A predictive model for recurrence after upfront surgery in patients with resectable pancreatic ductal adenocarcinoma (PDAC) by using preoperative clinical data and CT characteristics

The prognosis-based classification model in resectable pancreatic cancer

AIM

Pancreatic ductal adenocarcinoma treatment is mainly based on the anatomical resectability classification. However, prognosis-based classification may be more reasonable. In this study, we stratified resectable pancreatic ductal adenocarcinoma according to preoperative factors and reconsidered treatment strategies.

METHODS

We retrospectively evaluated 131 patients who underwent upfront surgery for resectable pancreatic ductal adenocarcinoma between 2007 and 2019. Recurrence within 1 year after surgery was defined as early recurrence, and the risk factors for early recurrence were identified using preoperative factors. Subsequently, we calculated the scores and stratified the participant groups.

RESULTS

Fifty-five (42 %) patients who relapsed within 1 year showed significantly poorer survival than those without recurrence (median overall survival, 14.0 vs. 80.6 months; p < 0.01). Multivariate analysis revealed that a tumor diameter of ≥24 mm (p < 0.01) and preoperative serum carbohydrate antigen 19-9 level of ≥380 U/mL (p = 0.04) were the independent risk factors for early recurrence. Early recurrence score was created using these factors, stratifying the participant group into three groups of 0-2 points, and the prognosis was significantly different (median overall survival, 49.3 vs. 31.2 vs. 16.0 months; p < 0.01).

CONCLUSION

We stratified the upfront surgical cases of resectable pancreatic ductal adenocarcinoma. The group with a score of 0 had a good prognosis, and upfront surgery was possibly not futile on patients in poor general condition. The group with a score of 2 had a poor prognosis and may require stronger preoperative treatment.

Iron-Dependent Cell Death: A New Treatment Approach against Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a devastating tumor type where a very high proportion of people diagnosed end up dying from cancer. Surgical resection is an option for only about 20% of patients, where the 5-year survival increase ranges from 10 to 25%. In addition to surgical resection, there are adjuvant chemotherapy schemes, such as FOLFIRINOX (a mix of Irinotecan, oxaliplatin, 5-Fluorouraci and leucovorin) or gemcitabine-based treatment. These last two drugs have been compared in the NAPOLI-3 clinical trial, and the NALIRIFOX arm was found to have a higher overall survival (OS) (11.1 months vs. 9.2 months). Despite these exciting improvements, PDAC still has no effective treatment. An interesting approach would be to drive ferroptosis in PDAC cells. A non-apoptotic reactive oxygen species (ROS)-dependent cell death, ferroptosis was first described by Dixon et al. in 2012. ROS are constantly produced in the tumor cell due to high cell metabolism, which is even higher when exposed to chemotherapy. Tumor cells have detoxifying mechanisms, such as Mn-SOD or the GSH-GPX system. However, when a threshold of ROS is exceeded in the tumor cell, the cell's antioxidant systems are overwhelmed, resulting in lipid peroxidation and, ultimately, ferroptosis. In this review, we point out ferroptosis as an approach to consider in PDAC and propose that altering the cellular ROS balance by combining oxidizing agents or with inhibitors of the main cellular detoxifiers triggers ferroptosis in PDAC.

Development and Validation of a Nomogram for Preoperative Prediction of Early Recurrence after Upfront Surgery in Pancreatic Ductal Adenocarcinoma by Integrating Deep Learning and Radiological Variables

Around 80% of pancreatic ductal adenocarcinoma (PDAC) patients experience recurrence after curative resection. We aimed to develop a deep-learning model based on preoperative CT images to predict early recurrence (recurrence within 12 months) in PDAC patients. The retrospective study included 435 patients with PDAC from two independent centers. A modified 3D-ResNet18 network was used for a deep learning model construction. A nomogram was constructed by incorporating deep learning model outputs and independent preoperative radiological predictors. The deep learning model provided the area under the receiver operating curve (AUC) values of 0.836, 0.736, and 0.720 in the development, internal, and external validation datasets for early recurrence prediction, respectively. Multivariate logistic analysis revealed that higher deep learning model outputs (odds ratio [OR]: 1.675; 95% CI: 1.467, 1.950; p < 0.001), cN1/2 stage (OR: 1.964; 95% CI: 1.036, 3.774; p = 0.040), and arterial involvement (OR: 2.207; 95% CI: 1.043, 4.873; p = 0.043) were independent risk factors associated with early recurrence and were used to build an integrated nomogram. The nomogram yielded AUC values of 0.855, 0.752, and 0.741 in the development, internal, and external validation datasets. In conclusion, the proposed nomogram may help predict early recurrence in PDAC patients.

Exosomes: A potential tool for immunotherapy of ovarian cancer

Ovarian cancer is a malignant tumor of the female reproductive system, with a very poor prognosis and high mortality rates. Chemotherapy and radiotherapy are the most common treatments for ovarian cancer, with unsatisfactory results. Exosomes are a subpopulation of extracellular vesicles, which have a diameter of approximately 30-100 nm and are secreted by many different types of cells in various body fluids. Exosomes are highly stable and are effective carriers of immunotherapeutic drugs. Recent studies have shown that exosomes are involved in various cellular responses in the tumor microenvironment, influencing the development and therapeutic efficacy of ovarian cancer, and exhibiting dual roles in inhibiting and promoting tumor development. Exosomes also contain a variety of genes related to ovarian cancer immunotherapy that could be potential biomarkers for ovarian cancer diagnosis and prognosis. Undoubtedly, exosomes have great therapeutic potential in the field of ovarian cancer immunotherapy. However, translation of this idea to the clinic has not occurred. Therefore, it is important to understand how exosomes could be used in ovarian cancer immunotherapy to regulate tumor progression. In this review, we summarize the biomarkers of exosomes in different body fluids related to immunotherapy in ovarian cancer and the potential mechanisms by which exosomes influence immunotherapeutic response. We also discuss the prospects for clinical application of exosome-based immunotherapy in ovarian cancer.