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Background:
Systematic Review

Meta-Analysis of Circulating Cell-Free DNA’s Role in the Prognosis of Pancreatic Cancer

by
Jelena Milin-Lazovic
1,†,
Petar Madzarevic
1,†,
Nina Rajovic
1,
Vladimir Djordjevic
2,3,
Nikola Milic
3,
Sonja Pavlovic
4,
Nevena Veljkovic
5,6,
Natasa M. Milic
1,7,‡ and
Dejan Radenkovic
2,3,*,‡
1
Institute for Medical Statistics and Informatics, Faculty of Medicine, University of Belgrade, 11000 Belgrade, Serbia
2
Department of Surgery, University Clinical Center of Serbia, 11000 Belgrade, Serbia
3
Faculty of Medicine, University of Belgrade, 11000 Belgrade, Serbia
4
Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, 11000 Belgrade, Serbia
5
Vinca Institute of Nuclear Sciences, National Institute of the Republic of Serbia, University of Belgrade, 11000 Belgrade, Serbia
6
Heliant Ltd., 11000 Belgrade, Serbia
7
Department of Internal Medicine, Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN 55902, USA
*
Author to whom correspondence should be addressed.
†
First co-authors.
‡
Senior co-authors.
Cancers 2021, 13(14), 3378; https://doi.org/10.3390/cancers13143378
Submission received: 19 May 2021 / Revised: 13 June 2021 / Accepted: 23 June 2021 / Published: 6 July 2021
(This article belongs to the Special Issue Surgical Treatment of Pancreatic Ductal Adenocarcinoma)
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Abstract

:

Simple Summary

Pancreatic cancer is an aggressive disease with a poor prognosis. The analysis of cell-free DNA (cfDNA) for genetic abnormalities is a promising new approach for the diagnosis and prognosis of pancreatic cancer patients. In this study, we conducted a systematic review and meta-analysis of studies that reported cfDNA in pancreatic ductal adenocarcinoma (PDAC). In total, 48 studies were included in the qualitative synthesis, while 44 were assessed in the quantitative synthesis, including 3524 PDAC patients. An overall negative impact of cfDNA and KRAS mutations on the overall (OS) and progression free survival (PFS) (HR = 2.42, 95% CI: 1.95–2.99 and HR = 2.46, 95% CI: 2.01–3.00, respectively) were found. The performance of molecular studies to assess the presence of KRAS mutation by liquid biopsy may support global efforts to improve outcomes for PDAC patients.

Abstract

Introduction: The analysis of cell-free DNA (cfDNA) for genetic abnormalities is a promising new approach for the diagnosis and prognosis of pancreatic cancer patients. Insights into the molecular characteristics of pancreatic cancer may provide valuable information, leading to its earlier detection and the development of targeted therapies. Material and Methods: We conducted a systematic review and a meta-analysis of studies that reported cfDNA in pancreatic ductal adenocarcinoma (PDAC). The studies were considered eligible if they included patients with PDAC, if they had blood tests for cfDNA/ctDNA, and if they analyzed the prognostic value of cfDNA/ctDNA for patients’ survival. The studies published before 22 October 2020 were identified through the PubMED, EMBASE, Web of Science and Cochrane Library databases. The assessed outcomes were the overall (OS) and progression-free survival (PFS), expressed as the log hazard ratio (HR) and standard error (SE). The summary of the HR effect size was estimated by pooling the individual trial results using the Review Manager, version 5.3, Cochrane Collaboration. The heterogeneity was assessed using the Cochran Q test and I2 statistic. Results: In total, 48 studies were included in the qualitative review, while 44 were assessed in the quantitative synthesis, with the total number of patients included being 3524. Overall negative impacts of cfDNA and KRAS mutations on OS and PFS in PDAC (HR = 2.42, 95% CI: 1.95–2.99 and HR = 2.46, 95% CI: 2.01–3.00, respectively) were found. The subgroup analysis of the locally advanced and metastatic disease presented similar results (HR = 2.51, 95% CI: 1.90–3.31). In the studies assessing the pre-treatment presence of KRAS, there was a moderate to high degree of heterogeneity (I2 = 87% and I2 = 48%, for OS and PFS, respectively), which was remarkably decreased in the analysis of the studies measuring post-treatment KRAS (I2 = 24% and I2 = 0%, for OS and PFS, respectively). The patients who were KRAS positive before but KRAS negative after treatment had a better prognosis than the persistently KRAS-positive patients (HR = 5.30, 95% CI: 1.02–27.63). Conclusion: The assessment of KRAS mutation by liquid biopsy can be considered as an additional tool for the estimation of the disease course and outcome in PDAC patients.

