Tumour budding as a prognostic biomarker in biopsies and resections of neoadjuvant-treated rectal adenocarcinoma.

BACKGROUND: Tumour budding (TB) is a marker of tumour aggressiveness which, when measured in rectal cancer resection specimens, predicts worse outcomes and response to neoadjuvant therapy. We investigated the utility of TB assessment in the setting of neoadjuvant treatment. METHODS AND RESULTS: A single-centre, retrospective cohort study was conducted. TB was assessed using the hot-spot International Tumour Budding Consortium (ITBCC) method and classified by the revised ITBCC criteria. Haematoxylin and eosin (H&E) and AE1/AE3 cytokeratin (CK) stains for ITB (intratumoural budding) in biopsies with PTB (peritumoural budding) and ITB (intratumoural budding) in resection specimens were compared. Logistic regression assessed budding as predictors of lymph node metastasis (LNM). Cox regression and Kaplan-Meier analyses investigated their utility as a predictor of disease-free (DFS) and overall (OS) survival. A total of 146 patients were included; 91 were male (62.3%). Thirty-seven cases (25.3%) had ITB on H&E and 79 (54.1%) had ITB on CK assessment of biopsy tissue. In univariable analysis, H&E ITB [odds (OR) = 2.709, 95% confidence interval (CI) = 1.261-5.822, P = 0.011] and CK ITB (OR = 2.165, 95% CI = 1.076-4.357, P = 0.030) predicted LNM. Biopsy-assessed H&E ITB (OR = 2.749, 95% CI = 1.258-6.528, P = 0.022) was an independent predictor of LNM. In Kaplan-Meier analysis, ITB identified on biopsy was associated with worse OS (H&E, P = 0.003, CK: P = 0.009) and DFS (H&E, P = 0.012; CK, P = 0.045). In resection specimens, CK PTB was associated with worse OS (P = 0.047), and both CK PTB and ITB with worse DFS (PTB, P = 0.014; ITB: P = 0.019). In multivariable analysis H&E ITB predicted OS (HR = 2.930, 95% CI = 1.261-6.809) and DFS (HR = 2.072, 95% CI = 1.031-4.164). CK PTB grading on resection also independently predicted OS (HR = 3.417, 95% CI = 1.45-8.053, P = 0.005). CONCLUSION: Assessment of TB using H&E and CK may be feasible in rectal cancer biopsy and post-neoadjuvant therapy-treated resection specimens and is associated with LNM and worse survival outcomes. Future management strategies for rectal cancer might be tailored to incorporate these findings.

Lymph node metastases and recurrence in pT1 colorectal cancer: Prediction with the International Budding Consortium Score-A retrospective, multi-centric study.

BACKGROUND: The International Collaboration on Cancer Reporting proposes histological tumour type, lymphovascular invasion, tumour grade, perineural invasion, extent, and dimensions of invasion as risk factors for lymph node metastases and tumour progression in completely endoscopically resected pT1 colorectal cancer (CRC). OBJECTIVE: The aim of the study was to propose a predictive and reliable score to optimise the clinical management of endoscopically resected pT1 CRC patients. METHODS: This multi-centric, retrospective International Budding Consortium (IBC) study included an international pT1 CRC cohort of 565 patients. All cases were reviewed by eight expert gastrointestinal pathologists. All risk factors were reported according to international guidelines. Tumour budding and immune response (CD8+ T-cells) were assessed with automated models using artificial intelligence. We used the information on risk factors and least absolute shrinkage and selection operator logistic regression to develop a prediction model and generate a score to predict the occurrence of lymph node metastasis or cancer recurrence. RESULTS: The IBC prediction score included the following parameters: lymphovascular invasion, tumour buds, infiltration depth and tumour grade. The score has an acceptable discrimination power (area under the curve of 0.68 [95% confidence intervals (CI) 0.61-0.75]; 0.64 [95% CI 0.57-0.71] after internal validation). At a cut-off of 6.8 points to discriminate high-and low-risk patients, the score had a sensitivity and specificity of 0.9 [95% CI 0.8-0.95] and 0.26 [95% 0.22, 0.3], respectively. CONCLUSION: The IBC score is based on well-established risk factors and is a promising tool with clinical utility to support the management of pT1 CRC patients.

