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.

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.

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.

Enterocolic phlebitis: a rare cause of bowel ischemia and review of the literature.

Enterocolic phlebitis (EP) is a rare cause of bowel ischemia due to isolated venulitis of the bowel wall and mesentery without arterial involvement. EP is often misdiagnosed as inflammatory bowel disease, carcinoma, or diverticulitis due to non-specific symptoms as well as non-specific clinical and radiological findings. While unresponsive to pharmacotherapy, surgical resection of the affected bowel appears to be the only successful therapy with a very low recurrence rate. Etiology of EP remains unknown. We report a case of EP with rare presentation in the left hemicolon and unusual histological findings emphasizing the heterogeneity of this cause of enterocolic ischemia. The review and comparison of the three entities-EP, mesenteric inflammatory veno-occlusive disease (MIVOD), and idiopathic myointimal hyperplasia of mesenteric veins (IMHMV), all describing patterns of bowel ischemia due to isolated pathology of mesenteric veins-reveal that the current terminology is unclear. EP and MIVOD are very similar and may be considered the same disease. IMHMV, though, differs in localization, symptom duration, and histological findings but also shares features with EP and MIVOD. Further studies and harmonized terminology are inevitable for better understanding of the disease, prevention of unnecessary pharmacotherapy, and reduction in time to diagnosis.

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.

Challenges in the diagnosis of marginal zone lymphoma with symptoms of small intestinal disease: a case report and scoping review of the literature.

Background: Marginal zone lymphoma can be accompanied by symptoms of small intestinal disease including abdominal pain and malabsorption. However, the best diagnostic approach for suspected marginal zone lymphoma is unknown and intestinal biopsies are frequently negative. We describe the case of a patient with symptoms of small bowel involvement where marginal zone lymphoma could only be detected upon peripheral lymph node resection. To assess the clinical variability of intestinal marginal zone lymphoma as a rare clinical entity, a scoping review with systematic literature research was performed. Methods: A 57-year-old man presented with a 10-year history of postprandial abdominal pain, systemic inflammation and recent weight loss. Endoscopies and a surgical small bowel specimen revealed non-specific findings. Flow cytometry from the bone marrow was highly suspicious for marginal zone lymphoma. A 2-18F-fluorodeoxyglucose-positron emission tomography/computed tomography (2-18F-FDG-PET/CT) showed hypermetabolic lymph nodes on both sides of the diaphragm. Cervical lymph node dissection finally confirmed marginal zone lymphoma. Immunochemotherapy yielded lasting oncological remission and resolved symptoms. We searched PubMed, Embase and Ovid MEDLINE® for additional case reports limited to the last 25 years. Five primary search terms combined using "AND" were used freely and as controlled vocabulary. Additional studies were identified by reviewing the reference lists of included articles. Results: Our review revealed 52 cases of marginal zone lymphoma with small intestinal manifestation. Patients presented with abdominal pain, bowel obstruction, weight loss or gastrointestinal bleeding. Diagnosis was mainly established by surgery (73%). The most frequent endoscopic findings were mucosal erosions and ulcerations. A 2-18F-FDG-PET/CT was positive in 9/15 patients. Treatment included rituximab, chemotherapy, surgery and/or radiation resulting in clinical remission in 82% of cases. Conclusions: Diagnostic workup for suspected small intestinal marginal zone lymphoma is challenging, necessitating a multidisciplinary approach. Endoscopy, imaging including 2-18F-FDG-PET/CT and small bowel resection or dissection of hypermetabolic lymph nodes can be useful. If marginal zone lymphoma is suspected vigorous diagnostic efforts are justified since remission can be achieved in most patients. Our review highlights the variable clinical presentation of this underdiagnosed disease and adds systematic data to the literature.

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.

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.

Digital pathology - Rising to the challenge.

Digital pathology has gone through considerable technical advances during the past few years and certain aspects of digital diagnostics have been widely and swiftly adopted in many centers, catalyzed by the COVID-19 pandemic. However, analysis of requirements, careful planning, and structured implementation should to be considered in order to reap the full benefits of a digital workflow. The aim of this review is to provide a practical, concise and hands-on summary of issues relevant to implementing and developing digital diagnostics in the pathology laboratory. These include important initial considerations, possible approaches to overcome common challenges, potential diagnostic pitfalls, validation and regulatory issues and an introduction to the emerging field of image analysis in routine.

Self-rule to multi-adapt: Generalized multi-source feature learning using unsupervised domain adaptation for colorectal cancer tissue detection.

Supervised learning is constrained by the availability of labeled data, which are especially expensive to acquire in the field of digital pathology. Making use of open-source data for pre-training or using domain adaptation can be a way to overcome this issue. However, pre-trained networks often fail to generalize to new test domains that are not distributed identically due to tissue stainings, types, and textures variations. Additionally, current domain adaptation methods mainly rely on fully-labeled source datasets. In this work, we propose Self-Rule to Multi-Adapt (SRMA), which takes advantage of self-supervised learning to perform domain adaptation, and removes the necessity of fully-labeled source datasets. SRMA can effectively transfer the discriminative knowledge obtained from a few labeled source domain's data to a new target domain without requiring additional tissue annotations. Our method harnesses both domains' structures by capturing visual similarity with intra-domain and cross-domain self-supervision. Moreover, we present a generalized formulation of our approach that allows the framework to learn from multiple source domains. We show that our proposed method outperforms baselines for domain adaptation of colorectal tissue type classification in single and multi-source settings, and further validate our approach on an in-house clinical cohort. The code and trained models are available open-source: https://github.com/christianabbet/SRA.

