Circulating Tumour Cells as Prognostic Biomarkers in Colorectal Cancer: A Systematic Review

Current Applications and Future Directions of Circulating Tumor Cells in Colorectal Cancer Recurrence

The ability to predict or detect colorectal cancer (CRC) recurrence early after surgery enables physicians to apply appropriate treatment plans and different follow-up strategies to improve patient survival. Overall, 30-50% of CRC patients experience cancer recurrence after radical surgery, but current surveillance tools have limitations in the precise and early detection of cancer recurrence. Circulating tumor cells (CTCs) are cancer cells that detach from the primary tumor and enter the bloodstream. These can provide real-time information on disease status. CTCs might become novel markers for predicting CRC recurrence and, more importantly, for making decisions about additional adjuvant chemotherapy. In this review, the clinical application of CTCs as a therapeutic marker for stage II CRC is described. It then discusses the utility of CTCs for monitoring cancer recurrence in advanced rectal cancer patients who undergo neoadjuvant chemoradiotherapy. Finally, it discusses the roles of CTC subtypes and CTCs combined with clinicopathological factors in establishing a multimarker model for predicting CRC recurrence.

Evaluating the relevance of surgical margins. Part one: The problems with current methodology

The goal of cancer surgery is to achieve a "clean" microscopic resection, with no residual tumour remaining in the wound. To achieve that goal, the surgeon typically incorporates a measured buffer of grossly normal tissue about the entire circumference of the tumour. Microscopic analysis of the resection boundaries is then performed to determine if all traces of the tumour have been completely removed. This analysis is thought to provide a surrogate indication as to the likelihood for that tumour to recur after surgery. However, it is recognised that tumour recurrence may not occur even when microscopic evidence of tumour has been identified at the resection margins, and recurrence can also occur when conventional histology has considered the tumour to have been completely removed. The explanations for this dichotomy are numerous and include technical and practical limitations of the processing methodology, and also several surgeon-related and tumour-related reasons. Ultimately, the inability to confidently determine when a tumour has been removed sufficiently to prevent recurrence can impact on the ability to provide owners with confident treatment advice. In this article, the authors describe the challenges with defining the true extent of the tumour margin from the perspective of the surgeon, the pathologist and the tumour. The authors also provide an analysis of why our current efforts to ensure that all traces of the local tumour have been successfully removed may provide an imperfect assessment of the risk of recurrence.

Defining the relevance of surgical margins. Part two: Strategies to improve prediction of recurrence risk

Due to the complex nature of tumour biology and the integration between host tissues and molecular processes of the tumour cells, a continued reliance on the status of the microscopic cellular margin should not remain our only determinant of the success of a curative-intent surgery for patients with cancer. Based on current evidence, relying on a purely cellular focus to provide a binary indication of treatment success can provide an incomplete interpretation of potential outcome. A more holistic analysis of the cancer margin may be required. If we are to move ahead from our current situation - and allow treatment plans to be more intelligently tailored to meet the requirements of each individual tumour - we need to improve our utilisation of techniques that either improve recognition of residual tumour cells within the surgical field or enable a more comprehensive interrogation of tumour biology that identifies a risk of recurrence. In the second article in this series on defining the relevance of surgical margins, the authors discuss possible alternative strategies for margin assessment and evaluation in the canine and feline cancer patient. These strategies include considering adoption of the residual tumour classification scheme; intra-operative imaging systems including fluorescence-guided surgery, optical coherence tomography and Raman spectroscopy; molecular analysis and whole transcriptome analysis of tissues; and the development of a biologic index (nomogram). These techniques may allow evaluation of individual tumour biology and the status of the resection margin in ways that are different to our current techniques. Ultimately, these techniques seek to better define the risk of tumour recurrence following surgery and provide the surgeon and patient with more confidence in margin assessment.

Exosomal lncRNA PCAT1 Promotes Tumor Circulating Cell-Mediated Colorectal Cancer Liver Metastasis by Regulating the Activity of the miR-329-3p/Netrin-1-CD146 Complex

Objective

This study explored the colorectal cancer exosome lncRNA prostate cancer associated transcript 1– (PCAT1) mediated circulating tumors and the mechanism of cell colorectal cancer liver metastasis.

Methods

Exosomes were extracted from the primary colorectal cancer (CRC) cell lines HCT116 and SW480 and cultured with T84 and human umbilical vein endothelial (HUVE) cells. The expression of PCAT1 and miR-329-3p was detected by real-time quantitative polymerase chain reaction (RT-qPCR), the expression of Netrin-1, CD146, and epithelial mesenchymal transition (EMT) related proteins was detected by Western blot, the proliferation activity of T84 cells was detected by cell counting kit 8 (CCK-8), and cell migration was detected by Transwell. The expression of the F-actin signal was detected by immunofluorescence after coculture of exosomes with human umbilical vein endothelial cells (HUVECs). Changes in subcutaneous tumor and liver nodule size after PCAT1 deletion were observed in a mouse model of liver metastasis from rectal cancer.

Results

PCAT1 expression was upregulated in primary cell lines and their exosomes. After exosomes were cocultured with colorectal cancer tumor circulating T84 cells, the expression of Netrin-1 and CD146 was upregulated, the expression of miR-329-3p was downregulated, the proliferation and migration ability of T84 cells were enhanced, and EMT occurred. After knocking down PCAT1, the above phenomenon was reversed. Similarly, after exosomes were cocultured with HUVECs, the expression of the F-actin signal increased, and after PCAT1 was knocked down, the F-actin signal also decreased. PCAT1 regulates miR-329-3p/Netrin-1 and affects the biological behavior of T84 and F-actin signal expression in HUVECs. In a mouse model of colorectal cancer liver metastasis, knocking down PCAT1 significantly reduced the nodules formed by liver metastasis in mice.

