Poorly differentiated clusters in colorectal cancer: a current review and implications for future practice

Correlations of pathomorphological parameters between lesions at the invasive front and lymph node metastases in colorectal cancer: a retrospective clinical study

BACKGROUND

Lymph node (LN) metastasis is one of the most important indicators to evaluate stage, choose treatment strategy, and predict outcome of colorectal cancer (CRC). The morphological correlation between primary tumors and LN metastases can help predict the incidence of LN metastasis in CRC more accurately and assist with more individualized risk-stratification management decisions.

METHODS

A retrospective study was devised with paired tissue specimens from the invasive front of primary tumors and LN metastases in 426 patients after a radial surgery for CRC. According to the presence (N +) or absence (N-) of regional LN metastasis and the number of LN metastases (pN1a/1b/1c/2a/2b), comparisons were performed regarding tumor budding (TB) and poorly-differentiated clusters (PDC). In addition, their correlation with the incidence of LN metastasis and the extent were explored.

RESULTS

The TB and PDC in the invasive front of primary tumors presented significant correlations with the incidence of LN metastasis and the number of LN metastases in CRC (P < 0.001). TB2/3 led to a risk of LN metastasis 6.68-fold higher than TB1, while PDC2/3 resulted in a risk of LN metastasis 8.46-fold higher than PDC1. Additionally, the risk of developing 4 or more LN metastases was 3.08-fold and 2.86-fold higher upon TB2/3 and PDC2/3 than that with TB1 and PDC1, respectively. Moderate positive correlations were found between the invasive front of primary tumors and LN metastases in terms of TB and PDC, respectively.

CONCLUSIONS

TB and PDC, at the invasive tumor front are important morphological markers to evaluate LN metastasis in CRC, and they can be employed as reference indicators to assess or predict the potential of LN metastasis in CRC in clinical practice.

Keratin 6A Is Expressed at the Invasive Front and Enhances the Progression of Colorectal Cancer

Keratins (KRTs) are intermediate filament proteins in epithelial cells, and they are important for cytoskeletal organization. KRT6A, classified as a type II KRT, is normally expressed in stratified squamous epithelium and squamous cell carcinomas. Little is known about the expression and role of KRT6A in adenocarcinomas. We investigated the clinicopathologic and molecular biological significance of KRT6A in colorectal adenocarcinoma. Immunostaining of colorectal adenocarcinoma cases treated at our institution demonstrated that KRT6A showed significantly stronger expression at the invasive front than that at the tumor center (P < .0001). The high KRT6A-expression cases (n = 47) tended to have a high budding grade associated with significantly worse prognoses. A multivariate analysis revealed that the KRT6A expression status was an independent prognostic factor for overall survival (P = .0004), disease-specific survival (P = .0097), and progression-free survival (P = .0033). The correlation between KRT6A and patient prognoses was also validated in an external cohort from a published data set. To determine the function of KRT6A in vitro, KRT6A was overexpressed in 3 colon cancer cell lines: DLD-1, SW620, and HCT 116. KRT6A overexpression increased migration and invasion in DLD-1 but did not in SW620 and HCT116. In 3-dimensional sphere-forming culture, KRT6A expression enhanced the irregular protrusion around the spheroid in DLD-1. Our findings in this study indicated that KRT6A expression is a valuable prognostic marker of colorectal cancer and KRT6A may be involved the molecular mechanism in the progression of invasive areas of colorectal cancer.

Improved Risk-Stratification Scheme for Mismatch-Repair Proficient Stage II Colorectal Cancers Using the Digital Pathology Biomarker QuantCRC

PURPOSE

There is a need to improve current risk stratification of stage II colorectal cancer to better inform risk of recurrence and guide adjuvant chemotherapy. We sought to examine whether integration of QuantCRC, a digital pathology biomarker utilizing hematoxylin and eosin-stained slides, provides improved risk stratification over current American Society of Clinical Oncology (ASCO) guidelines.

EXPERIMENTAL DESIGN

ASCO and QuantCRC-integrated schemes were applied to a cohort of 398 mismatch-repair proficient (MMRP) stage II colorectal cancers from three large academic medical centers. The ASCO stage II scheme was taken from recent guidelines. The QuantCRC-integrated scheme utilized pT3 versus pT4 and a QuantCRC-derived risk classification. Evaluation of recurrence-free survival (RFS) according to these risk schemes was compared using the log-rank test and HR.

RESULTS

Integration of QuantCRC provides improved risk stratification compared with the ASCO scheme for stage II MMRP colorectal cancers. The QuantCRC-integrated scheme placed more stage II tumors in the low-risk group compared with the ASCO scheme (62.5% vs. 42.2%) without compromising excellent 3-year RFS. The QuantCRC-integrated scheme provided larger HR for both intermediate-risk (2.27; 95% CI, 1.32-3.91; P = 0.003) and high-risk (3.27; 95% CI, 1.42-7.55; P = 0.006) groups compared with ASCO intermediate-risk (1.58; 95% CI, 0.87-2.87; P = 0.1) and high-risk (2.24; 95% CI, 1.09-4.62; P = 0.03) groups. The QuantCRC-integrated risk groups remained prognostic in the subgroup of patients that did not receive any adjuvant chemotherapy.

CONCLUSIONS

Incorporation of QuantCRC into risk stratification provides a powerful predictor of RFS that has potential to guide subsequent treatment and surveillance for stage II MMRP colorectal cancers.