The Complexity of Shapes: How the Circularity of Tumor Nodules Affects Prognosis in Colorectal Cancer.

The current stratification of tumor nodules in colorectal cancer (CRC) staging is subjective and leads to high interobserver variability. In this study, the objective assessment of the shape of lymph node metastases (LNMs), extranodal extension (ENE), and tumor deposits (TDs) was correlated with outcomes. A test cohort and a validation cohort were included from 2 different institutions. The test cohort consisted of 190 cases of stage III CRC. Slides with LNMs and TDs were annotated and processed using a segmentation algorithm to determine their shape. The complexity ratio was calculated for every shape and correlated with outcomes. A cohort of 160 stage III CRC cases was used to validate findings. TDs showed significantly more complex shapes than LNMs with ENE, which were more complex than LNMs without ENE (P < .001). In the test cohort, patients with the highest sum of complexity ratios had significantly lower disease-free survival (P < .01). When only the nodule with the highest complexity was considered, this effect was even stronger (P < .001). This maximum complexity ratio per patient was identified as an independent prognostic factor in the multivariate analysis (hazard ratio, 2.47; P < .05). The trends in the validation cohort confirmed the results. More complex nodules in stage III CRC were correlated with significantly worse disease-free survival, even if only based on the most complex nodule. These results suggest that more complex nodules reflect more invasive tumor biology. As most of the more complex nodules were diagnosed as TDs, we suggest providing a more prominent role for TDs in the nodal stage and include an objective complexity measure in their definition.

Pathologist Computer-Aided Diagnostic Scoring of Tumor Cell Fraction: A Swiss National Study.

Tumor cell fraction (TCF) estimation is a common clinical task with well-established large interobserver variability. It thus provides an ideal test bed to evaluate potential impacts of employing a tumor cell fraction computer-aided diagnostic (TCFCAD) tool to support pathologists' evaluation. During a National Slide Seminar event, pathologists (n = 69) were asked to visually estimate TCF in 10 regions of interest (ROIs) from hematoxylin and eosin colorectal cancer images intentionally curated for diverse tissue compositions, cellularity, and stain intensities. Next, they re-evaluated the same ROIs while being provided a TCFCAD-created overlay highlighting predicted tumor vs nontumor cells, together with the corresponding TCF percentage. Participants also reported confidence levels in their assessments using a 5-tier scale, indicating no confidence to high confidence, respectively. The TCF ground truth (GT) was defined by manual cell-counting by experts. When assisted, interobserver variability significantly decreased, showing estimates converging to the GT. This improvement remained even when TCFCAD predictions deviated slightly from the GT. The standard deviation (SD) of the estimated TCF to the GT across ROIs was 9.9% vs 5.8% with TCFCAD (P < .0001). The intraclass correlation coefficient increased from 0.8 to 0.93 (95% CI, 0.65-0.93 vs 0.86-0.98), and pathologists stated feeling more confident when aided (3.67 ± 0.81 vs 4.17 ± 0.82 with the computer-aided diagnostic [CAD] tool). TCFCAD estimation support demonstrated improved scoring accuracy, interpathologist agreement, and scoring confidence. Interestingly, pathologists also expressed more willingness to use such a CAD tool at the end of the survey, highlighting the importance of training/education to increase adoption of CAD systems.

Pathologists' first opinions on barriers and facilitators of computational pathology adoption in oncological pathology: an international study.

Computational pathology (CPath) algorithms detect, segment or classify cancer in whole slide images, approaching or even exceeding the accuracy of pathologists. Challenges have to be overcome before these algorithms can be used in practice. We therefore aim to explore international perspectives on the future role of CPath in oncological pathology by focusing on opinions and first experiences regarding barriers and facilitators. We conducted an international explorative eSurvey and semi-structured interviews with pathologists utilizing an implementation framework to classify potential influencing factors. The eSurvey results showed remarkable variation in opinions regarding attitude, understandability and validation of CPath. Interview results showed that barriers focused on the quality of available evidence, while most facilitators concerned strengths of CPath. A lack of consensus was present for multiple factors, such as the determination of sufficient validation using CPath, the preferred function of CPath within the digital workflow and the timing of CPath introduction in pathology education. The diversity in opinions illustrates variety in influencing factors in CPath adoption. A next step would be to quantitatively determine important factors for adoption and initiate validation studies. Both should include clear case descriptions and be conducted among a more homogenous panel of pathologists based on sub specialization.

