The presence of poorly differentiated clusters predicts survival in stage II colorectal cancer

The prognostic significance of growth pattern, tumor budding, poorly differentiated clusters, desmoplastic reaction pattern and tumor-stroma ratio in colorectal cancer and an evaluation of their relationship with KRAS, NRAS, BRAF mutations

Growth pattern (GP), tumor budding (TB), poorly differentiated clusters (PDC), desmoplastic reaction pattern (DRP) and tumor-stroma ratio (TSR) are prognostic histomorphological parameters in colorectal cancer (CRC). Correlations between these parameters, their individual prognostic values, and their relationship with KRAS/NRAS/BRAF mutations have not been comprehensively examined. We aimed to investigate these associations, which have not been previously explored in this combination. 126 CRC cases were included. GP, TB, PDC, DRP and TSR were evaluated by two experienced pathologists. KRAS/NRAS/BRAF mutation profile were determined using qPCR. Demographic, clinicopathological and survival data were recorded. Interrelations were investigated by statistical analysis. Infiltrative GP was more frequent in high-score TB, PDC-G3, and stroma-high tumors (p < 0.05). High-score TB was more common in PDC-G3 and stroma-high tumors (p < 0.05). Immature DRP was more frequent in stroma-high tumors (p = 0.014). Among histomorphological parameters, a significant relationship was found only between infiltrative GP and the presence of KRAS mutation (p = 0.023). Moreover, GP was significantly associated with pT, lymphatic invasion, perineural invasion (p < 0.05). Effects on survival were assessed using Kaplan-Meier method and Cox proportional hazards model. TB and PDC were identified as independent predictors of overall survival. Higher TB score (p = 0.008) and higher PDC grade (p = 0.013) lead to worse survival. Interestingly, GP, DRP, TSR or KRAS/NRAS/BRAF mutations were not associated with overall survival. Our results highlight the prognostic significance of TB and PDC. We suggest incorporating TB and PDC into routine CRC reports. The association of KRAS mutation with infiltrative GP supports its role in the acquisition of invasive behavior.

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.

A deep learning-based framework for predicting survival-associated groups in colon cancer by integrating multi-omics and clinical data

Precise prognostic classification of patients and identifying survival subgroups and their associated genes can be important clinical references when designing treatment strategies for cancer patients. Multi-omics and data integration techniques are powerful tools to achieve this goal. This study aimed to introduce a machine learning method to integrate three types of biological data, and investigate the performance of two other methods, in identifying the survival dependency of patients. The data included TCGA RNA-seq gene expression, DNA methylation, and clinical data from 368 patients with colon cancer also we use an independent external validation data set, containing 232 samples. Three methods including, hyper-parameter optimized autoencoders (HPOAE), normal autoencoder, and penalized principal component analysis (PPCA) were used for simultaneous data integration and estimation under a COX hazards model. The HPOAE was thought to outperform other methods. The HPOAE had the Log Rank Mantel-Cox value of 14.27 ± 2, and a Breslow-Generalized Wilcoxon value of 13.13 ± 1. Ten miRNA, 11 methylated genes, and 28 mRNA all by (importance of marginal cutoff > 0.95) were identified. The study demonstrated that hsa-miR-485-5p targets both ZMYM1 and tp53, the latter of which has been previously associated with cancer in numerous studies. Furthermore, compared to other methods, the HPOAE exhibited a greater capacity for identifying survival subgroups and the genes associated with them in patients with colon cancer. However, all of the results were obtained by computational methods, and clinical and experimental studies are needed to validate these results.

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.

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.