Risk assessment in pT1 colorectal cancer

Colorectal cancer (CRC) is a common malignancy worldwide and tumour stage is closely related to clinical outcome. A small but significant proportion of submucosal-invasive (ie, pT1) CRC are associated with regional lymph node metastases (LNM) and a worse prognosis. The likelihood of LNM in pT1 CRC needs to be balanced against the operative risk and costs of surgical resection when determining the best patient management. A wide range of histopathological and clinical factors may affect LNM risk in this setting. This script provides a comprehensive overview of the tumour and patient-associated features that have been linked to LNM risk in pT1 CRC. Some of the features are well established within the literature and are included in published guidelines, while others are novel and emerging in nature. Odds ratios for LNM that are associated with key predictive features are provided where appropriate, and published models developed as an aid to the calculation of LNM risk are discussed.

Are the width, length, depth, and area of submucosal invasion predictive of lymph node metastasis in pT1 colorectal cancer?

PURPOSE

Submucosa-limited (pathological T1, pT1) colorectal cancers (CRCs) pose a continuing challenge in the choice of treatment options, which range from local excision to radical surgery. The aim of this study was to evaluate the morphometric and morphologic risk factors associated with regional lymph node metastasis (LNM) in pT1 CRC.

METHODS

We performed a histological review of patients who underwent oncological resection between 2016 and 2022. Tumor grade, budding, poorly differentiated clusters (PDCs), cancer gland rupture, lymphovascular invasion (LVI), and presence of deep submucosal invasion (DSI), as well as width, length, total area, and area of DSI, were evaluated as potential risk factors for LNM.

RESULTS

A total of 264 cases of colon and rectal carcinomas with invasion into the submucosal layer (pT1) were identified. LNM was found in 46 of the 264 cases (17.4%). All morphometric parameters, as well as DSI (P=0.330), showed no significant association with LNM. High grade adenocarcinoma (P=0.050), budding (P=0.056), and PDCs (P<0.001) were associated with LNM. In the multivariate analysis, LVI presence remained the only significant independent risk factor (odds ratio, 15.7; 95% confidence interval, 8.5-94.9; P<0.001).

CONCLUSION

The DSI of T1 CRC, as well as other morphometric parameters of submucosal tumor spread, held no predictive value in terms of LNM. LVI was the only independent risk factor of LNM.

Deep Learning and Colon Cancer Interpretation: Rise of the Machine

The rapidly evolving development of artificial intelligence (AI) has spurred the development of numerous algorithms that augment information obtained from routine pathologic review of hematoxylin and eosin-stained slides. AI tools that predict prognosis and underlying molecular alterations have been the focus of much of the research to date. The results of these studies highlight the tremendous potential of AI to enhance our pathology reports by providing rapid predictions of key features that influence therapy and outcomes.

Lymph Node Molecular Analysis with OSNA Enables the Identification of pT1 CRC Patients at Risk of Recurrence: A Multicentre Study

Early-stage colorectal carcinoma (CRC)-pT1-is a therapeutic challenge and presents some histological features related to lymph node metastasis (LNM). A significant proportion of pT1 CRCs are treated surgically, resulting in a non-negligible surgical-associated mortality rate of 1.5-2%. Among these cases, approximately 6-16% exhibit LNM, but the impact on survival is unclear. Therefore, there is an unmet need to establish an objective and reliable lymph node (LN) staging method to optimise the therapeutic management of pT1 CRC patients and to avoid overtreating or undertreating them. In this multicentre study, 89 patients with pT1 CRC were included. All histological features associated with LNM were evaluated. LNs were assessed using two methods, One-Step Nucleic Acid Amplification (OSNA) and the conventional FFPE plus haematoxylin and eosin (H&E) staining. OSNA is an RT-PCR-based method for amplifying CK19 mRNA. Our aim was to assess the performance of OSNA and H&E in evaluating LNs to identify patients at risk of recurrence and to optimise their clinical management. We observed an 80.9% concordance in LN assessment using the two methods. In 9% of cases, LNs were found to be positive using H&E, and in 24.7% of cases, LNs were found to be positive using OSNA. The OSNA results are provided as the total tumour load (TTL), defined as the total tumour burden present in all the LNs of a surgical specimen. In CRC, a TTL ≥ 6000 CK19 m-RNA copies/µL is associated with poor prognosis. Three patients had TTL > 6000 copies/μL, which was associated with higher tumour budding. The discrepancies observed between the OSNA and H&E results were mostly attributed to tumour allocation bias. We concluded that LN assessment with OSNA enables the identification of pT1 CRC patients at some risk of recurrence and helps to optimise their clinical management.

Buds, clusters, and transitions in 21st century colorectal carcinoma: revolution or reinvention?†

Tumour budding (TB) describes single or small groups of neoplastic cells that lack continuity with an advancing tumour front. Poorly differentiated clusters (PDCs) are larger and qualitatively different. TB grade and PDCs may predict a worse outcome in colorectal carcinoma and other cancers and fall into the category of 'invasive front prognostic markers' that also includes intratumoural stroma type. Epithelial-mesenchymal transition (EMT) allows the adoption by epithelial cells of mesenchymal characteristics such as dyscohesion, migration, and stromal invasion. TB and PDCs harbor alterations in EMT-related proteins and RNAs and may be morphological manifestations of EMT. However, persistence of epithelioid features and absence of a full complement of typical alterations in TB and PDCs may indicate 'partial EMT', i.e. an intermediate/hybrid state. Recently, Pavlič et al asserted that TB and PDCs in colorectal cancer represent different manifestations of partial EMT and, perhaps controversially, that TB is closer than PDCs to complete transition. In clinical practice, low inter-observer agreement for invasive front prognostic markers is a potential problem. The UK colorectal cancer pathology dataset advises assessment of TB and recommends the use of an international consensus system, but time will tell if we are adopting reliable prognostic markers or reinventing the wheel. Additional studies of TB, PDCs, and EMT will presumably allow greater insight into their role in tumour development and progression. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Analysis of prognostic factors in different grades of histologic differentiation in colorectal cancer patients receiving preoperative neoadjuvant chemotherapy and establishment of prognostic nomograms for moderately differentiated grade

OBJECTIVE

The aim of this study is to analyze the differences in independent prognostic factors of cancer-specific survival (CSS) and overall survival (OS) in patients with different grades of histologic differentiation of colorectal cancer (CRC) who received preoperative neoadjuvant chemotherapy (NAC) and to establish a nomogram for predicting postoperative survival based on moderately differentiated CRC.