1. Introduction

Pancreatic cancer is an aggressive disease with a poor prognosis. Despite the constantly evolving therapeutic and diagnostic techniques, the survival rate for pancreatic cancer still remains low compared to other malignant tumors [1]. According to the American Cancer Society (ACS) and the National Cancer Institute (NCI), the overall 5-year survival rate for pancreatic cancer is below 9% [2]. Even the small percentage of people diagnosed with the local disease (10%) experience an aberrant 5-year survival rate of 37%. The vast majority see a fate of being diagnosed at the distant stage of the disease (53%), where the survival rate is 3% [2]. Pancreatic cancer’s low survival rates are attributed to late diagnosis, the lack of effective chemotherapy, and surgical limitations [3]. In 2017, there were 447,700 new cases diagnosed worldwide, and 441,082 deaths due to pancreatic cancer were recorded in the same year [4,5]. Pancreatic cancer accounts for 1.8% of all cancers, but causes 4.6% of all cancer deaths, thus resulting in it being the seventh highest cause of cancer death worldwide [2].
Cell-free DNA (cfDNA) has gained attention as a potential biomarker for a large variety of malignancies (lung, breast, liver, etc.) due to the increased levels of apoptosis, necrosis, pyroptosis, mitotic catastrophes, autophagy and phagocytosis present in cancer patients [6]. Thus, the detection of cfDNA changes in serum or plasma and the uncovering of genetic abnormalities being released from malignant tumors has been considered as promising candidate technique for cancer diagnosis through liquid biopsy [6,7]. The analysis of cell-free DNA (cfDNA) for genetic abnormalities is also a new promising approach for the diagnosis and prognosis of pancreatic cancer patients. Insights into the molecular characteristics of the pancreatic cancer may provide valuable information, leading to its earlier detection and the development of targeted therapies. The identification of a circulating biomarker for pancreatic cancer, in a non-invasive manner, is an exciting area of exploration, which may lead to personalized prognosis and therapeutic optimization from simple blood tests [8]. Previous studies have suggested that the vast majority of pancreatic ductal adenocarcinoma (PDAC) harbor mutations in the KRAS gene, with cfDNA mutant KRAS being an early marker of disease recurrence [9,10]. Tumor-derived cfDNA, known as circulating tumor DNA (ctDNA), has been the subject of extensive research. However, ctDNA’s clinical usability still has not been established due to the non-standardized technique for its quantification. With the introduction of digital droplet polymerase chain reaction (ddPCR), new insights in this area have been acquired [11,12]. ddPCR’s ability to aid in the determination of cfDNA and ctDNA’s size and level has been shown to yield prognostic value in pancreatic cancer [13]. In this study, we performed (1) a systematic review incorporating prior studies that explored the association between cfDNA and the prognosis of patients with PDAC, and (2) a meta-analysis which quantifies the association between the presence of KRAS mutation and overall survival (OS) and progression-free survival (PFS) in these patients.

2. Material and Methods

A systematic review was performed in accordance with the Preferred Reporting Items for Systematic Reviews [14] and the Meta-analysis of Observational Studies in Epidemiology [15]. The standardized protocol was specifically developed for the purpose of this review, and was used by independent reviewers.

2.1. Study Selection

The publications were screened for inclusion in the systematic review in two phases, and all of the disagreements were resolved by discussion at each stage with the inclusion of a third reviewer or by consensus. Studies were included based on the following criteria: (1) studies including patients with pancreatic cancer, (2) studies with blood tests for cfDNA/ctDNA, and (3) studies analyzing the prognostic value of cfDNA/ctDNA for patients’ survival results. Articles containing any of the following were excluded: (1) cfDNA/ctDNA extracted from tumor tissue; (2) studies without survival outcomes, such as OS and PFS; (3) studies lacking key data for the extraction of HR; or (4) diagnostic articles.

2.2. Search Strategy

A biostatistician with expertise in conducting systematic reviews and meta-analyses (N.M.M.) and a pancreatic cancer surgeon (D.R.) developed the search strategy. Searches of the PubMed, EMBASE, Web of Science and Cochrane Library databases until 22 October 2020 were performed for studies containing key words for cfDNA and pancreatic cancer: “cell-free DNA” or “ctDNA” or “cfDNA” or “circulating DNA” or “circulating tumor DNA” or “KRAS”, and “pancreatic cancer” or “pancreatic carcinoma” or “pancreatic adenocarcinoma”. There were no restrictions on the publication language or status. The authors of relevant studies were contacted in an attempt to obtain missing data, and to confirm the information on the study methodology and the results. The authors of relevant abstracts were contacted in order to identify eligible unpublished datasets. Reference lists of the articles that are included in the analysis were searched manually, as well as relevant reviews and editorials. Experts in the field were asked to provide information on potentially eligible studies.

2.3. Article Screening and Selection

Two reviewers (J.M.L., P.M.) independently evaluated the eligibility of all of the titles and abstracts, and performed full-text screening according to the inclusion and exclusion criteria. Disagreements were resolved by consensus (J.M.L., P.M.) or arbitration (N.M.M, D.R.).

2.4. Data Abstraction and Quality Assessment

Two reviewers (J.M.L., P.M.) independently extracted the following data: the first author’s name, year of publication, country, number of patients, study design, inclusion and exclusion criteria, TNM stage, sample origin, time of the sample collected, methods of DNA detection, detection markers, and information needed to assess the articles’ quality. The authors were contacted to clarify and confirm the accuracy of the abstracted data. The extraction of the survival outcome data included OS, PFS, disease-free survival (DFS), recurrence-free survival (RFS) and disease-specific survival (DSS). Hazard ratios (HR) with a corresponding 95% confidence interval (95% CI) were also obtained from the related articles.

2.5. Risk of Bias

The risk of bias in the individual studies was assessed according to the following criteria proposed by the GRADE (Grading of Recommendations, Assessment, Development and Evaluations) Working Group [16]: (1) failure to develop and apply appropriate eligibility criteria (the inclusion of a control population), (2) flawed measurements of both exposure and outcome, (3) failure to adequately control for confounding variables, and (4) incomplete follow-up. Two reviewers (J.M.L., P.M.) independently evaluated the risk of bias within and across the studies, and the overall quality of the gathered evidence. An adapted version of the Newcastle–Ottawa tool for observational studies was used [17].

2.6. Statistical Analysis

The assessed outcomes were OS and PFS expressed as the log HR and standard error (SE). For articles without explicit data for the HR and 95% CI, the logHR and SE were calculated by extracting the survival rates from Kaplan Meier curves using the WebPlotDigitizer v4.4 [18]. The HR was than estimated using a calculator formulated by Tierney et al. [19]. The number of patients at risk was extracted when available; if not, the numbers were calculated taking into account the total number of patients included in the survival analysis and selected time points accounting for the censored data [6,20,21,22,23,24,25,26,27,28,29,30,31]. In addition, if the HR data were not available, but were presented in the individual-level data, the HR with corresponding 95% CI were calculated by IBM SPSS, version 25 [32]. The summary HR effect size was estimated by pooling the individual trial results using the Review Manager, version 5.3, Cochrane Collaboration. The heterogeneity was assessed using the Cochran Q test and I2 statistic. According to Higgins and Thompson [33], the heterogeneity was defined as I2 > 50% or p value < 0.10. A random effect model was used due to presence of heterogeneity in all of the analysis [33]. The weight of each study was calculated by the inverse variance method and adjusted by effect models, which determined how much each study contributed to the pooled HR. Sensitivity analyses were performed in order to evaluate the effect of the sample origin and different survival outcome measures. A subgroup analysis was performed for locally advanced and metastatic disease. A separate forest plot was constructed for each analysis showing the HR (box), 95% CI (lines) and weight (size of box) for each trial. The diamond presented the overall effect size. The presence of publication bias was assessed by a linear regression test of the funnel plot asymmetry. p < 0.05 was considered to be statistically significant.

