Abstract
Pancreatic neuroendocrine tumors (PNETs) encompass a diverse group of malignancies marked by histological heterogeneity and highly variable clinical outcomes. We performed a systematic review on potential prognostic biomarkers in PNETs by searching the PubMed database. A total of 472 manuscripts were reviewed in detail, of which 52 multivariate studies met the inclusion criteria proposed by the Reporting Recommendations for Tumor Marker Prognostic Studies. These altogether analyzed 53 unique targets, and 36 of them were statistically associated with survival.
Introduction
Pancreatic neuroendocrine tumors (PNETs) are a group of heterogeneous malignancies with diverse pathological findings and clinical behaviors. With an annual incidence that is less than 1 per 100,000 (Halfdanarson et al. 2008, Sonbol et al. 2020), PNETs occur predominantly in a sporadic fashion but also develop in individuals with genetic syndromes such as multiple endocrine neoplasia type I and von Hippel-Lindau disease (Alexakis et al. 2004). Traditionally, PNETs are divided into functional and nonfunctional tumors based on the presence or absence of a hormonally driven, clinically manifested endocrinopathy (e.g. hypoglycemia, diarrhea, and peptic ulcer disease) (Oberg & Eriksson 2005). Nonfunctional PNETs are more common and are likely to be diagnosed as advanced, large lesions potentially because of delayed symptom onset (Massironi et al. 2008, Ehehalt et al. 2009), but the functional status of a tumor was not associated with survival (Sonbol et al. 2020). Cytoreductive therapies are limited and do not differ between functional and nonfunctional tumors. Mainstream therapy for localized PNETs is surgical resection with regular abdominal imaging and follow-up biochemical studies if indicated (Fendrich et al. 2009, Falconi et al. 2016, Howe et al. 2020). Systemic therapy for advanced and metastatic disease can consist of somatostatin analogs, peptide receptor radionuclide therapy (PRRT), molecular therapies targeting the mammalian target of rapamycin (mTOR) or vascular endothelial growth factor (VEGF) pathways, and cytotoxic chemotherapy such as capecitabine plus temozolomide (Strosberg et al. 2011, 2017, McKenna & Edil 2014, Falconi et al. 2016, Yao et al. 2016, Kunz et al. 2018, Halfdanarson et al. 2020).
Diagnosis and prognosis of PNETs depend on both cell morphology (degree of differentiation) and tumor grade as measured by the rate of cell proliferation (Ki-67 index and/or mitotic rate). This field has been rapidly evolving. The 2017 World Health Organization (WHO) Classification for Pancreatic Neuroendocrine Neoplasia categorized PNETs into well-differentiated NETs and poorly differentiated neuroendocrine carcinomas (NECs); NETs are further stratified into three groups (grade 1, 2, and 3) based on Ki-67 index and mitotic count (Lloyd 2017, Inzani et al. 2018). This was adopted by the 2019 WHO Classification of Tumors in the Digestive System (Nagtegaal et al. 2020). Although clinical outcomes of patients with PNETs have improved (Genc et al. 2018, Sonbol et al. 2020), there are significant variations in prognosis and tumor behavior within the same class of PNETs (Milione et al. 2017), complicating treatment decisions which range from observation to aggressive chemotherapy.
In recent years, several review articles that summarize data regarding potential prognostic biomarkers for PNETs have been published (Pape et al. 2008, Oberstein & Saif 2012, Chan et al. 2017). Yet, none of these studies have applied the rigors of the Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK) criteria (McShane et al. 2005) to the analysis, and therefore, the strength and relevance of their conclusions were limited. Here, we sought to assess the evidence and clinical applications of proposed, prognostic biomarkers in PNETs by evaluating existing publications according to robust sampling, laboratory, and statistical methods.
Materials and methods
Search strategy
We searched the PubMed database through February 1, 2021, in order to identify primary research articles that analyzed molecular markers as PNET prognostic factors. A non-redundant list of candidate articles was created from the union of the following three queries:
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(pancreas OR pancreatic OR gastroenteropancreatic) AND (neuroendocrine OR endocrine) AND (prognos* OR surviv* OR predict*) AND (immunost* OR immunohistoch*)
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(pancreas) OR pancreatic OR gastroenteropancreatic) AND (neuroendocrine OR endocrine) AND (prognos* OR surviv* OR predict*) AND (gene OR protein OR marker) AND (tumor OR cancer)
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(pancreas OR pancreatic OR gastroenteropancreatic) AND (neuroendocrine OR endocrine) AND (immunost* OR immunohistoch*) AND (gene OR protein OR marker) AND (tumor OR cancer) AND progress*
A separate search using the term 'NETest' was performed as the above did not capture this recently developed analytical method.