METHODS

We analyzed CRC patients from the SEER database who received NAC before operation between 2010 and 2015. The Kaplan-Meier curves were drawn to describe the differences in CSS and OS of CRC patients with different histologic grades of differentiation. Cox regression analysis was used to determine the independent prognostic factors. Nomograms were established to predict CSS and OS at 3 and 5 years by integrating independent prognostic factors. The calibration curve, receiver operating characteristic (ROC) curve, and C-index were used to verify nomograms.

RESULTS

A total of 6481 patients with CRC who received preoperative NAC were included in this study. Patients with different grades of histologic differentiation had significant differences in CSS and OS (P < 0.001), and the independent prognostic factors of different grades of histologic differentiation showed heterogeneity. In patients with moderately differentiated grade CRC, the independent prognostic factors for CSS and OS were age, race, marital status, serum carcinoembryonic antigen (CEA) level before treatment, site of primary tumor, histologic type, pT stage, pN stage, liver metastasis, and lung metastasis. Nomograms were established based on the independent prognostic factors of moderately differentiated grade CRC, and its calibration curves, area under the curve (AUC), and C-index showed good prediction accuracy.

CONCLUSIONS

The independent prognostic factors of CSS and OS are different in patients with different grades of histologic differentiation of CRC who received NAC before the operation. Nomograms can be used to predict the survival of patients with moderately differentiated grade CRC who received preoperative NAC and to assist clinicians in making clinical decisions.

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.

Tumor budding in gastric cancer

The tumor, nodes, metastasis (TNM) staging system has long been the gold standard for the classification and prognosis of solid tumors. However, the TNM staging system is not without limitations. Prognostic heterogeneity exists within patients at the same stage. Therefore, the pursuit of other biomarkers with the potential to classify patients with cancer has never stopped. One of them, tumor budding (TB), has gained much success in colorectal cancer. In recent years, TB in gastric cancer has attracted much attention from researchers, beginning to reveal the molecular and biological aspects of this phenomenon in gastric cancer, and has emerged as a promising prognostic biomarker in gastric cancer, predicting disease progression and unfavorable survival. Therefore, it is time and essential to provide a holistic overview of TB in gastric cancer, which has not been achieved and is the aim of this review.

Evidence for heterogeneity in response to treatment in mammary tumors of dogs as happens in humans

Tumors are formed by various clones developed over a long time. This gives rise to a heterogeneous nature. This heterogeneity is the hardest challenge in the treatment of cancers because it is the main reason for drug resistance. This is a well-known fact in human cancer. Therefore, we have reasoned that if the tumor heterogeneity in canine mammary gland tumors (CMGTs) could be shown by an ex vivo assay, which will be used first time in veterinary oncology practice, this could be used further in clinics. To achieve this, twenty-six patients were included in the study. Tumor tissues were obtained from animals during routine surgery. Tumor cells were isolated and seeded ex vivo. The cells were exposed to anticancer drugs that are clinically used. Seven days after the treatment, chemosensitivity has luminometrically been assayed by ATP-tumor chemosensitivity assay (ATP-TCA). It has clearly been shown that all the tumor tissues have responded to treatment differently, implying that heterogeneity exists in mammary tumors. There has also been found that there was a weak to moderate statistically significant correlation between tumor size and drug index. However, there has been no correlation between drug index and metastasis to lymph nodes. Hyperplasic areas had relatively higher PCNA values. The results of our study demonstrate the heterogeneity in responses to in vitro drugs. Clinical trials based on test results and follow-up studies with large numbers of animals are needed to prove that such chemotherapeutic activity assessment tests can be clinically useful in predicting drug responses in CMGTs.

Technical, functional, and oncological validity of robot-assisted total-intersphincteric resection (T-ISR) for lower rectal cancer

BACKGROUND

Few studies fairly compared anorectal function and prognostic outcomes between patients undergoing abdominoperineal resection (APR) and anorectal-function-saving operations (ASO) under the equivalent conditions. By contrast, surgeons used to be somewhat hesitant to conduct total intersphincteric resection (T-ISR) as maximal ASO, due to its technical complexity and potential anorectal dysfunction.

METHODS

Propensity-score matched cohorts undergoing robot-assisted R0 surgery [T-ISR vs APR vs partial-subtotal ISR (PS-ISR)/lower anterior resection (LAR)] for rectal cancer (n = 1361) were included. Operative outcomes, recurrence, and disease-free/overall survival (DFS/OS) were analyzed. Anorectal function was evaluated based on fecal incontinence score and high-resolution manometry between the T-ISR and other ASO groups.

RESULTS

Few differences were detected between the T-ISR and APR groups. More patients undergoing APR had T4 stage disease, while the lowest tumor margin was the same in both groups (mean, 1.5 cm from anal verge). Prognostic outcomes did not differ between the T-ISR and APR groups, including local (5.1% vs 7.7%, p = 1) or systemic (15.4% vs 25.6%, p = 0.401) recurrence, and 5-year DFS (78.7% vs 61.5%, p = 0.1) and OS (89% vs 82.1%, p = 0.434) rates, nor were there differences between the T-ISR and PS-ISR/LAR groups. The PS-ISR group generally showed less anorectal dysfunction than the T-ISR group, but maximal tolerance volume did not differ between these two groups and was within the range for the healthy population.