Multi-Institutional Evaluation of Pathologists' Assessment Compared to Immunoscore.

BACKGROUND: The Immunoscore (IS) is a quantitative digital pathology assay that evaluates the immune response in cancer patients. This study reports on the reproducibility of pathologists' visual assessment of CD3+- and CD8+-stained colon tumors, compared to IS quantification. METHODS: An international group of expert pathologists evaluated 540 images from 270 randomly selected colon cancer (CC) cases. Concordance between pathologists' T-score, corresponding hematoxylin-eosin (H&E) slides, and the digital IS was evaluated for two- and three-category IS. RESULTS: Non-concordant T-scores were reported in more than 92% of cases. Disagreement between semi-quantitative visual assessment of T-score and the reference IS was observed in 91% and 96% of cases before and after training, respectively. Statistical analyses showed that the concordance index between pathologists and the digital IS was weak in two- and three-category IS, respectively. After training, 42% of cases had a change in T-score, but no improvement was observed with a Kappa of 0.465 and 0.374. For the 20% of patients around the cut points, no concordance was observed between pathologists and digital pathology analysis in both two- and three-category IS, before or after training (all Kappa < 0.12). CONCLUSIONS: The standardized IS assay outperformed expert pathologists' T-score evaluation in the clinical setting. This study demonstrates that digital pathology, in particular digital IS, represents a novel generation of immune pathology tools for reproducible and quantitative assessment of tumor-infiltrated immune cell subtypes.

Pseudobudding: ruptured glands do not represent true tumor buds.

Tumor budding (TB) is a strong biomarker of poor prognosis in colorectal cancer and other solid cancers. TB is defined as isolated single cancer cells or clusters of up to four cancer cells at the invasive tumor front. In areas with a large inflammatory response at the invasive front, single cells and cell clusters surrounding fragmented glands are observed appearing like TB. Occurrence of these small groups is referred to as pseudobudding (PsB), which arises due to external influences such as inflammation and glandular disruption. Using a combination of orthogonal approaches, we show that there are clear biological differences between TB and PsB. TB is representative of active invasion by presenting features of epithelial-mesenchymal transition and exhibiting increased deposition of extracellular matrix within the surrounding tumor microenvironment (TME), whereas PsB represents a reactive response to heavy inflammation where increased levels of granulocytes within the surrounding TME are observed. Our study provides evidence that areas with a strong inflammatory reaction should be avoided in the routine diagnostic assessment of TB. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Fully Automated Tumor Bud Assessment in Hematoxylin and Eosin-Stained Whole Slide Images of Colorectal Cancer.

Tumor budding (TB), the presence of single cells or small clusters of up to 4 tumor cells at the invasive front of colorectal cancer (CRC), is a proven risk factor for adverse outcomes. International definitions are necessary to reduce interobserver variability. According to the current international guidelines, hotspots at the invasive front should be counted in hematoxylin and eosin (H&E)-stained slides. This is time-consuming and prone to interobserver variability; therefore, there is a need for computer-aided diagnosis solutions. In this study, we report an artificial intelligence-based method for detecting TB in H&E-stained whole slide images. We propose a fully automated pipeline to identify the tumor border, detect tumor buds, characterize them based on the number of tumor cells, and produce a TB density map to identify the TB hotspot. The method outputs the TB count in the hotspot as a computational biomarker. We show that the proposed automated TB detection workflow performs on par with a panel of 5 pathologists at detecting tumor buds and that the hotspot-based TB count is an independent prognosticator in both the univariate and the multivariate analysis, validated on a cohort of n = 981 patients with CRC. Computer-aided detection of tumor buds based on deep learning can perform on par with expert pathologists for the detection and quantification of tumor buds in H&E-stained CRC histopathology slides, strongly facilitating the introduction of budding as an independent prognosticator in clinical routine and clinical trials.

Semi-Supervised Learning to Automate Tumor Bud Detection in Cytokeratin-Stained Whole-Slide Images of Colorectal Cancer.