Two investigators (M Zhu and B E G Rothberg) inspected the title and abstract of each electronic citation to identify those manuscripts that were likely to report molecular assays of PNET prognosis. The full texts of chosen manuscripts were then evaluated in detail. Additional manuscripts were identified by inspecting the reference section of each identified publication. Manuscripts not in English were excluded.
Data extraction
Three investigators (M Zhu, R Liu and B E G Rothberg) reviewed each eligible manuscript and extracted data on the characteristics of the study including number of PNETs assayed, number of functional and nonfunctional tumors, and experimental methodology and results. The data recorded about each study for metrics included first author’s name, institution, and country of origin; journal and year of publication; sample size; starting material (frozen vs paraffin-embedded); outcome assessed; clinical covariates in multivariate analysis; and candidate proteins and genes selected for analysis. We also redacted additional data within each multivariate study, including whole slides vs tissue microarray, primary antibody, dilution used, secondary signal amplification, coloration method, mention of blinding, and scoring scheme in immunohistochemistry (IHC); agents, techniques, and conditions applied to immunoassays, PCR, and fluorescence in situ hybridization (FISH); survival analysis cut-off points; and computed hazard ratio (HR) with 95% CI and corresponding P value.
Methodological and validity assessment
We integrated REMARK guidelines (McShane et al. 2005) with additional metrics for biomarker assay validation as inclusion criteria for this review. Studies were eligible if they met each of the following conditions: (1) prospective or retrospective cohort design with a clearly defined source population, boundary dates, and justifications for all excluded eligible cases; (2) assay of predominantly primary tumor specimens if tissues markers were analyzed; (3) clear descriptions of methods for experimental techniques (IHC – antigen retrieval, selection and preparation of primary and secondary antibodies, visualization techniques, and choice of positive and negative controls; immunoassays – labeling and detection methods; PCR – primers and conditions; and FISH – probes and conditions); (4) statistical analysis using multivariate proportional hazards modeling that adjusted for clinical prognostic factors including but not limited to stage or grade; and (5) reporting of the resultant-adjusted HRs with 95% CIs and corresponding P values. Studies with <50% of PNETs were excluded unless PNET-specific data were provided. With respect to manuscripts that described proportional hazards modeling but did not meet inclusion criteria because of lack of details in describing the cohort, experimental methods, and the HRs with 95% CIs and P values, we contacted the corresponding authors in an attempt to obtain the missing information. Emails were sent to 48 investigators, and replies were received from 22 groups.
Results
Overview of included studies
Our initial literature search strategies identified 4267 manuscripts for consideration. A separate search on PubMed supplemented by inspection of references identified 45 manuscripts on NETest (Fig. 1). Following the close inspection of each study based on the inclusion criteria, a total of 52 multivariate studies were included in the final analysis (Supplementary Table 1, see section on supplementary materials given at the end of this article). They were divided into five categories based on the characteristics of target biomarkers and are presented in the following sections.
Flowchart for the selection of multivariate studies.
Citation: Endocrine-Related Cancer 28, 12; 10.1530/ERC-21-0075
Flowchart for the selection of multivariate studies.
Citation: Endocrine-Related Cancer 28, 12; 10.1530/ERC-21-0075
Flowchart for the selection of multivariate studies.
Citation: Endocrine-Related Cancer 28, 12; 10.1530/ERC-21-0075
Categories of cellular markers based on proposed function.
| Stress proteins | Structural proteins | Signaling proteins |
|---|---|---|
| HMOX-1 GTna | MMP-9 | SSTR2 |
| HIF-1α | α-internexina | UCH-L1a |
| DJ-1 | Carbonic anhydrase 9 | KIT |
| HSP90a | CK19a | mTOR |
| PTEN | ||
| Survivina | ||
| HuDa | ||
| STK33a | ||
| PDGFRA | ||
| Progesterone receptor | ||
| VEGFR3a | ||
| NDRG-1a | ||
| PHLDA-3a |
aThese markers were statistically associated with survival.