CONCLUSIONS

T-ISR can replace most traditional APR, except for advanced T4 disease with aggressive infiltration into the levator-sphincters, and can provide tolerable anorectal dysfunction.

Clinicopathological significance of neutrophil-rich colorectal carcinoma

AIMS

The importance of the interaction between tumour cells and neutrophils has recently begun to emerge. However, the significance of tumour-infiltrating neutrophil (TIN) in colorectal carcinomas (CRCs) remains unclear. The aim of this study was to investigate the prognostic significance of TIN in CRCs.

METHODS

CRCs were evaluated for TIN and were classified as neutrophil-rich (NR), neutrophil-intermediate (NI) and neutrophil-poor (NP) based on the presence of >15, 5-15 and <5 TIN per 100 tumour cells, respectively. Various clinicopathological parameters were recorded in each case including age, gender, histological grade, tumour, node, metastasis (TNM) stage, tumour location and DNA mismatch repair (MMR) status.

RESULTS

Among the 348 CRC cases reviewed, 38 cases were NR, 43 cases were NI and 267 cases were NP. High TIN was associated with higher histological grade (p=0.0222), right-sided tumour location (p=0.0025), advanced TNM stage (p=0.0346) and higher rate of MMR-deficient CRCs (p=0.0027). Patients with NR CRCs had significantly poorer 5-year recurrence-free survival comparing to patients with NI or NP CRCs on Kaplan-Meier analysis (p=0.0001) and high TIN remained an independent risk factor with multivariate analysis (p=0.0137; HR: 1.930, 95% CI: 1.144 to 3.255). NR CRCs are more commonly seen in MMR-deficient than in MMR-proficient CRCs (p=0.0006). Patients with MMR-deficient NR CRCs showed similar 5-year recurrence-free survival compared with MMR-proficient NR CRCs.

CONCLUSIONS

Our findings reveal that high TIN confers poorer patient prognosis in both MMR-proficient and MMR-deficient CRCs.

Prediction model of poorly differentiated colorectal cancer (CRC) based on gut bacteria

BACKGROUND

The mortality of colorectal cancer is high, the malignant degree of poorly differentiated colorectal cancer is high, and the prognosis is poor.

OBJECTIVE

To screen the characteristic intestinal microbiota of poorly differentiated intestinal cancer.

METHODS

Fecal samples were collected from 124 patients with moderately differentiated CRC and 123 patients with poorly differentiated CRC, and the bacterial 16S rRNA V1-V4 region of the fecal samples was sequenced. Alpha diversity analysis was performed on fecal samples to assess the diversity and abundance of flora. The RDP classifier Bayesian algorithm was used to analyze the community structure. Linear discriminant analysis and Student's t test were used to screen the differences in flora. The PICRUSt1 method was used to predict the bacterial function, and six machine learning models, including logistic regression, random forest, neural network, support vector machine, CatBoost and gradient boosting decision tree, were used to construct a prediction model for the poor differentiation of colorectal cancer.

RESULTS

There was no significant difference in fecal flora alpha diversity between moderately and poorly differentiated colorectal cancer (P > 0.05). The bacteria that accounted for a large proportion of patients with poorly differentiated and moderately differentiated colorectal cancer were Blautia, Escherichia-Shigella, Streptococcus, Lactobacillus, and Bacteroides. At the genus level, there were nine bacteria with high abundance in the poorly differentiated group, including Bifidobacterium, norank_f__Oscillospiraceae, Eisenbergiella, etc. There were six bacteria with high abundance in the moderately differentiated group, including Megamonas, Erysipelotrichaceae_UCG-003, Actinomyces, etc. The RF model had the highest prediction accuracy (100.00% correct). The bacteria that had the greatest variable importance in the model were Pseudoramibacter, Megamonas and Bifidobacterium.

CONCLUSION

The degree of pathological differentiation of colorectal cancer was related to gut flora, and poorly differentiated colorectal cancer had some different bacterial flora, and intestinal bacteria can be used as biomarkers for predicting poorly differentiated CRC.

Quantitative Pathologic Analysis of Digitized Images of Colorectal Carcinoma Improves Prediction of Recurrence-Free Survival

BACKGROUND & AIMS

To examine whether quantitative pathologic analysis of digitized hematoxylin and eosin slides of colorectal carcinoma (CRC) correlates with clinicopathologic features, molecular alterations, and prognosis.

METHODS

A quantitative segmentation algorithm (QuantCRC) was applied to 6468 digitized hematoxylin and eosin slides of CRCs. Fifteen parameters were recorded from each image and tested for associations with clinicopathologic features and molecular alterations. A prognostic model was developed to predict recurrence-free survival using data from the internal cohort (n = 1928) and validated on an internal test (n = 483) and external cohort (n = 938).

RESULTS

There were significant differences in QuantCRC according to stage, histologic subtype, grade, venous/lymphatic/perineural invasion, tumor budding, CD8 immunohistochemistry, mismatch repair status, KRAS mutation, BRAF mutation, and CpG methylation. A prognostic model incorporating stage, mismatch repair, and QuantCRC resulted in a Harrell's concordance (c)-index of 0.714 (95% confidence interval [CI], 0.702-0.724) in the internal test and 0.744 (95% CI, 0.741-0.754) in the external cohort. Removing QuantCRC from the model reduced the c-index to 0.679 (95% CI, 0.673-0.694) in the external cohort. Prognostic risk groups were identified, which provided a hazard ratio of 2.24 (95% CI, 1.33-3.87, P = .004) for low vs high-risk stage III CRCs and 2.36 (95% CI, 1.07-5.20, P = .03) for low vs high-risk stage II CRCs, in the external cohort after adjusting for established risk factors. The predicted median 36-month recurrence rate for high-risk stage III CRCs was 32.7% vs 13.4% for low-risk stage III and 15.8% for high-risk stage II vs 5.4% for low-risk stage II CRCs.