[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.

Atypical Pleomorphic Lipomatous Tumor of the Right Hand Mimicing Venous Malformation.

We present the case of a 31-year-old woman who was referred with a 12-month history of a tumor on the ulnar side of her dominant right hand. The eventual histopathologic diagnosis was an atypical pleomorphous lipomatous tumor, an entity that has only been recently classified in the World Health Organization Classification of Soft Tissue and Bone Tumors.

Intraperitoneal microbial contamination drives post-surgical peritoneal adhesions by mesothelial EGFR-signaling.

Abdominal surgeries are lifesaving procedures but can be complicated by the formation of peritoneal adhesions, intra-abdominal scars that cause intestinal obstruction, pain, infertility, and significant health costs. Despite this burden, the mechanisms underlying adhesion formation remain unclear and no cure exists. Here, we show that contamination of gut microbes increases post-surgical adhesion formation. Using genetic lineage tracing we show that adhesion myofibroblasts arise from the mesothelium. This transformation is driven by epidermal growth factor receptor (EGFR) signaling. The EGFR ligands amphiregulin and heparin-binding epidermal growth factor, are sufficient to induce these changes. Correspondingly, EGFR inhibition leads to a significant reduction of adhesion formation in mice. Adhesions isolated from human patients are enriched in EGFR positive cells of mesothelial origin and human mesothelium shows an increase of mesothelial EGFR expression during bacterial peritonitis. In conclusion, bacterial contamination drives adhesion formation through mesothelial EGFR signaling. This mechanism may represent a therapeutic target for the prevention of adhesions after intra-abdominal surgery.

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.

Remote area nurses' perceptions of the enablers and barriers for delivering end-of-life care in remote Australia to Aboriginal people who choose to pass away on their traditional lands.

INTRODUCTION: This research was undertaken to gain insight into what remote area nurses perceived were enablers and barriers to being involved in delivering care to an Aboriginal person with a terminal diagnosis passing away on their traditional lands. It is hoped that this gives remote area nurses, Aboriginal Australians and service providers a glimpse into what is happening in the remote areas of Australia. Remote area nurses often work in isolated and in extreme geographical locations. This also means that a significant proportion work alongside and with Aboriginal Australians. In addition, remote area nurses are often left to support people in the communities they work in under extreme and often under-resourced conditions. METHODS: A literature review was undertaken on this subject and a four-section questionnaire was then developed based on the literature. This included demographic questions and two sections using an ordinal Likert scale. The Likert scale questions asked remote area nurses about the skills they felt they used to deal with particular situations and the capacity of the health service to deal with the situations. The fourth section comprised open-ended questions. Thematic analysis was undertaken on the open-ended questions. Categories and themes were developed, and the results discussed. The four-part questionnaire was designed to be anonymous, and it formed part of the questionnaire distributed to students enrolled with the School of Indigenous and Remote Health Alice Springs, Flinders University by email, and to not-for-profit membership organisation CRANAplus for distribution through their networks. RESULTS: Remote area nurses felt that the barriers to supporting an Aboriginal Australian with a terminal diagnosis passing away on their traditional lands were a lack of support around the delivery of culturally appropriate end-of-life care, lack of a stable workforce, insufficient cultural knowledge and understanding, and a lack of guidance and support from family. They felt the enablers were effective communication with the family and Aboriginal elders providing advice to staff and direction on how they can support the family, willingness of staff to participate in care, and input from Aboriginal health practitioners. CONCLUSION: Remote area nurses perceived they lacked support and knowledge from several different areas, both from within their communities and outside of their communities. Despite the barriers, it was evident that remote area nurses can be very resourceful at enabling the processes of supporting Aboriginal people with a terminal diagnosis passing away on their traditional lands.

Prognostic and Predictive Value of Tumor Budding in Colorectal Cancer.

BACKGROUND: Tumor budding (TB) is an adverse prognostic factor in colorectal cancer (CRC). International consensus on a standardized assessment method has led to its wider reporting. However, uncertainty regarding its clinical value persists. This study aimed to (1) confirm the prognostic significance of TB, particularly in stage II CRC; (2) to determine optimum thresholds for TB risk grouping; and (3) to determine whether TB influences responsiveness to chemotherapy. METHODS: TB was assessed in CRC sections from 1575 QUASAR trial patients randomized between adjuvant chemotherapy and observation. Optimal risk group cutoffs were determined by maximum likelihood methods, with their influence on recurrence and mortality investigated in stratified log-rank analyses on exploratory (n = 504), hypothesis-testing (n = 478), and final (n = 593) data sets. RESULTS: The optimal threshold for high-grade TB (HGTB) was ≥ 10 buds per 1.23 mm2. High-grade TB tumors had significantly worse outcomes than those with lower TB: 10-year recurrence 36% versus 22% (risk ratio, 2.00 [95% CI, 1.62-2.45]; 2P < .0001) and 10-year mortality 50% vs. 37% (risk ratio, 1.53 [95% CI, 1.34-1.76]; 2P < .0001). The prognostic significance remained equally strong after allowance for other pathological risk factors, including stage, grade, lymphovascular invasion, and mismatch repair status. There was a nonsignificant trend toward increasing chemotherapy efficacy with increasing bud counts. CONCLUSIONS: TB is a strong independent predictor of recurrence. Chemotherapy efficacy is comparable in patients with higher and lower TB; hence, absolute reductions in recurrence and death with chemotherapy should be about twice as large in patients with ≥ 10 than < 10 TB counts.

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.