Premalignant pancreatic cells seed stealth metastasis in distant organs in mice

Differentially expressed genes associated with high metabolic tumor volume served as diagnostic markers and potential therapeutic targets for pancreatic cancer

BACKGROUND

The lack of distinct biomarkers for pancreatic cancer is a major cause of early-stage detection difficulty. The pancreatic cancer patient group with high metabolic tumor volume (MTV), one of the values measured from positron emission tomography-a confirmatory method and standard care for pancreatic cancer, showed a poorer prognosis than those with low MTV. Therefore, MTV-associated differentially expressed genes (DEGs) may be candidates for distinctive markers for pancreatic cancer. This study aimed to evaluate the possibility of MTV-related DEGs as markers or therapeutic targets for pancreatic cancer.

METHODS

Tumor tissues and their normal counterparts were obtained from patients undergoing preoperative 18F-FDG PET/CT. The tissues were classified into MTV-low and MTV-high groups (7 for each) based on the MTV2.5 value of 4.5 (MTV-low: MTV2.5 < 4.5, MTV-high: MTV2.5 ≥ 4.5). Gene expression fold change was first calculated in cancer tissue compared to its normal counter and then compared between low and high MTV groups to obtain significant DEGs. To assess the suitability of the DEGs for clinical application, the correlation of the DEGs with tumor grades and clinical outcomes was analyzed in TCGA-PAAD, a large dataset without MTV information.

RESULTS

Total RNA-sequencing (MTV RNA-Seq) revealed that 44 genes were upregulated and 56 were downregulated in the high MTV group. We selected the 29 genes matching MTV RNA-seq patterns in the TCGA-PAAD dataset, a large clinical dataset without MTV information, as MTV-associated genes (MAGs). In the analysis with the TCGA dataset, MAGs were significantly associated with patient survival, treatment outcomes, TCGA-PAAD-suggested markers, and CEACAM family proteins. Some MAGs showed an inverse correlation with miRNAs and were confirmed to be differentially expressed between normal and cancerous pancreatic tissues. Overexpression of KIF11 and RCC1 and underexpression of ADCY1 and SDK1 were detected in ~ 60% of grade 2 pancreatic cancer patients and associated with ~ 60% mortality in stages I and II.

CONCLUSIONS

MAGs may serve as diagnostic markers and miRNA therapeutic targets for pancreatic cancer. Among the MAGs, KIF11, RCC1, ADCY, and SDK1 may be early diagnostic markers.

Liquid biopsy analysis of lipometabolic exosomes in pancreatic cancer

Pancreatic cancer is characterized by its high malignancy, insidious onset and poor prognosis. Most patients with pancreatic cancer are usually diagnosed at advanced stage or with the distant metastasis due to the lack of an effective early screening method. Liquid biopsy technology is promising in studying the occurrence, progression, and early metastasis of pancreatic cancer. In particular, exosomes are pivotal biomarkers in lipid metabolism and liquid biopsy of blood exosomes is valuable for the evaluation of pancreatic cancer. Lipid metabolism is crucial for the formation and activity of exosomes in the extracellular environment. Exosomes and lipids have a complex relationship of mutual influence. Furthermore, spatial metabolomics can quantify the levels and spatial locations of individual metabolites in cancer tissue, cancer stroma, and para-cancerous tissue in pancreatic cancer. However, the relationship among exosomes, lipid metabolism, and pancreatic cancer is also worth considering. This study mainly updates the research progress of metabolomics in pancreatic cancer, their relationship with exosomes, an important part of liquid biopsy, and their lipometabolic roles in pancreatic cancer. We also discuss the mechanisms by which possible metabolites, especially lipid metabolites through exosome transport and other processes, contribute to the recurrence and metastasis of pancreatic cancer.