CONCLUSIONS

QuantCRC provides a powerful adjunct to routine pathologic reporting of CRC. A prognostic model using QuantCRC improves prediction of recurrence-free survival.

Tumour budding and poorly differentiated clusters in colon cancer - different manifestations of partial epithelial-mesenchymal transition

Morphological features including infiltrative growth, tumour budding (TB), and poorly differentiated clusters (PDCs) have a firmly established negative predictive value in colorectal cancer (CRC). Despite extensive research, the mechanisms underlying different tumour growth patterns remain poorly understood. The aim of this study was to investigate the involvement of epithelial-mesenchymal transition (EMT) in TB and PDCs in CRC. Using laser-capture microdissection, we obtained distinct parts of the primary CRC including TB, PDCs, expansive tumour front, and the central part of the tumour, and analysed the expression of EMT-related markers, i.e. the miR-200 family, ZEB1/2, RND3, and CDH1. In TB, the miR-200 family and CDH1 were significantly downregulated, while ZEB2 was significantly upregulated. In PDCs, miR-141, miR-200c, and CDH1 were significantly downregulated. No significant differences were observed in the expression of any EMT-related markers between the expansive tumour front and the central part of the tumour. Our results suggest that both TB and PDCs are related to partial EMT. Discrete differences in morphology and expression of EMT-related markers between TB and PDCs indicate that they represent different manifestations of partial EMT. TB seems to be closer to complete EMT than PDCs. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

A Novel Combined Tumor Budding-Poorly Differentiated Clusters Grading System Predicts Recurrence and Survival in Stage I-III Colorectal Cancer

Tumor budding (TB) and poorly differentiated clusters (PDCs) are powerful prognostic factors in colorectal cancer (CRC). Despite their morphologic and biological overlap, TB and PDC are assessed separately and are distinguished by an arbitrary cutoff for cell cluster size. This cutoff can be challenging to apply in practice and its biological significance remains unclear. We developed a novel scoring system that incorporates TB and PDC into a single parameter ("Combined Score"; CS), eliminating the need for such cutoffs and allowing the prognostic value of PDC to be captured alongside TB. In a cohort of 481 stage I-III CRC resections, CS was significantly associated with American Joint Committee on Cancer (AJCC) stage, T-stage, N-stage, histologic grade, tumor deposits, lymphovascular invasion, and perineural invasion (P<0.0001). In addition, CS was significantly associated with decreased 5-year recurrence-free survival, overall survival, and disease-specific survival (P<0.0001). TB and PDC showed similar associations with oncologic outcomes, with hazard ratios consistently lower than for CS. The association between CS and oncologic outcomes remained significant in subgroup analyses stratified by AJCC stage, anatomic location (rectum/colon) and neoadjuvant therapy status. On multivariable analysis, CS retained its significant association with oncologic outcomes (P=0.0002, 0.005, and 0.009) for recurrence-free survival, disease-specific survival, and overall survival, respectively. In conclusion, CS provides powerful risk stratification in CRC which is at least equivalent to that of TB and PDC assessed individually. If validated elsewhere, CS has practical advantages and a biological rationale that may make it an attractive alternative to assessing these features separately.

Association of tumor size in pathological T4 colorectal cancer with desmoplastic reaction and prognosis

Background

Tumor size in pathological T4 (pT4) colorectal cancer (CRC) is associated with oncological prognosis; however, its relation to epithelial‐mesenchymal transition (EMT)‐associated histology is unclear. We aimed to investigate the association of tumor size with oncological prognosis and EMT.

Methods

We performed a retrospective analysis of 95 patients with primary CRC who underwent radical surgery and were consecutively diagnosed with pT4.

Results

Both 3‐y disease‐free survival (DFS) and cancer‐specific survival (CSS) were significantly higher in patients with tumor size ≥50 mm than in those with tumor size <50 mm (P = .009 and P = .011, respectively). The independent factors identified in the multivariate analysis for DFS were pathological lymph node metastasis (hazard ratio [HR], 2.551; 95% confidence interval [CI], 1.031–6.315; P = .043), distant metastasis (HR, 2.511; 95% CI, 1.140–5.532; P = .022), tumor size (HR, 0.462; 95% CI, 0.234–0.913; P = .026), and adjuvant chemotherapy (HR, 0.357; 95% CI, 0.166–0.766; P = .008). The independent factors identified in multivariate analysis for CSS were tumor location (HR, 10.867; 95% CI, 2.539–45.518; P = .001) and tumor size (HR, 0.067; 95% CI, 0.014–0.321; P < .001). In pT4 CRC, smaller tumor size was associated with nonmature desmoplastic reaction and EMT‐related histology.

Conclusions

Tumor size ≥50 mm was associated with a better DFS and CSS than that of <50 mm, in patients with pT4 CRC. Smaller tumor size with advanced invasion likely reflects a more biologically aggressive phenotype in pT4 CRC.