3. Results

3.1. Systematic Review

A total of 5768 potentially eligible articles were found. After duplicates were removed, 3997 titles and abstracts were screened. After reading the titles and abstracts, 3694 articles were excluded because they were not original studies, examined populations other than humans (animals, cell lines), examined other diseases, did not measure ctDNA/cfDNA, or were retracted studies, author corrections or abstracts. Of the 303 reviewed full text articles, 255 were excluded because they were not written in the English language, had no survival data, had no liquid biopsy data, were methodological studies, were ongoing clinical trials, or the full-text version of the article was not available. A total of 48 articles were selected for inclusion in the systematic review, and 44 studies were included in the meta-analysis. The flow chart presenting the steps of the study selection in detail is shown in Figure 1.
The characteristics of all of the 48 publications included in the systematic review are presented in detail in Table 1. Most of the studies were conducted in China or Japan (Figure 2). The studies were published between 1996 and 2020, with a minimum sample size of 10 [20] and a maximum of 210 patients [34]. The UICC/AJCC TNM classification was given by an exact number of patients in each stage in 18 of the studies [23,25,28,30,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48], while the UICC/AJCC TNM classification was not reported for a subgroup of patients for which the ctDNA was measured, but was instead given for a total number of patients included in the study in four studies [20,49,50,51]. In total, 42 studies measured the cfDNA in plasma [9,13,21,24,25,26,27,28,29,30,31,34,35,36,38,39,40,41,42,43,44,45,46,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66], three studies measured it from serum [37,47,67], two studies measured it from blood [20,22] and one study examined the cfDNA both in serum and plasma [23]. The time of the sampling was pre-treatment in 29 studies [9,13,23,24,26,29,34,36,37,38,39,40,43,44,46,49,52,53,54,55,57,58,60,62,63,64,65,66,67], pre/post-treatment in 10 studies [25,27,28,30,35,41,42,45,47,59], post-treatment only in two studies [48,50], pre/post and during treatment in one study [22], and six studies did not report the time of sampling [20,21,31,51,56,61]. KRAS was explicitly measured in 41 studies [9,20,21,22,23,24,25,26,27,28,29,30,31,34,35,36,37,38,39,41,42,43,44,45,46,47,48,49,50,52,53,54,55,57,58,59,60,61,62,63,64,67], the cfDNA/ctDNA total concentration was measured in four studies [13,52,56,65], cfDNATFx was measured in one study [66], hypermethylation was measured in one study [40], TP53 was measured in one study [62], ERBB exon 17 was measured in one study [58], and SPARC MI, UCHL1 MI, PENK M and NPTX2 MI were measured in one study [51].
A total of 44 studies used polymerase chain reaction (PCR) (ddPCR in 24 [9,23,24,25,26,28,30,31,34,37,38,41,44,45,46,49,52,55,57,58,59,60,63,67]; two used restriction fragment length-PCR (RFLP-PCR) [36,54]; two used nested PCR [53,56]; two used peptide nucleic acid-mediated clamping (PNA clamping) [22,47]; three used beads, emulsions, amplification and magnetics (BEAMing) [27,50,64]; one used mutant allele-specific PCR (MASA PCR) [35]; one used amplification-refractory mutation system PCR (ARMS PCR) [21]; one used quantitative methylation specific polymerase chain reaction (qMSP PCR) [51]; and an explicit PCR method was not reported in eight studies [20,29,39,40,42,46,48,66]). Next-generation sequencing (NGS) was used in three studies [61,62,65], and a bioassay was used as a primary method in one study [13].

3.2. Pre-Treatment KRAS Mutation, and Overall and Progression-Free Survival

A meta-analysis was performed in order to assess the relationship between the presence of KRAS mutations in PDAC patients and OS before treatment. A total of 35 studies had OS as an outcome. Four studies were excluded from the overall HR effect size calculation due to measuring hypermethylation in ctDNA [40] or only post-treatment cfDNA, [48,50] or performing cfDNA TFx analysis [66]. Finally, 31 studies were included in the meta-analysis. The presence of pre-treatment KRAS mutations had significant prognostic value for OS in PDAC (HR = 2.42, 95% CI: 1.95–2.99) (Figure 3). There was a high degree of heterogeneity in the OS analysis (I2 = 87%) and a significant presence of publication bias (p = 0.021) (Supplemental Figure S1). The sensitivity analysis, excluding two studies which examined ctDNA in serum, showed a similar HR (HR = 2.49, 95% CI: 2.00–3.10) (Supplemental Figure S2).
A meta-analysis was performed in order to assess the relationship between the presence of KRAS mutations in PDAC patients and PFS before treatment. A total of 19 studies had PFS, DFS, RFS or DSS as an outcome. The presence of pre-treatment KRAS mutations demonstrated a significant prognostic value for PFS in PDAC patients (HR = 2.46, 95% CI: 2.01–3.00, n = 19) (Figure 4). There was a high degree of heterogeneity in the PFS analysis (I2 = 48%) %) and a significant presence of publication bias (p < 0.001) (Supplemental Figure S3). The sensitivity analysis including only PFS as an outcome resulted in a similar HR (HR = 2.27, 95% CI: 1.83–2.82, n = 14) (Supplemental Figure S4).