Tumor budding is a histopathological biomarker associated with metastases and adverse survival outcomes in colorectal carcinoma (CRC) patients. It is characterized by the presence of single tumor cells or small clusters of cells within the tumor or at the tumor-invasion front. In order to obtain a tumor budding score for a patient, the region with the highest tumor bud density must first be visually identified by a pathologist, after which buds will be counted in the chosen hotspot field. The automation of this process will expectedly increase efficiency and reproducibility. Here, we present a deep learning convolutional neural network model that automates the above procedure. For model training, we used a semi-supervised learning method, to maximize the detection performance despite the limited amount of labeled training data. The model was tested on an independent dataset in which human- and machine-selected hotspots were mapped in relation to each other and manual and machine detected tumor bud numbers in the manually selected fields were compared. We report the results of the proposed method in comparison with visual assessment by pathologists. We show that the automated tumor bud count achieves a prognostic value comparable with visual estimation, while based on an objective and reproducible quantification. We also explore novel metrics to quantify buds such as density and dispersion and report their prognostic value. We have made the model available for research use on the grand-challenge platform.

Computer-Assisted Diagnosis of Lymph Node Metastases in Colorectal Cancers Using Transfer Learning With an Ensemble Model.

Screening of lymph node metastases in colorectal cancer (CRC) can be a cumbersome task, but it is amenable to artificial intelligence (AI)-assisted diagnostic solution. Here, we propose a deep learning-based workflow for the evaluation of CRC lymph node metastases from digitized hematoxylin and eosin-stained sections. A segmentation model was trained on 100 whole-slide images (WSIs). It achieved a Matthews correlation coefficient of 0.86 (±0.154) and an acceptable Hausdorff distance of 135.59 µm (±72.14 µm), indicating a high congruence with the ground truth. For metastasis detection, 2 models (Xception and Vision Transformer) were independently trained first on a patch-based breast cancer lymph node data set and were then fine-tuned using the CRC data set. After fine-tuning, the ensemble model showed significant improvements in the F1 score (0.797-0.949; P <.00001) and the area under the receiver operating characteristic curve (0.959-0.978; P <.00001). Four independent cohorts (3 internal and 1 external) of CRC lymph nodes were used for validation in cascading segmentation and metastasis detection models. Our approach showed excellent performance, with high sensitivity (0.995, 1.0) and specificity (0.967, 1.0) in 2 validation cohorts of adenocarcinoma cases (n = 3836 slides) when comparing slide-level labels with the ground truth (pathologist reports). Similarly, an acceptable performance was achieved in a validation cohort (n = 172 slides) with mucinous and signet-ring cell histology (sensitivity, 0.872; specificity, 0.936). The patch-based classification confidence was aggregated to overlay the potential metastatic regions within each lymph node slide for visualization. We also applied our method to a consecutive case series of lymph nodes obtained over the past 6 months at our institution (n = 217 slides). The overlays of prediction within lymph node regions matched 100% when compared with a microscope evaluation by an expert pathologist. Our results provide the basis for a computer-assisted diagnostic tool for easy and efficient lymph node screening in patients with CRC.

Multicenter International Study of the Consensus Immunoscore for the Prediction of Relapse and Survival in Early-Stage Colon Cancer.

