presence of nuclei with a high DNA content (defined as a DNA content higher than 5C) as determined by Leytas indicated a poor prognosis. When the combined results of FCM and Leytas were taken into account, three different groups of patients could be distinguished. The group of patients with a diploid malignancy (n = 12) had a median survival of more than 60 months. The group of patients (n = 11) with a nondiploid tumor having fewer than 100 nuclei with a high DNA content per 1600 microscope fields formed an intermediate group (median survival, 42 months), whereas the median survival of the remaining patients (n = 22), who had a nondiploid malignancy combined with more than 100 of these nuclei per 1600 microscope fields, was only 15 months. In addition, comparison of the clinical parameters by means of a multivariate analysis (Cox regression model) showed that the combined results of FCM and DNA image cytometry had the largest influence on survival. It is concluded that DNA image cytometry appears to be supplementary to FCM for the study of DNA ploidy abnormalities and that the combined results of these methods have a major influence on the clinical outcome.