3.3. Post-Treatment KRAS Mutation and Overall and Progression-Free Survival

A total of 10 studies examined ctDNA post-treatment; the presence of post-treatment KRAS mutations demonstrated significant prognostic value for OS in PDAC patients (HR = 3.53, 95% CI: 2.56–4.87, n = 10) (Figure 5). There was a low degree of heterogeneity in the OS analysis (I2 = 24%) and no publication bias (p = 0.186) (Supplemental Figure S5). Patients in nine studies underwent different regimes of chemotherapy; in six studies, surgery was performed, and combined radiotherapy was performed in one study.
The presence of post-treatment KRAS mutations demonstrated significant prognostic value for PFS in PDAC patients (HR = 3.53, 95% CI: 2.49–4.99, n = 10) (Figure 6). There was no heterogeneity in the PFS analysis (I2 = 0%) and no publication bias (p = 0.247) (Supplemental Figure S6). Patients in nine studies underwent different regimes of chemotherapy, and in six studies surgery was performed.
Changes in cfDNA positivity during the treatment with PFS as an outcome were examined in three studies. The responders (patients who were KRAS positive before treatment and KRAS negative after treatment) had a better prognosis than the non-responders (patients who were KRAS positive before treatment and remained KRAS positive after the treatment) (HR = 5.30, 95% CI: 1.02–27.63, n = 3) (Supplemental Figure S7).

3.4. Analysis of the Locally-Advanced and Metastatic Disease

A subgroup analysis of the studies examining the locally advanced and metastatic PDAC showed that KRAS mutations had significant prognostic value for OS (HR = 2.51, 95% CI: 1.90–3.31, n = 15) (Figure 7). There was a high degree of heterogeneity in the OS analysis (I2 = 79%) but no publication bias (p = 0.061) (Supplemental Figure S8). In the analysis examining only the metastatic disease, the effect was similar (HR = 1.90, 95% CI: 1.39–2.61, n = 6) (Supplemental Figure S9).
A subgroup analysis of the studies examining locally-advanced and metastatic PDAC showed that KRAS mutations demonstrated significant prognostic value for PFS (HR = 2.51, 95% CI: 1.98–3.19, n = 7) (Figure 8). There was not enough data to perform a separate analysis of metastatic disease with PFS as an outcome, or to test the funnel plot asymmetry (Supplemental Figure S10).