Background: The prognostic value of Immunoscore was evaluated in Stage II/III colon cancer (CC) patients, but it remains unclear in Stage I/II, and in early-stage subgroups at risk. An international Society for Immunotherapy of Cancer (SITC) study evaluated the pre-defined consensus Immunoscore in tumors from 1885 AJCC/UICC-TNM Stage I/II CC patients from Canada/USA (Cohort 1) and Europe/Asia (Cohort 2). METHODS: Digital-pathology is used to quantify the densities of CD3+ and CD8+ T-lymphocyte in the center of tumor (CT) and the invasive margin (IM). The time to recurrence (TTR) was the primary endpoint. Secondary endpoints were disease-free survival (DFS), overall survival (OS), prognosis in Stage I, Stage II, Stage II-high-risk, and microsatellite-stable (MSS) patients. RESULTS: High-Immunoscore presented with the lowest risk of recurrence in both cohorts. In Stage I/II, recurrence-free rates at 5 years were 78.4% (95%-CI, 74.4−82.6), 88.1% (95%-CI, 85.7−90.4), 93.4% (95%-CI, 91.1−95.8) in low, intermediate and high Immunoscore, respectively (HR (Hi vs. Lo) = 0.27 (95%-CI, 0.18−0.41); p < 0.0001). In Cox multivariable analysis, the association of Immunoscore to outcome was independent (TTR: HR (Hi vs. Lo) = 0.29, (95%-CI, 0.17−0.50); p < 0.0001) of the patient's gender, T-stage, sidedness, and microsatellite instability-status (MSI). A significant association of Immunoscore with survival was found for Stage II, high-risk Stage II, T4N0 and MSS patients. The Immunoscore also showed significant association with TTR in Stage-I (HR (Hi vs. Lo) = 0.07 (95%-CI, 0.01−0.61); P = 0.016). The Immunoscore had the strongest (69.5%) contribution χ2 for influencing survival. Patients with a high Immunoscore had prolonged TTR in T4N0 tumors even for patients not receiving chemotherapy, and the Immunoscore remained the only significant parameter in multivariable analysis. CONCLUSION: In early CC, low Immunoscore reliably identifies patients at risk of relapse for whom a more intensive surveillance program or adjuvant treatment should be considered.

Impact of scanner variability on lymph node segmentation in computational pathology.

Computer-aided diagnostics in histopathology are based on the digitization of glass slides. However, heterogeneity between the images generated by different slide scanners can unfavorably affect the performance of computational algorithms. Here, we evaluate the impact of scanner variability on lymph node segmentation due to its clinical importance in colorectal cancer diagnosis. 100 slides containing 276 lymph nodes were digitized using 4 different slide scanners, and 50 of the lymph nodes containing metastatic cancer cells. These 400 scans were subsequently annotated by 2 experienced pathologists to precisely label lymph node boundary. Three different segmentation methods were then applied and compared: Hematoxylin-channel-based thresholding (HCT), Hematoxylin-based active contours (HAC), and a convolution neural network (U-Net). Evaluation of U-Net trained from both a single scanner and an ensemble of all scanners was completed. Mosaic images based on representative tiles from a scanner were used as a reference image to normalize the new data from different test scanners to evaluate the performance of a pre-trained model. Fine-tuning was carried out by using weights of a model trained on one scanner to initialize model weights for other scanners. To evaluate the domain generalization, domain adversarial learning and stain mix-up augmentation were also implemented. Results show that fine-tuning and domain adversarial learning decreased the impact of scanner variability and greatly improved segmentation across scanners. Overall, U-Net with stain mix-up (Matthews correlation coefficient (MCC) = 0.87), domain adversarial learning (MCC = 0.86), and HAC (MCC = 0.87) were shown to outperform HCT (MCC = 0.81) for segmentation of lymph nodes when compared against the ground truth. The findings of this study should be considered for future algorithms applied in diagnostic routines.

Clinical Performance of the Consensus Immunoscore in Colon Cancer in the Asian Population from the Multicenter International SITC Study.

BACKGROUND: In this study, we evaluated the prognostic value of Immunoscore in patients with stage I−III colon cancer (CC) in the Asian population. These patients were originally included in an international study led by the Society for Immunotherapy of Cancer (SITC) on 2681 patients with AJCC/UICC-TNM stages I−III CC. METHODS: CD3+ and cytotoxic CD8+ T-lymphocyte densities were quantified in the tumor and invasive margin by digital pathology. The association of Immunoscore with prognosis was evaluated for time to recurrence (TTR), disease-free survival (DFS), and overall survival (OS). RESULTS: Immunoscore stratified Asian patients (n = 423) into different risk categories and was not impacted by age. Recurrence-free rates at 3 years were 78.5%, 85.2%, and 98.3% for a Low, Intermediate, and High Immunoscore, respectively (HR[Low-vs-High] = 7.26 (95% CI 1.75−30.19); p = 0.0064). A High Immunoscore showed a significant association with prolonged TTR, OS, and DFS (p < 0.05). In Cox multivariable analysis stratified by center, Immunoscore association with TTR was independent (HR[Low-vs-Int+High] = 2.22 (95% CI 1.10−4.55) p = 0.0269) of the patient's gender, T-stage, N-stage, sidedness, and MSI status. A significant association of a High Immunoscore with prolonged TTR was also found among MSS (HR[Low-vs-Int+High] = 4.58 (95% CI 2.27−9.23); p ≤ 0.0001), stage II (HR[Low-vs-Int+High] = 2.72 (95% CI 1.35−5.51); p = 0.0052), low-risk stage-II (HR[Low-vs-Int+High] = 2.62 (95% CI 1.21−5.68); p = 0.0146), and high-risk stage II patients (HR[Low-vs-Int+High] = 3.11 (95% CI 1.39−6.91); p = 0.0055). CONCLUSION: A High Immunoscore is significantly associated with the prolonged survival of CC patients within the Asian population.

