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Background: Colorectal cancer (CRC) is one of the most prevalent malignant diseases worldwide. Risk prediction for tumor recurrence is important for making effective treatment decisions and for the survival outcomes of patients with CRC after surgery. Herein, we aimed to explore a prediction algorithm and the risk factors for postoperative tumor recurrence using a machine learning (ML) approach with standardized pathology reports for patients with stage II and III CRC. Methods: Pertinent clinicopathological features were compiled from medical records and standardized pathology reports of patients with stage II and III CRC. Four ML models based on logistic regression (LR), random forest (RF), classification and regression decision trees (CARTs), and support vector machine (SVM) were applied for the development of the prediction algorithm. The area under the curve (AUC) of the ML models was determined in order to compare the prediction accuracy. Genomic studies were performed using a panel-targeted next-generation sequencing approach. Results: A total of 1073 patients who received curative intent surgery at the National Cheng Kung University Hospital between January 2004 and January 2019 were included. Based on conventional statistical methods, chemotherapy (p = 0.003), endophytic tumor configuration (p = 0.008), TNM stage III disease (p < 0.001), pT4 (p < 0.001), pN2 (p < 0.001), increased numbers of lymph node metastases (p < 0.001), higher lymph node ratios (LNR) (p < 0.001), lymphovascular invasion (p < 0.001), perineural invasion (p < 0.001), tumor budding (p = 0.004), and neoadjuvant chemoradiotherapy (p = 0.025) were found to be correlated with the tumor recurrence of patients with stage II–III CRC. While comparing the performance of different ML models for predicting cancer recurrence, the AUCs for LR, RF, CART, and SVM were found to be 0.678, 0.639, 0.593, and 0.581, respectively. The LR model had a better accuracy value of 0.87 and a specificity value of 1 in the testing set. Two prognostic factors, age and LNR, were selected by multivariable analysis and the four ML models. In terms of age, older patients received fewer cycles of chemotherapy and radiotherapy (p < 0.001). Right-sided colon tumors (p = 0.002), larger tumor sizes (p = 0.008) and tumor volumes (p = 0.049), TNM stage II disease (p < 0.001), and advanced pT3–4 stage diseases (p = 0.04) were found to be correlated with the older age of patients. However, pN2 diseases (p = 0.005), lymph node metastasis number (p = 0.001), LNR (p = 0.004), perineural invasion (p = 0.018), and overall survival rate (p < 0.001) were found to be decreased in older patients. Furthermore, PIK3CA and DNMT3A mutations (p = 0.032 and 0.039, respectively) were more frequently found in older patients with stage II–III CRC compared to their younger counterparts. Conclusions: This study demonstrated that ML models have a comparable predictive power for determining cancer recurrence in patients with stage II–III CRC after surgery. Advanced age and high LNR were significant risk factors for cancer recurrence, as determined by ML algorithms and multivariable analyses. Distinctive genomic profiles may contribute to discrete clinical behaviors and survival outcomes between patients of different age groups. Studies incorporating complete molecular and genomic profiles in cancer prediction models are beneficial for patients with stage II–III CRC.

The short isoform of human TIAM2 has been shown to promote proliferation and invasion in various cancer cells. However, the roles of TIAM2S in immune cells in relation to tumor development have not been investigated. To characterize the effects of TIAM2S, we generated TIAM2S-overexpressing mouse lines and found that aged TIAM2S-transgenic (TIAM2S-TG) developed significantly higher occurrence of lymphocytic infiltration and tumorigenesis in various organs, including colon. In addition, TIAM2S-TG is more sensitized to AOM-induced colon tumor development, suggesting a priming effect toward tumorigenesis. In the light of our recent findings that TIAM2S functions as a novel regulator of cellular serotonin level, we found that serotonin, in addition to Cox2, is a unique inflammation marker presented in the colonic lesion sites in the aged TG animals. Furthermore, our results demonstrated that ectopic TIAM2S altered immunity via the expansion of T lymphocytes; this was especially pronounced in CD8+ T cells in combination with CXCL13/BCA-1 pro-inflammatory chemokine in the serum of TIAM2S-TG mice. Consequently, T lymphocytes and B cells were recruited to the lesion sites and stimulated IL-23/IL17A expression to form the tertiary lymphoid organs. Collectively, our research suggests that TIAM2S provokes a pro-inflammatory immune microenvironment permissive to colorectal tumorigenesis through the serotonin-induced immunomodulatory effects.

Background: Cancer subtype switching, which involves unclear cancer cell origin, cell fate decision, and transdifferentiation of cells within a confined tumor microenvironment, remains a major problem in pancreatic cancer (PDA). Results: By analyzing PDA subtypes in The Cancer Genome Atlas, we identified that epigenetic silencing of apoptosisassociated tyrosine kinase (AATK) inversely was correlated with mRNA expression and was enriched in the quasimesenchymal cancer subtype. By comparing early mouse pancreatic lesions, the non-invasive regions showed AATK coexpression in cells with acinar-to-ductal metaplasia, nuclear VAV1 localization, and cell cycle suppression; but the invasive lesions conversely revealed diminished AATK expression in those with poorly differentiated histology, cytosolic VAV1 localization, and co-expression of p63 and HNF1α. Transiently activated AATK initiates acinar differentiation into a ductal cell fate to establish apical-basal polarization in acinar-to-ductal metaplasia. Silenced AATK and ectopically expressed p63 and HNF1α allow the proliferation of ductal PanINs in mice. Conclusion: Epigenetic silencing of AATK regulates the cellular transdifferentiation, proliferation, and cell cycle progression in converting PDA-subtypes.