4. Discussion

In this study, we found an overall negative impact of KRAS mutations on OS and PFS in PDAC (HR = 2.42, 95% CI: 1.95–2.99 and HR = 2. 46, 95% CI: 2.01–3.00, respectively). The subgroup analysis of locally-advanced and metastatic disease presented similar results (HR = 2.51, 95% CI: 1.90–3.31). In studies assessing the pre-treatment presence of KRAS mutations, there was a high degree of heterogeneity in OS (I2 = 87%) and a moderate level of heterogeneity in the PFS analysis (I2 = 48%), which was remarkably decreased in the analysis of studies measuring post-treatment KRAS mutations (I2 = 24% and I2 = 0%, for OS and PFS, respectively).
There is a constant effort to find novel biomarkers which could improve the diagnosis, follow-up and therapeutic approaches in pancreatic cancer. The discovery that nucleic acids originating from cancer cells can be found in the peripheral circulation of cancer patients has had a major impact towards the development of non-invasive techniques, such as liquid-biopsy methodology, for the detection of tumor biomarkers. The analysis of cell-free DNA (cfDNA) for genetic abnormalities is a new promising research area for the diagnosis and prognosis of pancreatic cancer patients. CfDNA is also found in the blood of healthy individuals due to the continuous apoptosis/necrosis of hematopoietic cell line cells [6,68]. It usually consists of short fragments of less than 1000 base pairs (bp), with most being under 200bp [69]. When cell-free DNA originates from cancer cells, it is denoted as circulated tumor DNA (ctDNA). CtDNA is released into circulation primarily by the apoptosis of tumor cells and/or as a result of tumor necrosis [70,71]. Due to CtDNA’s extremely low concentration (as low as 0.01% of total cfDNA) and its fragmented and short-sized nature, the detection of the mutational status of ctDNA is very challenging, and highly sensitive techniques have to be utilized for its detection.
Different techniques are available for cfDNA/ctDNA detection: NGS, ddPCR, BEAMing, RFLP-PCR, and nested PCR, etc. For the mutational screening of cfDNA/ctDNA, the next-generation sequencing method (NGS) has been usually applied (both targeted and whole-genome sequencing). As the quantification of tumor-specific mutations in ctDNA has been shown to be more relevant for studying tumors, DdPCR was the most common technique used in the published studies due to its high sensitivity in the detection of rare mutations, its ability to quantify copy number variations and specific genomic loci, as well as its relatively simple workflow, in contrast to other methods [71]. Similar to the conventional PCR, this technology uses Taq-polymerase and primers/probes, but before the amplification reaction itself, the sample is divided into particles (”partitioning”)—tens of thousands of droplets—and the PCR reaction takes place in each of them. Another difference from conventional or real-time (qPCR) is that it is possible to perform the direct quantification of the PCR product, without using a standard curve. The primary applications for ddPCR are rare allele detection in heterogeneous samples like liquid biopsies or FFPE samples of solid tumors, non-invasive prenatal diagnostics, viral load detection, gene expression and copy number variation, single cell gene expression profiling, and the validation of low-frequency mutations identified by sequencing analysis. Moreover, epigenomic markers originating from tumor cells could be analyzed (methylation sites, circulating regulatory RNAs) [72]. A good agreement between BEAMing and ddPCR has been shown, with a kappa value of 0.91 (95% CI: 0.85–0.95) [73]. Recent advances in NGS technology have enabled similar sensitivity to the detection of ctDNA by ddPCR [74]. As each presented molecular platform has advantages and disadvantages, without evidence of a clear advantage for all of the purposes [28], the choice of platform should be determined to best meet the scientific and clinical questions being posed.
Most studies included measurements of ct/cf DNA in plasma, as plasma has been the preferred source for the extraction of circulating DNA. Even though serum contains a much higher amount (approximately a 2–24-times higher amount) of cfDNA than plasma, serum is not favored due to the possibility of contamination from white blood cells during clotting [75,76]. In this study, the sensitivity analysis excluding studies which used serum for cfDNA/ctDNA detection demonstrated similar results to those including only plasma measurements (HR = 2.49, 95% CI: 2.00–3.10).
Previous research has shown that the decrease in the levels of ctDNA during the treatment of PDAC patients may be a result of a significant reduction in the tumor burden. In contrast, the increase of the postoperative ctDNA levels may be due to a ctDNA release caused by tissue damage during surgery. Levy et al. showed that, in patients with PDAC, an endoscopic ultrasound fine-needle aspiration may be associated with increased an plasma concentration of cfDNA and the increased detection of mutant KRAS after the procedure [28]. Another reason for the increase in postoperative ctDNA levels may be a recurrence or tumor metastasis [47]. Lee et al. [42] suggested the importance of the post-operative analysis of ctDNA. Several of the studies included in this systematic review had pre/post treatment measurements of ctDNA, but only a few reported the survival between pre-positive/post-positive, pre-positive/post-negative, pre-negative/post-negative and pre-negative/post-positive patients. A meta-analysis of three studies that reported the survival between responders (pre-positive/post-negative) and non-responders (pre-positive/post-positive) presented poorer survival for persistently positive KRAS patients (HR = 5.30, 95% CI: 1.02–27.63, n = 3). Based on the main results of this meta-analysis, in terms of their survival prognosis, PDAC patients may be grouped in two categories: those who are ctDNA positive with worse outcomes, and those who are ctDNA negative with better outcomes [42,47,59]. In cases where the ctDNA is detectable at diagnosis but becomes undetectable post-treatment, a reduction in the relapse risk is present in comparison with those in whom the ctDNA remains detectable. CtDNA can provide valuable information to determine the treatment decisions stratifying patients at low and high risk of the progression and recurrence of the disease. Prospective research should be conducted based on standardized protocols in order to evaluate further treatment strategies. It was observed previously that, in the subset of patients with resectable PDAC, ctDNA may assist the clinician in the timely detection of recurrence and the concordant introduction/addition of therapeutic measures [42,77]. It should be noted that most of the studies from this systematic review included patients with varying disease stages, thus limiting the interpretation of the prognostic role of ctDNA in resectable disease, or as a marker of disease recurrence. The data collected was utilized to determine the ways in which ctDNA’s presence impacts the prognosis, rather than how specific ctDNA subtypes impact the prognosis or at what stage in the disease/treatment course these prognostic predictors are valid. Wild-type alternative and other onco-drivers present in cfDNA in specific patient cohorts (ex. KRAS G12C) are known to be highly actionable, allowing for precision medicine [78].
RAS genes (HRAS, KRAS, and NRAS) comprise the most frequently mutated oncogene family in human cancer. KRAS is mutated in 25% of all of the cancer cases, and is associated with poor disease prognosis [77]. Given that KRAS mutations are found in nearly all of the PDAC, this cancer type is arguably the most RAS-addicted cancer. Its roles in pancreatic cancer cell processes, such as increased proliferation, survival, migration and invasion, are well known [78]. An activating point mutation of the KRAS oncogene on codon 12 (exon 2) is the initiating event in the majority of PDAC cases (70–95%). KRASG12D and KRASG12V mutations constitute about 80% of the KRAS mutations in PDAC [79]. For decades, KRAS oncoprotein was classified as undruggable cancer target [77]. According to growing evidence linking KRAS mutations to increased PDAC growth, the National Cancer Institute identified the targeting of KRAS as one of four major priorities for pancreatic cancer research. Targeted therapies and KRAS inhibitors appear to be very promising. A recent review investigating small-molecule KRAS inhibitors suggested that combining the antitumor effects from innovative new KRAS inhibitors like AMG510 with other agents, nanoparticles, or other auxiliary processes that can overcome the PDAC biochemical and tissue delivery issues offers hope for a new therapeutic way forward in PDAC [80].
Recently, the significance of a multigene approach based on liquid biopsy was highlighted to guide individual tailored therapy for PDAC patients. Alterations in other driver genes such as CDKN2A, BRCA1/2, ERB2 and NTRK, etc. have been shown to be associated with PDAC, and they are also relevant to targeted treatments. In the recent study by Pishvaian et al. presenting 1856 patients with PDAC, 58% of the patients had molecular testing, actionable molecular alterations were identified in 26%, out of which 46 patients received a matched therapy as a second- or later-line therapy and presented a better OS [81]. In a study including 259 PDAC patients with varying disease stages, a potentially actionable mutation was detected in 29% [82], while in a study including patients with advanced PDAC, therapeutically relevant alterations were observed in 48% of the samples [83]. Given the difficulties that exist in obtaining a tumor sample in PDAC, the results of these studies highlight the importance of performing molecular profiling based on liquid biopsy, due to its simplicity and accessibility, and the importance of finding actionable early mutations in a tumor with limited therapeutic options. In addition, given that mutations may vary during the course of the disease, it is important to monitor these molecular changes [84]. With the ongoing debate regarding the use of neoadjuvant therapy in purely resectable PDAC patients, it should be also noted that ctDNA detection may play a relevant role in answering a key question: who, from these particular groups of patients, is a candidate for neoadjuvant therapy?
A strength of this study was the broad sensitive search strategy used across multiple bibliographic databases that resulted in 3997 articles screened and 48 studies included in the systematic review. The most recent meta-analysis of similar scope started with an initial set of 724 articles, with the inclusion of 18 articles due to its narrow specific search strategy [10]. The greatest number of patients (n = 3524) included in this analysis generated the most comprehensive meta-analysis of the assessment of the prognostic utility of cfDNA/ctDNA’s in PDAC, while the meta-analysis assessing KRAS mutations included a total 2400 patients. In addition, in order to increase the utility of the data with were not directly shown, but were available in figures or as individual data, we used several recommended techniques to obtain HRs.
This study had several limitations, related to the clarification of liquid biopsy results in general, and those related to the proper understanding of the meta-analyses’ results. The accurate interpretation of liquid biopsy results is rather challenging because of the presence of somatic mosaicism in plasma. One of the most common sources of the biological background noise of blood liquid biopsy is somatic mutation in blood cells [85]. The accumulation of somatic mutations in hematopoietic stem cells leads to their clonal expansion. This process, called clonal hematopoiesis (CH) is common in an aging healthy population [86]. Interestingly, not only mutations related to hematological malignancies, but also mutations in genes characteristic for solid tumors are detected as a result of CH. Mutations in the KRAS gene are also found as CH-mutations [87]. It is very important to exclude these non-tumor derived CH-mutations, in order to avoid the incorrect interpretation and inappropriate therapeutic management of solid tumors. CH mutations can be determined by performing the paired sequencing of plasma cfDNA and DNA from white blood cells. It is expected that artificial intelligence tools, such as machine learning, will enable the distinction between CH mutations and tumor-derived molecular alterations in liquid biopsy [85].
A relatively large number of studies were included, resulting in a wide range of initial tumor burdens, mixed-size patient groups and various methods of ctDNA detection, all of which contributed to increased heterogeneity. Different therapies, study designs and a range of follow up times also contributed to this high value of heterogeneity. Specific conclusions based on tumor stage, ctDNA concentration and mutations other than KRAS were not possible to derive. The studies included in our meta-analyses encompassed, predominantly, patients from European and Asian populations (Figure 2). Given that the misclassification of the variants coming from data that did not include dissimilar subpopulations could potentially lead to the inadequate treatments of individuals from underrepresented populations [88], the conclusions derived here should be treated cautiously. Large-scale population studies indicated that there are more significant numbers of population-specific variations than we believed previously [89,90]. Thus, the potential of ctDNA to improve the health outcomes for PDAC patients should be evaluated in the context of various populations. The results of the meta-analysis presenting the relationship between the presence of KRAS mutations before treatment and the survival of PDAC patients should be interpreted with caution due to the presence of significant publication bias.