A combined spatial score of granzyme B and CD68 surpasses CD8 as an independent prognostic factor in TNM stage II colorectal cancer.

BACKGROUND: Previous assessments of peritumoral inflammatory infiltrate in colorectal cancer (CRC) have focused on the role of CD8+ T lymphocytes. We sought to compare the prognostic value of CD8 with downstream indicators of active immune cell function, specifically granzyme B (GZMB) and CD68 in the tumour microenvironment. METHODS: Immunohistochemical (IHC) staining was performed for CD8, GZMB, CD68 and CD163 on next-generation tissue microarrays (ngTMAs) in a primary cohort (n = 107) and a TNM stage II validation cohort (n = 151). Using digital image analysis, frequency of distinct immune cell types was calculated for tumour proximity (TP) zones with varying radii (10 µm-100 µm) around tumour cells. RESULTS: Associations notably of advanced TNM stage were observed for low density of CD8 (p = 0.002), GZMB (p < 0.001), CD68 (p = 0.034) and CD163 (p = 0.011) in the primary cohort. In the validation cohort only low GZMB (p = 0.036) was associated with pT4 stage. Survival analysis showed strongest prognostic effects in the TP25µm zone at the tumour centre for CD8, GZMB and CD68 (all p < 0.001) in the primary cohort and for CD8 (p = 0.072), GZMB (p = 0.035) and CD68 (p = 0.004) in the validation cohort with inferior prognostic effects observed at the tumour invasive margin. In a multivariate survival analysis, joint analysis of GZMB and CD68 was similarly prognostic to CD8 in the primary cohort (p = 0.007 vs. p = 0.002) and superior to CD8 in the validation cohort (p = 0.005 vs. p = 0.142). CONCLUSION: Combined high expression of GZMB and CD68 within 25 µm to tumour cells is an independent prognostic factor in CRC and of superior prognostic value to the well-established CD8 in TNM stage II cancers. Thus, assessment of antitumoral effect should consider the quality of immune activation in peritumoral inflammatory cells and their actual proximity to tumour cells.

[Tumor budding in colorectal cancer-Information for clinical use and instructions for practical evaluation]. / Tumor Budding beim kolorektalen Karzinom ­ Informationen zur klinischen Anwendung und Anleitung zur praktischen Bestimmung.

BACKGROUND: Some patients with high-risk colorectal cancer show a worse prognosis within the same UICC stage. Therefore, the identification of additional risk factors is necessary to find the best treatment for these patients. OBJECTIVE: In which settings can tumor budding help the clinical decision-making process for treatment planning and how should scoring be performed? MATERIAL AND METHODS: Evaluation of current publications on tumor budding with an emphasis on practical grading and potential problems in the determination of tumor budding. RESULTS: Tumor budding is a significant risk factor for worse clinical outcome of colorectal cancer and can influence clinical decision-making in pT1 and stage II colorectal cancer. A scoring method was standardized by the ITBCC 2016 and is feasible in everyday practice. Challenges in assessment can be addressed by increasing awareness of potential problem cases.

Image-based assessment of extracellular mucin-to-tumor area predicts consensus molecular subtypes (CMS) in colorectal cancer.