The Importance of Being “That” Colorectal pT1: A Combined Clinico-Pathological Predictive Score to Improve Nodal Risk Stratification

The management of endoscopically resected pT1 colorectal cancer (CRC) relies on nodal metastasis risk estimation based on the assessment of specific histopathological features. Avoiding the overtreatment of metastasis-free patients represents a crucial unmet clinical need. By analyzing a consecutive series of 207 pT1 CRCs treated with colectomy and lymphadenectomy, this study aimed to develop a novel clinicopathological score to improve pT1 CRC metastasis prediction. First, we established the clinicopathological profile of metastatic cases: lymphovascular invasion (OR: 23.8; CI: 5.12–110.9) and high-grade tumor budding (OR: 5.21; CI: 1.60–16.8) correlated with an increased risk of nodal metastasis, while age at diagnosis >65 years (OR: 0.26; CI: 0.09–0.71) and high tumor-infiltrating lymphocytes (OR: 0.19; CI: 0.06–0.59) showed a protective effect. Combining these features, we built a five-tier risk score that, applied to our series, identified cases with a higher risk (score ≥ 2) of nodal metastasis (OR: 7.7; CI: 2.4–24.4). Notably, a score of 0 was only assigned to cases with no metastases (13/13 cases) and all the score 4 samples (2/2 cases) showed nodal metastases. In conclusion, we developed an effectively combined score to assess pT1 CRC nodal metastasis risk. We believe that its adoption within a multidisciplinary pT1 unit could improve patients' clinical management and limit surgical overtreatment.

A Mass Spectrometry Imaging Based Approach for Prognosis Prediction in UICC Stage I/II Colon Cancer

Simple Summary

Tumor treatment is heavily dictated by the tumor progression status. However, in colon cancer, it is difficult to predict disease progression in the early stages. In this study, we have employed a proteomic analysis using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). MALDI-MSI is a technique that measures the molecular content of (tumor) tissue. We analyzed tumor samples of 276 patients. If the patients developed distant metastasis, they were considered to have a more aggressive tumor type than the patients that did not. In this comparative study, we have developed bioinformatics methods that can predict the tendency of tumor progression and advance a couple of molecules that could be used as prognostic markers of colon cancer. The prediction of tumor progression can help to choose a more adequate treatment for each individual patient.

Abstract

Currently, pathological evaluation of stage I/II colon cancer, following the Union Internationale Contre Le Cancer (UICC) guidelines, is insufficient to identify patients that would benefit from adjuvant treatment. In our study, we analyzed tissue samples from 276 patients with colon cancer utilizing mass spectrometry imaging. Two distinct approaches are herein presented for data processing and analysis. In one approach, four different machine learning algorithms were applied to predict the tendency to develop metastasis, which yielded accuracies over 90% for three of the models. In the other approach, 1007 m/z features were evaluated with regards to their prognostic capabilities, yielding two m/z features as promising prognostic markers. One feature was identified as a fragment from collagen (collagen 3A1), hinting that a higher collagen content within the tumor is associated with poorer outcomes. Identification of proteins that reflect changes in the tumor and its microenvironment could give a very much-needed prediction of a patient’s prognosis, and subsequently assist in the choice of a more adequate treatment.

Stroma AReactive Invasion Front Areas (SARIFA)-A New Easily to Determine Biomarker in Colon Cancer-Results of a Retrospective Study

Simple Summary

Many studies have used histomorphological features to more precisely predict the prognosis of patients with colon cancer, focusing on tumor budding, poorly differentiated clusters, and the tumor–stroma ratio. Here, we introduce SARIFA: Stroma AReactive Invasion Front Area(s). We defined SARIFA as the direct contact between a tumor gland/tumor cell cluster (≥5 cells) and inconspicuous surrounding adipose tissue in the invasion front. SARIFA shows an excellent interobserver reliability and high prognostic value and is thus a promising histomorphological prognostic indicator for adipose-infiltrative adenocarcinomas of the colon.

Abstract

Many studies have used histomorphological features to more precisely predict the prognosis of patients with colon cancer, focusing on tumor budding, poorly differentiated clusters, and the tumor–stroma ratio. Here, we introduce SARIFA: Stroma AReactive Invasion Front Area(s). We defined SARIFA as the direct contact between a tumor gland/tumor cell cluster (≥5 cells) and inconspicuous surrounding adipose tissue in the invasion front. In this retrospective, single-center study, we classified 449 adipose-infiltrative adenocarcinomas (not otherwise specified) from two groups based on SARIFA and found 25% of all tumors to be SARIFA-positive. Kappa values between the two pathologists were good/very good: 0.77 and 0.87. Patients with SARIFA-positive tumors had a significantly shorter colon-cancer-specific survival (p = 0.008, group A), absence of metastasis, and overall survival (p < 0.001, p = 0.003, group B). SARIFA was significantly associated with adverse features such as pT4 stage, lymph node metastasis, tumor budding, and higher tumor grade. Moreover, SARIFA was confirmed as an independent prognostic indicator for colon-cancer-specific survival (p = 0.011, group A). SARIFA assessment was very quick (<1 min). Because of low interobserver variability and good prognostic significance, SARIFA seems to be a promising histomorphological prognostic indicator in adipose-infiltrative adenocarcinomas of the colon. Further studies should validate our results and also determine whether SARIFA is a universal prognostic indicator in solid cancers.

A Case of Colonic Micropapillary Carcinoma with a High Frequency of Apoptosis

Colorectal micropapillary carcinoma (MPC) exhibits aggressive biological characteristics, with empty spaces and reversed polarity, similar to the poorly differentiated clusters (PDCs) formed from detached cancer cells. Epithelial-mesenchymal transition, which is involved in the cancer cell acquisition of apoptosis resistance, is closely linked with histological findings of MPC, PDCs, and tumor buds (TBs), with MPC and TBs considered as apoptosis-resistant features. However, we encountered a case of colonic MPC with frequent apoptosis. We examined the case using immunohistochemistry. In many of the tumor glands (TGs) of the MPC, empty spaces and tumor cell detachment toward the gland interior were observed. Moreover, TG ruptures were scattered, with PDCs adjacent to them. Apoptosis occurred mainly at the TG and PDC peripheries in the middle and deep tumor layers, and transforming growth factor beta 1 (TGF-β1) positivity was evident in those tumor cells. Cells positive for apoptosis-related M30 were distributed mainly in the deep layer with a significant PDC and TB presence. However, apoptosis and M30 positivity were low in the TBs. Non-tumorous bud components, especially those in the deep layer, had poor ability to promptly acquire apoptosis resistance. No nuclear β-catenin positivity was found in any of the tumor cells. Apoptosis has the potential to reciprocally produce MPC, PDCs, and TBs, with TGF-β1 involvement.