5. Conclusions

The assessment of KRAS mutation by liquid biopsy can be considered as an additional tool for the estimation of the disease course and outcome in PDAC patients. While ddPCR was utilized in most studies to detect the KRAS mutations, due to greater test sensitivity, other technologies in the era of NGS may also be useful in clinical practice. The choice of the molecular platform should be determined in order to best meet the scientific and clinical questions being posed.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/cancers13143378/s1, Figure S1: Funnel plot of the meta-analysis presented in Figure 3, Figure S2: Forest plot presenting the relationship between presence of KRAS mutations before treatment in PDAC patients and OS: sensitivity analysis excluding samples from serum, Figure S3: Funnel plot of the meta-analysis presented in Figure 4, Figure S4: Forest plot presenting the relationship between presence of KRAS mutations before treatment in PDAC patients and PFS: sensitivity analysis including only PFS but not DFS and DSS, Figure S5: Funnel plot of the meta-analysis presented in Figure 5, Figure S6: Funnel plot of the meta-analysis presented in Figure 6, Figure S7: Forest plot comparing PFS between responders (patients who were KRAS positive before treatment and KRAS negative after treatment) and non-responders (patients who were KRAS positive before treatment and remained KRAS positive after the treatment), Figure S8: Funnel plot of the meta-analysis presented in Figure 7, Figure S9: Forest plot presenting the relationship between presence of KRAS mutations before treatment in metastatic PDAC patients and OS, Figure S10: Funnel plot of the meta-analysis presented in Figure 8.

Author Contributions

Conceptualization, J.M.-L., P.M., N.M.M., D.R.; methodology, J.M.-L., P.M., S.P., N.V., N.M.M., D.R.; software, J.M.-L., P.M., N.R., N.M.M.; validation, N.R., V.D., N.M., S.P., N.V.; formal analysis, J.M.-L., P.M., N.R., N.M., N.M.M., D.R.; investigation, J.M.-L., P.M., N.R., V.D., N.M., S.P., N.V., N.M.M., D.R.; resources, S.P., N.V., N.M.M., D.R.; data curation, J.M.-L., P.M., N.R., V.D., N.M., N.M.M.; writing—original draft preparation, J.M.-L., P.M., N.R., N.M., N.M.M., D.R.; writing—review and editing, J.M.-L., P.M., N.R., V.D., N.M., S.P., N.V., N.M.M., D.R.; visualization, J.M.-L., P.M., N.V., N.M.M.; supervision, S.P., N.V., N.M.M., D.R.; project administration, J.M.-L., P.M., N.M.M., D.R.; funding acquisition, J.M.-L., P.M., V.D., N.M.M., D.R. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All of the data generated in this research are in the manuscript or its supplemental files.