Phenotyping neuroblastoma cells through intelligent scrutiny of stain-free biomarkers in holographic flow cytometry

To efficiently tackle certain tumor types, finding new biomarkers for rapid and complete phenotyping of cancer cells is highly demanded. This is especially the case for the most common pediatric solid tumor of the sympathetic nervous system, namely, neuroblastoma (NB). Liquid biopsy is in principle a very promising tool for this purpose, but usually enrichment and isolation of circulating tumor cells in such patients remain difficult due to the unavailability of universal NB cell-specific surface markers. Here, we show that rapid screening and phenotyping of NB cells through stain-free biomarkers supported by artificial intelligence is a viable route for liquid biopsy. We demonstrate the concept through a flow cytometry based on label-free holographic quantitative phase-contrast microscopy empowered by machine learning. In detail, we exploit a hierarchical decision scheme where at first level NB cells are classified from monocytes with 97.9% accuracy. Then we demonstrate that different phenotypes are discriminated within NB class. Indeed, for each cell classified as NB its belonging to one of four NB sub-populations (i.e., CHP212, SKNBE2, SHSY5Y, and SKNSH) is evaluated thus achieving accuracy in the range 73.6%-89.1%. The achieved results solve the realistic problem related to the identification circulating tumor cell, i.e., the possibility to recognize and detect tumor cells morphologically similar to blood cells, which is the core issue in liquid biopsy based on stain-free microscopy. The presented approach operates at lab-on-chip scale and emulates real-world scenarios, thus representing a future route for liquid biopsy by exploiting intelligent biomedical imaging.

Liquid biopsies for cancer: From bench to clinic

Over the past two decades, liquid biopsy has been increasingly used as a supplement, or even, a replacement to the traditional biopsy in clinical oncological practice, due to its noninvasive and early detectable properties. The detections can be based on a variety of features extracted from tumor‑derived entities, such as quantitative alterations, genetic changes, and epigenetic aberrations, and so on. So far, the clinical applications of cancer liquid biopsy mainly aimed at two aspects, prediction (early diagnosis, prognosis and recurrent evaluation, therapeutic response monitoring, etc.) and intervention. In spite of the rapid development and great contributions achieved, cancer liquid biopsy is still a field under investigation and deserves more clinical practice. To better open up future work, here we systematically reviewed and compared the latest progress of the most widely recognized circulating components, including circulating tumor cells, cell-free circulating DNA, noncoding RNA, and nucleosomes, from their discovery histories to clinical values. According to the features applied, we particularly divided the contents into two parts, beyond epigenetics and epigenetic-based. The latter was considered as the highlight along with a brief overview of the advances in both experimental and bioinformatic approaches, due to its unique advantages and relatively lack of documentation.

Technologies for Viable Circulating Tumor Cell Isolation

The spread of tumor cells throughout the body by traveling through the bloodstream is a critical step in metastasis, which continues to be the main cause of cancer-related death. The detection and analysis of circulating tumor cells (CTCs) is important for understanding the biology of metastasis and the development of antimetastatic therapy. However, the isolation of CTCs is challenging due to their high heterogeneity and low representation in the bloodstream. Different isolation methods have been suggested, but most of them lead to CTC damage. However, viable CTCs are an effective source for developing preclinical models to perform drug screening and model the metastatic cascade. In this review, we summarize the available literature on methods for isolating viable CTCs based on different properties of cells. Particular attention is paid to the importance of in vitro and in vivo models obtained from CTCs. Finally, we emphasize the current limitations in CTC isolation and suggest potential solutions to overcome them.

Nomogram for Predicting Distant Metastasis of Pancreatic Ductal Adenocarcinoma: A SEER-Based Population Study