Modified Tumor Budding as a Better Predictor of Lymph Node Metastasis in Early Gastric Cancer: Possible Real-World Applications

Simple Summary

To obtain the optimal treatment effect of endoscopic resection (ER) in early gastric carcinoma (EGC), a well-established indication for post-ER surgery is needed. In addition, accurate prediction of lymph node metastasis (LNM) is necessary to achieve this goal. Here, we present modified tumor budding (mTB), which excludes signet ring cells from conventional tumor budding (TB) as a novel predictor for LNM. Conventional TB and mTB were the most predictive independent factors for LNM. Furthermore, mTB was superior to TB in predicting LNM (p = 0.0004–0.0008). In conclusion, mTB significantly enhanced the predictive power of LNM, which could be a novel indicator for determining post-ER surgery.

Abstract

Endoscopic resection (ER) is a minimally invasive treatment for early gastric cancer (EGC) with a low risk of lymph node metastasis (LNM). Recently, tumor budding (TB) has emerged as a potential predictor of LNM in EGC. We assessed the clinical significance of modified TB (mTB) that excludes the signet ring cell component and compared several TB assessment methods. Two hundred and eighty-nine patients with EGC at Uijeongbu St. Mary’s Hospital from 2010 to 2021 were enrolled. In univariate analysis, age, size, depth of invasion, tumor type, histologic type, Lauren classification, lymphatic invasion, venous invasion, poorly differentiated carcinoma (“not otherwise specified” predominant), and TB were significantly associated with LNM. Multivariate regression analysis showed that mTB (difference area under the curve [dAUC] = 0.085 and 0.087) was superior to TB (dAUC = 0.054 and 0.057) in predicting LNM. In addition, total TB counts on representative slide sections (dAUC = 0.087 and 0.057) in assessing TB and mTB and the ITBCC method (dAUC = 0.085) in mTB were superior to the presence or absence method (dAUC = 0.042 and 0.029). The mTB significantly increases LNM prediction ability, which can provide important information for patients with EGC.

Cytoproliferative activity in colorectal poorly differentiated clusters: Biological significance in tumor setting

BACKGROUND

Poorly differentiated clusters (PDCs) have gained a significant prognostic role in colorectal carcinomas (CRCs) being associated to high risk of lymph node metastasis, shorter survival time and poor prognosis. The knowledge in PDC biology is not completely clear.

MATERIALS AND METHODS

We assessed Ki-67 LI in 45 CRCs showing ≥10 PDCs. We distinguished PDCs at the periphery of the tumor masses (pPDCs) from those within the tumor masses (cPDCs). We chose 3 cut-offs of Ki-67 labeling index (Ki-67 LI): <10%, 10-50%, and >50% of the labeled cells.

RESULTS

Ki-67 LI in pPDCs was<10% in 37 cases (82%), 10-50% in 6 cases (13%) and >50%in 2 cases (5%); Ki-67 LI in cPDCs was<10% in 4 cases (23.5%), 10-50% in 4 (23.5%) and >50% in 9 (54%). Ki-67 LI in tumor budding foci (TBs) was <10% in 8 cases (32%), 10-50% in 8 (32%) and >50% in 9 (36%). The difference of Ki-67 LI reaches the statistical significance (p < 0.005). Ki-67 LI <10% in the pPDCs was associated with nodal metastases (pN+) (p < 0.0001), pTNM stage III and IV(p < 0.0001) and TB (p < 0.001). Ki-67 LI > 50% in cPDC was significantly associated withpT3-pT4 and advanced pTNM stages (p < 0.0001), N+ (p = 0.0001) and LVI (p < 0.05).

CONCLUSION

Different Ki-67 LI detected between cPDCs and pPDCs suggesting a biological difference in PDCs. An actively proliferating central tumor areas can be distinguished from the peripheral portion of the tumors in which the cells interact with the stroma acquiring invasive and metastatic potential.

Epithelial-Mesenchymal Transition in Colorectal Carcinoma: Comparison Between Primary Tumor, Lymph Node and Liver Metastases

There is emerging evidence suggesting that epithelial-mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET) play an important role in colorectal carcinoma (CRC), but their exact role remains controversial. Our aim was to analyze the miR-200 family as EMT markers and their target genes expression at invasive tumor front and in nodal and liver metastases. Sixty-three formalin-fixed paraffin-embedded tissue samples from 19 patients with CRC were included. Using a micropuncture technique, tissue was obtained from central part and invasive front of the primary tumor, and nodal and liver metastases. Expression of the miR-200 family and their target genes CDKN1B, ONECUT2, PTPN13, RND3, SOX2, TGFB2 and ZEB2 was analyzed using real-time PCR. We found miR-200 family down-regulation at invasive front compared to central part, and up-regulation of miRNA-200a/b/c and miR-429 in metastases compared to invasive front. At invasive front, TGFB2 was the only gene with inverse expression to the miR-200 family, whereas in metastases inverse expression was found for ONECUT2 and SOX2. CDKN1B, PTPN13 and ZEB2 were down-regulated at invasive front and up-regulated in metastases. Our results suggest the involvement of partial EMT at invasive tumor front, and partial MET in metastases in CRC, based on miR-200 family and its target genes expression.