Acknowledgments

We would like to thank our colleagues from Heliant Ltd., Belgrade, Serbia, for creating a geographical overview of patient cases included in the meta-analyses.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Flow chart of the study selection.
Figure 1. Flow chart of the study selection.
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Cancers 13 03378 g002 550
Figure 2. Geographical overview of the patient cases included in the meta-analyses. Data from multicenter and multicountry studies were excluded.
Figure 2. Geographical overview of the patient cases included in the meta-analyses. Data from multicenter and multicountry studies were excluded.
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Cancers 13 03378 g003 550
Figure 3. Forest plot presenting the relationship between the presence of KRAS mutations before treatment in PDAC patients and OS.
Figure 3. Forest plot presenting the relationship between the presence of KRAS mutations before treatment in PDAC patients and OS.
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Cancers 13 03378 g004 550
Figure 4. Forest plot presenting the relationship between the presence of KRAS mutations before treatment in PDAC patients and PFS.
Figure 4. Forest plot presenting the relationship between the presence of KRAS mutations before treatment in PDAC patients and PFS.
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Cancers 13 03378 g005 550
Figure 5. Forest plot presenting the relationship between the presence of KRAS mutations after treatment in PDAC patients and OS.
Figure 5. Forest plot presenting the relationship between the presence of KRAS mutations after treatment in PDAC patients and OS.
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Cancers 13 03378 g006 550
Figure 6. Forest plot presenting the relationship between the presence of KRAS mutations after treatment in PDAC patients and PFS.
Figure 6. Forest plot presenting the relationship between the presence of KRAS mutations after treatment in PDAC patients and PFS.
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Cancers 13 03378 g007 550
Figure 7. Forest plot presenting the relationship between the presence of KRAS mutations before treatment in locally-advanced and metastatic PDAC patients and OS.
Figure 7. Forest plot presenting the relationship between the presence of KRAS mutations before treatment in locally-advanced and metastatic PDAC patients and OS.
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Cancers 13 03378 g008 550
Figure 8. Forest plot presenting the relationship between the presence of KRAS mutations before treatment in locally-advanced and metastatic PDAC patients and PFS.
Figure 8. Forest plot presenting the relationship between the presence of KRAS mutations before treatment in locally-advanced and metastatic PDAC patients and PFS.
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Table
Table 1. Overview of the current literature on circulating tumor DNA in pancreatic cancer, with survival endpoints and patient group data.
Table 1. Overview of the current literature on circulating tumor DNA in pancreatic cancer, with survival endpoints and patient group data.
Reference (Year)CountrynTNM or Other Tumor Stage Classification AvailableMedian Follow up *Sample OriginTime of Sample OriginMethodMarkerEndpoints
Nomoto [20] (1996)Japan10II-IV, numbers not reportednot reportedbloodnot reportedPCRKRASOS
Mulcahy [54] (1998)England2121 unresectable disease5.5 (1.2–16.7)plasmapre-treatmentRFLP-PCRKRASOS
Yamada [35] (1998)Japan30I = 3, II = 6, III = 7, IV = 512.2 (1.3–43.5)plasmapre/post treatmentMASA-PCRKRAS codon 12OS
Castells [36] (1999)Spain44I = 4, II = 11, III = 5, IV = 23, unknown = 19 (6–17)plasmapre-treatmentRFLP-PCRKRASOS
Chen [53] (2010)China91unresectable7 (3–21)plasmapre-treatmentNested PCRKRASOS
Earl [55] (2015)Spain31resectable = 10, locally advanced = 8, metastatic = 13not reportedplasmapre-treatmentddPCRKRASOS
Kinugasa [37] (2015)Japan75II = 2, III = 5, IV = 68not reportedserumpre-treatmentddPCRKRAS, G12VOS
Sausen [49] (2015)USA and Denmark51I–III, numbers not reported32plasmapre-treatmentddPCRKRASPFS
Semrad [21] (2015)USA28locally advanced and metastatic, numbers not reportednot reportedplasmanot reportedARMS PCRKRASOS, PFS
Singh [56] (2015)India110resectable and unresectable, numbers not reportednot reportedplasmanot reportedNested PCRctDNA concentrationOS
Hadano [38] (2016)Japan105I–II = 84, III–IV = 2154 (14–96)plasmapre-treatmentddPCRKRASOS
Tjensvoll [22] (2016)Norway14locally advanced = 2, metastatic = 123.7bloodpre/post and during treatmentPNA directed PCR clampingKRAS B1, B2OS, PFS
Adamo [57] (2017)United Kingdom26resectable = 6, non-resectable = 5, metastatic = 15not reportedplasmapre-treatmenttNGS and ddPCRKRASDSS
Ako [23] (2017)Japan40UICC1 = 22, UICC2 = 7, UICC3 = 9, not clear = 27.8 (0.3–30.2)plasma/serumpre-treatmentddPCRKRAS, G12D, G12VOS
Allenson [24] (2017)USA, Czech and Slovakia85Localized = 33, localized postsurgical = 20, locally advanced = 13, metastatic = 19not reportedplasmapre-treatmentddPCRKRASOS
Chen [39] (2017)Denmark189III = 40, IV = 149not reportedplasmapre-treatmentNGS and PCRKRASOS
Cheng [58] (2017)China188Metastatic = 188no reportedplasmapre-treatmentNGS/ddPCRKRAS G12V, ERBB2, exon 17OS
Del Re [25] (2017)Italy27III = 4, IV = 23not reportedplasmapre/post treatmentddPCRKRASPFS
