Abstract

Simple SummaryPancreatic cancer is a digestive tumor that is most difficult to treat and carries one of the worst prognoses. The anatomical location of the pancreas makes it very difficult to obtain enough tumor material to establish a molecular diagnosis, so knowing the biology of this tumor and implementing new targeted-therapies is still a pending issue. The use of liquid biopsy, a blood sample test to detect circulating-tumor DNA fragments (ctDNA), is key to overcoming this difficulty and improving the evolution of this tumor. Liquid biopsies are equally representative of the tissue from which they come and allow relevant molecular and diagnostic information to be obtained in a faster and less invasive way. One challenge related to ctDNA is the lack of consistency in the study design. Moreover, ctDNA accounts for only a small percentage of the total cell-free circulating DNA and prior knowledge about particular mutations is usually required. Thus, our aim was to understand the current role and future perspectives of ctDNA in pancreatic cancer using digital-droplet PCR technology.Pancreatic cancer (PC) is one of the most devastating malignant tumors, being the seventh leading cause of cancer-related death worldwide. Researchers and clinicians are endeavoring to develop strategies for the early detection of the disease and the improvement of treatment results. Adequate biopsy is still challenging because of the pancreas’s poor anatomic location. Recently, circulating tumor DNA (ctDNA) could be identified as a liquid biopsy tool with huge potential as a non-invasive biomarker in early diagnosis, prognosis and management of PC. ctDNA is released from apoptotic and necrotic cancer cells, as well as from living tumor cells and even circulating tumor cells, and it can reveal genetic and epigenetic alterations with tumor-specific and individual mutation and methylation profiles. However, ctDNA sensibility remains a limitation and the accuracy of ctDNA as a biomarker for PC is relatively low and cannot be currently used as a screening or diagnostic tool. Increasing evidence suggests that ctDNA is an interesting biomarker for predictive or prognosis studies, evaluating minimal residual disease, longitudinal follow-up and treatment management. Promising results have been published and therefore the objective of our review is to understand the current role and the future perspectives of ctDNA in PC.

Highlights

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  • KRAS represents an important potential biomarker for PDAC. It has been the best-characterized PDAC tumor-related gene due to the following reasons: (i) among human malignancies, PDAC has the highest frequency of KRAS mutations, (ii) the most frequent KRAS point mutations are located in codon 12, and (iii) alterations in this gene appear to occur at an early stage of pancreatic carcinogenesis

  • Despite sensitivities of improved next-generation sequencing (NGS)-based approaches being similar to digital‐droplet PCR (ddPCR), this paper will focus on the potential application of ddPCR to detect circulating tumor DNA (ctDNA)

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Summary

Increasing Genetic Knowledge to Improve Clinical Practice

Better understanding of the molecular pathways that direct tumor progression is leading to the development of what we know as precision medicine. The most important frequent gene mutations include KRAS, CDKN2A, SMAD4 and TP53 Their widespread use is limited by the difficulty of obtaining tumor tissues. In 1998, Yamada et al demonstrated that KRAS mutations in plasma may be clinically useful for evaluating tumor burden and treatment efficacy for pancreatic cancer [31]. Several research studies have reported the prognosis and predictive significance of KRAS ctDNA detected in metastatic and perioperative settings, as well as their therapeutic evaluation along longitudinal monitoring of the disease [33,34]. Despite sensitivities of improved NGS-based approaches being similar to ddPCR, this paper will focus on the potential application of ddPCR to detect ctDNA for PDAC

Digital-Droplet PCR Technology
Future Perspectives
Findings
Conclusions
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