Development and initial validation of a deep learning algorithm to quantify histological features in colorectal carcinoma including tumour budding/poorly differentiated clusters

AIMS

To develop and validate a deep learning algorithm to quantify a broad spectrum of histological features in colorectal carcinoma.

METHODS AND RESULTS

A deep learning algorithm was trained on haematoxylin and eosin-stained slides from tissue microarrays of colorectal carcinomas (N = 230) to segment colorectal carcinoma digitised images into 13 regions and one object. The segmentation algorithm demonstrated moderate to almost perfect agreement with interpretations by gastrointestinal pathologists, and was applied to an independent test cohort of digitised whole slides of colorectal carcinoma (N = 136). The algorithm correctly classified mucinous and high-grade tumours, and identified significant differences between mismatch repair-proficient and mismatch repair-deficient (MMRD) tumours with regard to mucin, inflammatory stroma, and tumour-infiltrating lymphocytes (TILs). A cutoff of >44.4 TILs per mm² carcinoma gave a sensitivity of 88% and a specificity of 73% in classifying MMRD carcinomas. Algorithm measures of tumour budding (TB) and poorly differentiated clusters (PDCs) outperformed TB grade derived from routine sign-out, and compared favourably with manual counts of TB/PDCs with regard to lymphatic, venous and perineural invasion. Comparable associations were seen between algorithm measures of TB/PDCs and manual counts of TB/PDCs for lymph node metastasis (all P < 0.001); however, stronger correlations were seen between the proportion of positive lymph nodes and algorithm measures of TB/PDCs. Stronger associations were also seen between distant metastasis and algorithm measures of TB/PDCs (P = 0.004) than between distant metastasis and TB (P = 0.04) and TB/PDC counts (P = 0.06).

CONCLUSIONS

Our results highlight the potential of deep learning to identify and quantify a broad spectrum of histological features in colorectal carcinoma.

Deep Learning Prediction of Metastasis in Locally Advanced Colon Cancer Using Binary Histologic Tumor Images

In this study, we developed the Binary ImaGe Colon Metastasis classifier (BIg-CoMet), a semi-guided approach for the stratification of colon cancer patients into two risk groups for the occurrence of distant metastasis, using an InceptionResNetV2-based deep learning model trained on binary images. We enrolled 291 colon cancer patients with pT3 and pT4 adenocarcinomas and converted one cytokeratin-stained representative tumor section per case into a binary image. Image augmentation and dropout layers were incorporated to avoid overfitting. In a validation collective (n = 128), BIg-CoMet was able to discriminate well between patients with and without metastasis (AUC: 0.842, 95% CI: 0.774-0.911). Further, the Kaplan-Meier curves of the metastasis-free survival showed a highly significant worse clinical course for the high-risk group (log-rank test: p < 0.001), and we demonstrated superiority over other established risk factors. A multivariable Cox regression analysis adjusted for confounders supported the use of risk groups as a prognostic factor for the occurrence of metastasis (hazard ratio (HR): 5.4, 95% CI: 2.5-11.7, p < 0.001). BIg-CoMet achieved good performance for both UICC subgroups, especially for UICC III (n = 53), with a positive predictive value of 80%. Our study demonstrates the ability to stratify colon cancer patients via a semi-guided process on images that primarily reflect tumor architecture.

Improving tumor budding reporting in colorectal cancer: a Delphi consensus study

Tumor budding is a long-established independent adverse prognostic marker in colorectal cancer, yet methods for its assessment have varied widely. In an effort to standardize its reporting, a group of experts met in Bern, Switzerland, in 2016 to reach consensus on a single, international, evidence-based method for tumor budding assessment and reporting (International Tumor Budding Consensus Conference [ITBCC]). Tumor budding assessment using the ITBCC criteria has been validated in large cohorts of cancer patients and incorporated into several international colorectal cancer pathology and clinical guidelines. With the wider reporting of tumor budding, new issues have emerged that require further clarification. To better inform researchers and health-care professionals on these issues, an international group of experts in gastrointestinal pathology participated in a modified Delphi process to generate consensus and highlight areas requiring further research. This effort serves to re-affirm the importance of tumor budding in colorectal cancer and support its continued use in routine clinical practice.

Clinical Significance of Preoperative Inflammatory Markers in Prediction of Prognosis in Node-Negative Colon Cancer: Correlation between Neutrophil-to-Lymphocyte Ratio and Poorly Differentiated Clusters

Although stage I and II colon cancers (CC) generally show a very good prognosis, a small proportion of these patients dies from recurrent disease. The identification of high-risk patients, who may benefit from adjuvant chemotherapy, becomes therefore essential. We retrospectively evaluated 107 cases of stage I (n = 28, 26.2%) and II (n = 79, 73.8%) CC for correlations among preoperative inflammatory markers, histopathological factors and long-term prognosis. A neutrophil-to-lymphocyte ratio greater than 3 (H-NLR) and a platelet-to-lymphocyte ratio greater than 150 (H-PLR) were significantly associated with the presence of poorly differentiated clusters (PDC) (p = 0.007 and p = 0.039, respectively). In addition, H-NLR and PDC proved to be significant and independent survival prognosticators for overall survival (OS; p = 0.007 and p < 0.001, respectively), while PDC was the only significant prognostic factor for cancer-specific survival (CSS; p < 0.001,). Finally, the combination of H-NLR and PDC allowed an optimal stratification of OS and CSS in our cohort, suggesting a potential role in clinical practice for the identification of high-risk patients with stage I and II CC.