Henriksen [40] (2017)Denmark95I = 11, II = 29, III = 13, IV = 42not reportedplasmapre-treatmentPCRHypermethylationOS
Pietrasz [59] (2017)France135resectable, locally advanced and stage IV34.2plasmapre/post treatmentddPCR/NGSKRASOS, PFS
Sefrioui [26] (2017)France58resectable = 16, locally advanced = 18, metastatic = 247.5 (1–64)plasmapre-treatmentddPCRKRAS ctDNAOS
Van Laethem [50] (2017)Belgium60II-IV, numbers not reportednot reportedplasmapost treatmentBEAMingKRASOS
Kim [60] (2018)Korea106resectable = 41, locally advanced = 25, metastatic = 4010.3 (0.07–19.96)plasmapre-treatmentddPCRKRAS concentrationOS, PFS
Kruger [27] (2018)Germany54locally advanced = 7, metastatic = 47not reportedplasmapre/post treatmentBEAMingKRASOS, PFS
Lapin [13] (2018)Norway61locally advanced = 6, metastatic = 557.7 months (0.3–25.8)plasmapre-treatment and duringAgilent 2100 Bioanalyzer and Agilent High Sensitivity DNA kitcfDNA concentration and sizePFS, OS
Levy [28] (2018)USA35I = 3, II = 16, III = 7, IV = 911.2 (5.48–13.2)plasmapre/post treatmentddPCRKRASOS
Lin [46] (2018)China65UICCI/II = 5, UICCIII/IV = 6021plasmapre-treatmentddPCRKRAS codon 12OS
Nakano [47] (2018)Japan45I = 2, II = 43not reportedserumpre/post treatmentPNA directed PCR clampingKRAS codon12/13OS, DFS
Perets [29] (2018)Israel17metastatic = 14not reportedplasmapre-treatmentPCRKRASOS
Yang [41] (2018)China35I = 3, II = 29, III = 312.4 (6.1–17.2)plasmapre/post treatmentddPCRKRASOS, PFS
Bernard [52] (2019)USA104Metastatic = 104187 daysplasmapre-treatmentddPCRctDNA, KRASPFS
Groot [30] (2019)USA59I–II = 43, III–IV = 1616 (13–19)plasmapre/post treatmentddPCRKRASOS, PFS, RFR
Lee [42] (2019)Australia, New Zealand and Singapore131I = 1, II = 3, III = 3338.4plasmapre/post treatmentPCRKRASRFS, OS
Liu [43] (2019)China112I/II = 58, III/IV = 22not reportedplasmapre-treatmentPCRKRASOS
Mohan [9] (2019)England55locally advanced disease = 24, metastatic = 31no reportedplasmapre-treatmentNGS/ddPCRKRASOS
Patel [61] (2019)USA94Advanced = 9418.2 (95% CI, 13.7–22.7).plasmanot reportedNGSKRASOS
Shi [44] (2019)China113I = 49, II = 57, III = 723.6plasmapre-treatmentNGS/ddPCRKRASRFS, OS
Watanabe [45] (2019)Japan78locally advanced = 13, metastasis = 20, recurrence = 4, peritoneal dissemination = 2. I = 3, II = 33, III = 1, IV = 116.2plasmapre/post treatmentddPCRKRAS (C12V, G12D, G12R, and Q61H)OS
Bachet [62] (2020)Multicenter113advanced = 113not reportedplasmapre-treatmentNGSKRAS, TP53OS, PFS
Cheng [34] (2020)China210III = 71, IV = 139not reportedplasmapre-treatmentddPCRKRAS G12V, G12DOS
Jiang [48] (2020)China27I = 13, II = 9, IV = 518.6 months (12.4–28.9)plasmapost treatmentPCR/NGSKRASDFS
Okada [31] (2020)Japan96resectable = 66 unresectable = 30not reportedplasmanot reportedddPCRKRASDFS
Singh [51] (2020)India61I-IV, numbers not reported36plasmanot reportedqMSP PCRSPARC MI, UCHL1 MI, PENK M, NPTX2 MIOS
Strijker [63] (2020)The Netherlands and Italy58no metastases on baseline imaging or distant lymph only = 10, liver metastases = 37, metastases other than liver = 1012.3 (2.3–27.7)plasmapre-treatmentNGS/ddPCRKRAS codon 12/13OS
Sugimori [67] (2020)Japan47locally advanced = 17, peritoneal metastasis = 9, liver/lung metastasis = 21not reportedserumpre-treatmentddPCR/NGSKRAS G12/13PFS
Toledano [64] (2020)Spain61distant metastasis = 61not reportedplasmapre-treatmentBEAMingKRASOS, PFS
Uesato [65] (2020)Japan104advanced and liver metastasis = 104not reportedplasmapre-treatmentNGSctDNAOS, PFS
Wei [66] (2020)China70no liver metastasis = 12, liver metastasis = 51not reportedplasmapre-treatmentWGS/PCRctDNATFxOS
BEAMing, beads, emulsions, amplification and magnetics; ddPCR, digital droplet polymerase chain reaction; NGS, next-generation sequencing; PCR, polymerase chain reaction; RFLP-PCR, restriction fragment length polymorphism polymerase chain reaction; MASA-PCR, mutant allele-specific polymerase chain reaction; PNA-clamping PCR, peptide nucleic acid-mediated clamping polymerase chain reaction; NGS, next-generation sequencing; WGS, whole-genome sequencing; ARMS PCR, amplification-refractory mutation system; TFx, tumor fraction; OS, overall survival; RFR, relapse free rate; PFS, progress-free survival; DSS, disease specific survival; DFS, disease free survival; qMSP PCR, quantitative methylation specific polymerase chain reaction. * median follow up in months.
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Milin-Lazovic, J.; Madzarevic, P.; Rajovic, N.; Djordjevic, V.; Milic, N.; Pavlovic, S.; Veljkovic, N.; Milic, N.M.; Radenkovic, D. Meta-Analysis of Circulating Cell-Free DNA’s Role in the Prognosis of Pancreatic Cancer. Cancers 2021, 13, 3378. https://doi.org/10.3390/cancers13143378

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Milin-Lazovic J, Madzarevic P, Rajovic N, Djordjevic V, Milic N, Pavlovic S, Veljkovic N, Milic NM, Radenkovic D. Meta-Analysis of Circulating Cell-Free DNA’s Role in the Prognosis of Pancreatic Cancer. Cancers. 2021; 13(14):3378. https://doi.org/10.3390/cancers13143378

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Milin-Lazovic, Jelena, Petar Madzarevic, Nina Rajovic, Vladimir Djordjevic, Nikola Milic, Sonja Pavlovic, Nevena Veljkovic, Natasa M. Milic, and Dejan Radenkovic. 2021. "Meta-Analysis of Circulating Cell-Free DNA’s Role in the Prognosis of Pancreatic Cancer" Cancers 13, no. 14: 3378. https://doi.org/10.3390/cancers13143378

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