The backbone of all colorectal cancer classifications including the consensus molecular subtypes (CMS) highlights microsatellite instability (MSI) as a key molecular pathway. Although mucinous histology (generally defined as >50% extracellular mucin-to-tumor area) is a "typical" feature of MSI, it is not limited to this subgroup. Here, we investigate the association of CMS classification and mucin-to-tumor area quantified using a deep learning algorithm, and  the expression of specific mucins in predicting CMS groups and clinical outcome. A weakly supervised segmentation method was developed to quantify extracellular mucin-to-tumor area in H&E images. Performance was compared to two pathologists' scores, then applied to two cohorts: (1) TCGA (n = 871 slides/412 patients) used for mucin-CMS group correlation and (2) Bern (n = 775 slides/517 patients) for histopathological correlations and next-generation Tissue Microarray construction. TCGA and CPTAC (n = 85 patients) were used to further validate mucin detection and CMS classification by gene and protein expression analysis for MUC2, MUC4, MUC5AC and MUC5B. An excellent inter-observer agreement between pathologists' scores and the algorithm was obtained (ICC = 0.92). In TCGA, mucinous tumors were predominantly CMS1 (25.7%), CMS3 (24.6%) and CMS4 (16.2%). Average mucin in CMS2 was 1.8%, indicating negligible amounts. RNA and protein expression of MUC2, MUC4, MUC5AC and MUC5B were low-to-absent in CMS2. MUC5AC protein expression correlated with aggressive tumor features (e.g., distant metastases (p = 0.0334), BRAF mutation (p < 0.0001), mismatch repair-deficiency (p < 0.0001), and unfavorable 5-year overall survival (44% versus 65% for positive/negative staining). MUC2 expression showed the opposite trend, correlating with less lymphatic (p = 0.0096) and venous vessel invasion (p = 0.0023), no impact on survival.The absence of mucin-expressing tumors in CMS2 provides an important phenotype-genotype correlation. Together with MSI, mucinous histology may help predict CMS classification using only histopathology and should be considered in future image classifiers of molecular subtypes.

Dataset for Pathology Reporting of Colorectal Cancer: Recommendations From the International Collaboration on Cancer Reporting (ICCR).

OBJECTIVE: The aim of this study to describe a new international dataset for pathology reporting of colorectal cancer surgical specimens, produced under the auspices of the International Collaboration on Cancer Reporting (ICCR). BACKGROUND: Quality of pathology reporting and mutual understanding between colorectal surgeon, pathologist and oncologist are vital to patient management. Some pathology parameters are prone to variable interpretation, resulting in differing positions adopted by existing national datasets. METHODS: The ICCR, a global alliance of major pathology institutions with links to international cancer organizations, has developed and ratified a rigorous and efficient process for the development of evidence-based, structured datasets for pathology reporting of common cancers. Here we describe the production of a dataset for colorectal cancer resection specimens by a multidisciplinary panel of internationally recognized experts. RESULTS: The agreed dataset comprises eighteen core (essential) and seven non-core (recommended) elements identified from a review of current evidence. Areas of contention are addressed, some highly relevant to surgical practice, with the aim of standardizing multidisciplinary discussion. The summation of all core elements is considered to be the minimum reporting standard for individual cases. Commentary is provided, explaining each element's clinical relevance, definitions to be applied where appropriate for the agreed list of value options and the rationale for considering the element as core or non-core. CONCLUSIONS: This first internationally agreed dataset for colorectal cancer pathology reporting promotes standardization of pathology reporting and enhanced clinicopathological communication. Widespread adoption will facilitate international comparisons, multinational clinical trials and help to improve the management of colorectal cancer globally.

LAG-3 Expression Predicts Outcome in Stage II Colon Cancer.

INTRODUCTION: LAG-3 is an inhibitory immune checkpoint molecule that suppresses T cell activation and inflammatory cytokine secretion. T cell density in the tumor microenvironment of colon cancer plays an important role in the host's immunosurveillance. We therefore hypothesized that LAG-3 expression on tumor-infiltrating lymphocytes (TILs) predicts outcome in patients with stage II colon cancer. PATIENTS AND METHODS: Immunohistochemical staining for LAG-3 was performed on tissue microarrays (TMAs) of formalin-fixed paraffin-embedded tissue from 142 stage II colon cancer patients. LAG-3 expression was assessed in TILs within both the tumor front and tumor center and scored as either positive or negative. The primary endpoint was disease-free survival (DFS). RESULTS: In patients diagnosed with stage II colon cancer, the presence of LAG-3 expression on TILs was significantly associated with better 5-year DFS (HR 0.34, 95% CI 0.14-0.80, p = 0.009). The effect on DFS was mainly due to LAG-3-positive TILs in the tumor front (HR 0.33, 95% CI 0.13-0.82, p = 0.012). CONCLUSION: Assessment of LAG-3 might help to predict outcomes in patients with stage II colon cancer and potentially identify those patients who might benefit from adjuvant chemotherapy. Therefore, LAG-3 may serve as a prognostic biomarker in stage II colon cancer.

