Derivation of pancreatic acinar cell carcinoma cell line HS-1 as a patient-derived tumor organoid

Comprehensive review of pancreatic acinar cell carcinoma: epidemiology, diagnosis, molecular features and treatment

Pancreatic acinar cell carcinoma is a rare form (0.2-4.3%) of pancreatic neoplasm with unique clinical and molecular characteristics, which largely differ from pancreatic ductal adenocarcinoma. Pancreatic acinar cell carcinoma occurs more frequently in males and can occur in children. Serum lipase is elevated in 24-58% of patients with pancreatic acinar cell carcinoma. Pancreatic acinar cell carcinomas tend to be large at diagnosis (median tumour size: ~5 cm) and are frequently located in the pancreas head. Radiologically, pancreatic acinar cell carcinoma generally exhibits a solid appearance; however, necrosis, cystic changes and intratumoral haemorrhage can occur in larger lesions. Immunostaining is essential for the definitive diagnosis of pancreatic acinar cell carcinoma. Compared with pancreatic ductal adenocarcinoma, pancreatic acinar cell carcinoma has a more favourable prognosis. Although radical surgery is recommended for patients with pancreatic acinar cell carcinoma who do not have distant metastases, the recurrence rate is high. The effectiveness of adjuvant therapy for pancreatic acinar cell carcinoma is unclear. The response to FOLFIRINOX is generally favourable, and some patients achieve a complete response. Pancreatic acinar cell carcinoma has a different genomic profile compared with pancreatic ductal adenocarcinoma. Although genomic analyses have shown that pancreatic acinar cell carcinoma rarely has KRAS, TP53 and CDKN2A mutations, it has a higher prevalence of homologous recombination-related genes, including BRCA1/2 and ATM, than pancreatic ductal adenocarcinoma, suggesting high sensitivity to platinum-containing regimens and PARP inhibitors. Targeted therapies for genomic alternations are beneficial. Therefore, genetic testing is important for patients with pancreatic acinar cell carcinoma to choose the optimal therapeutic strategy.

Tumor microenvironment characterization in colorectal cancer to identify prognostic and immunotherapy genes signature

BACKGROUND

The tumor microenvironment (TME) plays a crucial role in tumorigenesis, progression, and therapeutic response in many cancers. This study aimed to comprehensively investigate the role of TME in colorectal cancer (CRC) by generating a TMEscore based on gene expression.

METHODS

The TME patterns of CRC datasets were investigated, and the TMEscores were calculated. An unsupervised clustering method was used to divide samples into clusters. The associations between TMEscores and clinical features, prognosis, immune score, gene mutations, and immune checkpoint inhibitors were analyzed. A TME signature was constructed using the TMEscore-related genes. The results were validated using external and clinical cohorts.

RESULTS

The TME pattern landscape was for CRC was examined using 960 samples, and then the TMEscore pattern of CRC datasets was evaluated. Two TMEscore clusters were identified, and the high TMEscore cluster was associated with early-stage CRC and better prognosis in patients with CRC when compared with the low TMEscore clusters. The high TMEscore cluster indicated elevated tumor cell scores and tumor gene mutation burden, and decreased tumor purity, when compared with the low TMEscore cluster. Patients with high TMEscore were more likely to respond to immune checkpoint therapy than those with low TMEscore. A TME signature was constructed using the TMEscore-related genes superimposing the results of two machine learning methods (LASSO and XGBoost algorithms), and a TMEscore-related four-gene signature was established, which had a high predictive value for discriminating patients from different TMEscore clusters. The prognostic value of the TMEscore was validated in two independent cohorts, and the expression of TME signature genes was verified in four external cohorts and clinical samples.

CONCLUSION

Our study provides a comprehensive description of TME characteristics in CRC and demonstrates that the TMEscore is a reliable prognostic biomarker and predictive indicator for patients with CRC undergoing immunotherapy.

A quick and reliable image-based AI algorithm for evaluating cellular senescence of gastric organoids

OBJECTIVE

Organoids are a powerful tool with broad application prospects in biomedicine. Notably, they provide alternatives to animal models for testing potential drugs before clinical trials. However, the number of passages for which organoids maintain cellular vitality ex vivo remains unclear.

METHODS

Herein, we constructed 55 gastric organoids from 35 individuals, serially passaged the organoids, and captured microscopic images for phenotypic evaluation. Senescence-associated β-galactosidase (SA-β-Gal), cell diameter in suspension, and gene expression reflecting cell cycle regulation were examined. The YOLOv3 object detection algorithm integrated with a convolutional block attention module (CBAM) was used to evaluate organoid vitality.

RESULTS

SA-β-Gal staining intensity; single-cell diameter; and expression of p15, p16, p21, CCNA2, CCNE2, and LMNB1 reflected the progression of aging in organoids during passaging. The CBAM-YOLOv3 algorithm precisely evaluated aging organoids on the basis of organoid average diameter, organoid number, and number × diameter, and the findings positively correlated with SA-β-Gal staining and single-cell diameter. Organoids derived from normal gastric mucosa had limited passaging ability (passages 1-5), before aging, whereas tumor organoids showed unlimited passaging potential for more than 45 passages (511 days) without showing clear senescence.

CONCLUSIONS

Given the lack of indicators for evaluating organoid growth status, we established a reliable approach for integrated analysis of phenotypic parameters that uses an artificial intelligence algorithm to indicate organoid vitality. This method enables precise evaluation of organoid status in biomedical studies and monitoring of living biobanks.

Patient-derived organoids in translational oncology and drug screening

Patient-derived organoids (PDO) are a new biomedical research model that can reconstruct phenotypic and genetic characteristics of the original tissue and are useful for research on pathogenesis and drug screening. To introduce the progression in this field, we review the key factors of constructing organoids derived from epithelial tissues and cancers, covering culture medium and matrix, morphological characteristics, genetic profiles, high-throughput drug screening, and application potential. We also discuss the co-culture system of cancer organoids with tumor microenvironment (TME) associated cells. The co-culture system is widely used in evaluating crosstalk of cancer cells with TME components, such as fibroblasts, endothelial cells, immune cells, and microorganisms. The article provides a prospective for standardized cultivation mode, automatic morphological evaluation, and drug sensitivity screening using high-throughput methods.