(1) Background: The aim of this study was to identify risk factors for distant metastasis of pancreatic ductal adenocarcinoma (PDAC) and develop a valid predictive model to guide clinical practice; (2) Methods: We screened 14328 PDAC patients from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015. Lasso regression analysis combined with logistic regression analysis were used to determine the independent risk factors for PDAC with distant metastasis. A nomogram predicting the risk of distant metastasis in PDAC was constructed. A receiver operating characteristic (ROC) curve and consistency-index (C-index) were used to determine the accuracy and discriminate ability of the nomogram. A calibration curve was used to assess the agreement between the predicted probability of the model and the actual probability. Additionally, decision curve analysis (DCA) and clinical influence curve were employed to assess the clinical utility of the nomogram; (3) Results: Multivariate logistic regression analysis revealed that risk factors for distant metastasis of PDAC included age, primary site, histological grade, and lymph node status. A nomogram was successfully constructed, with an area under the curve (AUC) of 0.871 for ROC and a C-index of 0.871 (95% CI: 0.860-0.882). The calibration curve showed that the predicted probability of the model was in high agreement with the actual predicted probability. The DCA and clinical influence curve showed that the model had great potential clinical utility; (4) Conclusions: The risk model established in this study has a good predictive performance and a promising potential application, which can provide personalized clinical decisions for future clinical work.

Verification of a Novel Minimally Invasive Device for the Isolation of Rare Circulating Tumor Cells (CTC) in Cancer Patients’ Blood

Simple Summary

Detection of circulating tumor cells (CTCs) in blood can be used to diagnose cancer or monitor treatment response for various cancers. However, these cells are rare in the bloodstream in the early stages of cancers, and it, therefore, remains a technical challenge to isolate them. To overcome the limitations of a blood draw, we introduce a minimally invasive device, called the BMProbe™, for the isolation of CTCs directly from the bloodstream. Thereby a large volume of blood is screened. This study first shows how the geometry of the in vivo BMProbe™ causes improved cell deposition conditions. We then performed a verification of the in vivo device using blood samples from lung cancer patients. The results indicate the functionality of the BMProbe™ to isolate CTCs in blood samples. The future step is to use the BMProbe™ in various types of cancer patients to detect CTCs.

Abstract

Circulating tumor cells (CTCs) exist in low quantities in the bloodstream in the early stages of cancers. It, therefore, remains a technical challenge to isolate them in large enough quantities for a precise diagnosis and downstream analysis. We introduce the BMProbe™, a minimally invasive device that isolates CTCs during a 30-minute incubation in the median cubital vein. The optimized geometry of the device creates flow conditions for improved cell deposition. The CTCs are isolated using antibodies that are bound to the surface of the BMProbe™. In this study, flow experiments using cell culture cells were conducted. They indicate a 31 times greater cell binding efficiency of the BMProbe™ compared to a flat geometry. Further, the functionality of isolating CTCs from patient blood was verified in a small ex vivo study that compared the cell count from seven non-small-cell lung carcinoma (NSCLC) patients compared to nine healthy controls with 10 mL blood samples. The median cell count was 1 in NSCLC patients and 0 in healthy controls. In conclusion, the BMProbe™ is a promising method to isolate CTCs in large quantities directly from the venous bloodstream without removing blood from a patient. The future step is to verify the functionality in vivo.

Circulating tumor cells: biology and clinical significance

Circulating tumor cells (CTCs) are tumor cells that have sloughed off the primary tumor and extravasate into and circulate in the blood. Understanding of the metastatic cascade of CTCs has tremendous potential for the identification of targets against cancer metastasis. Detecting these very rare CTCs among the massive blood cells is challenging. However, emerging technologies for CTCs detection have profoundly contributed to deepening investigation into the biology of CTCs and have facilitated their clinical application. Current technologies for the detection of CTCs are summarized herein, together with their advantages and disadvantages. The detection of CTCs is usually dependent on molecular markers, with the epithelial cell adhesion molecule being the most widely used, although molecular markers vary between different types of cancer. Properties associated with epithelial-to-mesenchymal transition and stemness have been identified in CTCs, indicating their increased metastatic capacity. Only a small proportion of CTCs can survive and eventually initiate metastases, suggesting that an interaction and modulation between CTCs and the hostile blood microenvironment is essential for CTC metastasis. Single-cell sequencing of CTCs has been extensively investigated, and has enabled researchers to reveal the genome and transcriptome of CTCs. Herein, we also review the clinical applications of CTCs, especially for monitoring response to cancer treatment and in evaluating prognosis. Hence, CTCs have and will continue to contribute to providing significant insights into metastatic processes and will open new avenues for useful clinical applications.