RHAMM in liver metastases of stage IV colorectal cancer with mismatch-repair proficient status correlates with tumor budding, cytotoxic T-cells and PD-1/PD-L1.

BACKGROUND: During the last decades, the management for metastatic colorectal cancer patients has improved due to novel therapeutic approaches. A mismatch-repair deficient status seems to favour a better response to checkpoint inhibitor therapy, but the question arises whether a specific subgroup of stage IV patients with mismatch-repair (MMR) proficient status should also be considered. RHAMM (Receptor for Hyaluronic Acid Mediated Motility/HAMMR/CD168) is characterized by tumor progression and immunogenicity. Therefore, the aim of this study is to determine whether RHAMM within the CRLM of MMR-proficient patients correlate with a more immunological microenvironment, represented by cytotoxic T-cells, PD-1 and PD-1. METHODS: Two patient cohorts of liver metastases from MMR colorectal cancers were included into the study (n = 81 and 76) using ngTMA® technology and immunohistochemically analyzed for RHAMM, cytotoxic T-cells (CD8+), PD-1/PD-L1, intrametastatic budding (IMB) and perimetastatic budding (PMB). RESULTS: RHAMM-positive IMB was linked to a higher PD-L1 expression (r = 0.32; p = 0.233 and r = 0.28; p = 0.044) in the center and periphery of the metastasis and RHAMM-positive PMB was associated with a higher expression of PD-1 (r = 0.33; p = 0.0297), and especially PD-L1 (r = 0.604; p < 0.0001 and r = 0.43; p = 0.003) in the center and periphery of the metastasis. IMB and PMB were additionally associated with a higher count of CD8+ T-cells (p < 0.0001; r = 0.58; p < 0.0001; r = 0.53). CONCLUSIONS: The RHAMM status can be assessed in IMB/PMB either in biopsies or in resections of colorectal cancer liver metastases. A positive RHAMM status in IMB and/or PMB may be a potential indicator for a checkpoint inhibitor therapy for stage IV colorectal cancer patients with MMR proficient status.

[The autopsy of Napoleon Bonaparte: Anatomo-pathological assessment for the bicentenary of the death of Napoleon I on the island of Saint Helena in 1821]. / L'autopsie de Napoléon Bonaparte. Mise au point anatomo-pathologique pour le bicentenaire de la mort de Napoléon Ier sur l'île de Sainte-Hélène en 1821.

Napoleon Bonaparte died on 5 May 1821 on the island of St Helena after almost six years of exile. The next day, Dr Francesco Antommarchi, a Corsican doctor chosen by the Bonaparte family to treat the exiled emperor, performed the autopsy in the presence of sixteen people, including seven British doctors. Two hundred years after the event of 6 May 1821, the cause of Napoleon's death is still a mystery. Various hypotheses, such as arsenic intoxication, cardiac arrhythmia or, more recently, anaemia caused by gastrointestinal haemorrhage associated with chronic gastritis, have been put forward in the medical-historical literature. The main reasons for all these debates and misunderstandings are the presence of several autopsy reports, their often unscientific interpretation, as well as a certain taste for mystery. However, from a scientific point of view, the question arises as to whether autopsy reports are really conclusive as to the real cause of death. Thus, on the occasion of the bicentenary of Napoleon I's death in St. Helena, an international group of anatomo-pathologists specialising in digestive pathology set themselves the goal of analysing Napoleon I's autopsy reports according to their level of medical evidence (high, moderate and low). The autopsy reports of 1821 support the hypothesis of advanced malignant neoplasia of the stomach associated with gastric haemorrhage as the immediate cause of Napoleon I's death on 5 May 1821.