Conclusions

LncRNA PCAT1 derived from colorectal cancer exosomes regulates the activity of the Netrin-1-CD146 complex in circulating tumor cells (CTCs) to promote the occurrence of colorectal cancer EMT and liver metastasis and provides new molecular targets for the treatment of colorectal cancer liver metastasis.

Blood Circulating CD133+ Extracellular Vesicles Predict Clinical Outcomes in Patients with Metastatic Colorectal Cancer

Simple Summary

In this study, we explored the prognostic and predictive value of blood circulating EVs expressing selected surface proteins in patients with metastatic colorectal cancer (mCRC). A recently patented flow cytometry protocol was used for the identification and subtyping of blood circulating EVs in a cohort of patients with stage IV colorectal cancer (n = 54) and in a cohort of healthy controls (n = 48). We observed an increased blood concentration of tumor-induced blood circulating EVs in the mCRC cohort as compared to healthy controls. Additionally, we show an intriguing link between circulating CD133+ EVs and poor clinical outcomes in patients with mCRC. This study provides novel insights about the potential impact of EVs as a relevant source of candidate biomarkers in mCRC.

Abstract

Colorectal cancer (CRC) is one of the most incident and lethal malignancies worldwide. Recent treatment advances prolonged survival in patients with metastatic colorectal cancer (mCRC). However, there are still few biomarkers to guide clinical management and treatment selection in mCRC. In this study, we applied an optimized flow cytometry protocol for EV identification, enumeration, and subtyping in blood samples of 54 patients with mCRC and 48 age and sex-matched healthy controls (HCs). The overall survival (OS) and overall response rate (ORR) were evaluated in mCRC patients enrolled and treated with a first line fluoropyrimidine-based regimen. Our findings show that patients with mCRC presented considerably higher blood concentrations of total EVs, as well as CD133+ and EPCAM+ EVs compared to HCs. Overall survival analysis revealed that increased blood concentrations of total EVs and CD133+ EVs before treatment were significantly associated with shorter OS in mCRC patients (p = 0.001; and p = 0.0001, respectively). In addition, we observed a correlation between high blood levels of CD133+ EVs at baseline and reduced ORR to first-line systemic therapy (p = 0.045). These findings may open exciting perspectives into the application of novel blood-based EV biomarkers for improved risk stratification and optimized treatment strategies in mCRC.

Discriminating Epithelial to Mesenchymal Transition Phenotypes in Circulating Tumor Cells Isolated from Advanced Gastrointestinal Cancer Patients

Gastrointestinal (GI) cancers constitute a group of highest morbidity worldwide, with colorectal cancer (CRC) and gastric cancer being among the most frequently diagnosed. The majority of gastrointestinal cancer patients already present metastasis by the time of diagnosis, which is widely associated with cancer-related death. Accumulating evidence suggests that epithelial-to-mesenchymal transition (EMT) in cancer promotes circulating tumor cell (CTCs) formation, which ultimately drives metastasis development. These cells have emerged as a fundamental tool for cancer diagnosis and monitoring, as they reflect tumor heterogeneity and the clonal evolution of cancer in real-time. In particular, EMT phenotypes are commonly associated with therapy resistance. Thus, capturing these CTCs is expected to reveal important clinical information. However, currently available CTC isolation approaches are suboptimal and are often targeted to capture epithelial CTCs, leading to the loss of EMT or mesenchymal CTCs. Here, we describe size-based CTCs isolation using the RUBYchip™, a label-free microfluidic device, aiming to detect EMT biomarkers in CTCs from whole blood samples of GI cancer patients. We found that, for most cases, the mesenchymal phenotype was predominant, and in fact a considerable fraction of isolated CTCs did not express epithelial markers. The RUBYchip™ can overcome the limitations of label-dependent technologies and improve the identification of CTC subpopulations that may be related to different clinical outcomes.

A Novel Method for Colorectal Cancer Screening Based on Circulating Tumor Cells and Machine Learning

Colorectal cancer is one of the most common types of cancer, and it can have a high mortality rate if left untreated or undiagnosed. The fact that CRC becomes symptomatic at advanced stages highlights the importance of early screening. The reference screening method for CRC is colonoscopy, an invasive, time-consuming procedure that requires sedation or anesthesia and is recommended from a certain age and above. The aim of this study was to build a machine learning classifier that can distinguish cancer from non-cancer samples. For this, circulating tumor cells were enumerated using flow cytometry. Their numbers were used as a training set for building an optimized SVM classifier that was subsequently used on a blind set. The SVM classifier’s accuracy on the blind samples was found to be 90.0%, sensitivity was 80.0%, specificity was 100.0%, precision was 100.0% and AUC was 0.98. Finally, in order to test the generalizability of our method, we also compared the performances of different classifiers developed by various machine learning models, using over-sampling datasets generated by the SMOTE algorithm. The results showed that SVM achieved the best performances according to the validation accuracy metric. Overall, our results demonstrate that CTCs enumerated by flow cytometry can provide significant information, which can be used in machine learning algorithms to successfully discriminate between healthy and colorectal cancer patients. The clinical significance of this method could be the development of a simple, fast, non-invasive cancer screening tool based on blood CTC enumeration by flow cytometry and machine learning algorithms.