Genetic and epigenetic markers
Eleven studies (Study ID 1–11) investigated the prognostic value of epigenetic and genetic markers. The presence of alternative lengthening of telomeres (ALT) was associated with poor survival (Kim et al. 2017, Singhi et al. 2017), but expression of ATRX/DAXX yielded discordant results with three studies showing that positive expression was prognostic of improved survival (Marinoni et al. 2014, Kim et al. 2017, Roy et al. 2018), while one study suggested the opposite (Park et al. 2017). Another study showed that mutations in DAXX were associated with increased tumor recurrence (Cives et al. 2019). In addition, hypomethylation of long interspersed nucleotide element-1 (LINE-1), a surrogate marker for genome-wide hypomethylation, hypermethylation of a specific gene cluster rich in DAPK1, TIMP3, PAX5, HIC1, CADM1, PYCARD, ESR1, VHL, RARB, and WT1, and upregulation of certain histone deacetylases (HDAC4, 5, 6, 8, and Sirt1) were associated with reduced survival (Stefanoli et al. 2014, Klieser et al. 2017). In the realm of genetic markers, proteins that potentially mediate DNA repair mechanisms including MGMT, thymidylate synthase, p21, ATM, and CHK2 might be associated with survival (Lee et al. 2014, Yang et al. 2014, Hua et al. 2020).
IHC-based immunologic markers
Potential correlations between immune cell infiltration and survival were explored by five studies (Study ID 12–16). Increased intratumoral FoxP3+ T cells (de Reuver et al. 2016), tumor-associated macrophages ((TAMs), CD68+CD163+) (Cai et al. 2019), tumor-infiltrating neutrophils (CD15+) (Zhang et al. 2020a), and tumor-infiltrating platelets (CD42b+) (Xu et al. 2019) were associated with reduced survival. But the presence of tertiary lymphoid structures marked by intratumoral and peritumoral aggregates of CD4+ T cells, CD8+ T cells, FoxP3+ T cells, CD45RO+ T cells, CD20+ B cells, CD11c+ dendritic cells, NCR1+ natural killer cells, and CD68+TAMs was associated with improved survival (Zhang et al. 2020b).
Neuroendocrine cell-associated markers
Five studies (Study ID 17–21) evaluated whether baseline neuroendocrine cell-associated markers were associated with the survival of patients with both functional and nonfunctional PNETs. Elevated chromogranin A (CgA) was statistically associated with reduced overall survival (OS) in three studies, but they used different cut-off points (2 × upper limit of normal (ULN), 2.5 × ULN, and 10 × ULN) and commercial assays (Yao et al. 2011, Han et al. 2015, Zandee et al. 2017). One study reported that elevated neuron-specific enolase (NSE) was statistically associated with poor OS, while 5-hydroindoleacetaic acid was not (Zandee et al. 2017). Additionally, triple-positive IHC staining of CgA, synaptophysin, and neural cell adhesion molecule was associated with improved survival (Liu et al. 2019a).
Other peripheral blood-based markers
Peripheral blood counts may help to predict survival (Study ID 22–26). A higher neutrophil-to-lymphocyte ratio (NLR) prior to surgical resection or treatment was associated with reduced survival (Zhou et al. 2017a, Gaitanidis et al. 2018, Harimoto et al. 2019, Panni et al. 2019), while a higher lymphocyte-to-monocyte ratio (LMR) at baseline was associated with improved survival (Gaitanidis et al. 2018, Zhou et al. 2020).
Five other serum-based studies (Study ID 27–31) showed that higher levels of preoperative gamma-glutamyl transferase (GGT)-to-lymphocyte ratio (Zhou et al. 2017b), preoperative C-reactive protein (CRP) (Wiese et al. 2016, Primavesi et al. 2020), baseline alkaline phosphatase (AP) (Jimenez-Fonseca et al. 2018a), and preoperative elastase (Nanno et al. 2018) were associated with poor survival.
In addition, two novel testing platforms using peripheral blood were identified. One study evaluated the prognostic value of circulating tumor cells (CTCs) of epithelial origin in PNETs (Study ID 32). In this study, 90 patients with metastatic PNETs were followed for at least 3 years or until death after initial blood collection, and a CTC ≥1 was associated with reduced progression-free survival and OS (Mandair et al. 2020). Another study evaluated the performance of NETest, which detects circulating RNA with RT-PCR (Study ID 33). Thirty-five patients with grade 1–2 (G1–2) PNETs were included, and a NETest score of >40% was associated with increased risk of tumor recurrence after surgical resection (Genç et al. 2018).
Cellular markers
Within the category of cellular markers (Study ID 34–52), we further divided the molecular targets into three groups based on their proposed function (Table 1). In the group of 'stress proteins', polymorphism of the HMOX-1 promoter region (Vashist et al. 2011) and increased expression of heat shock protein 90 (HSP90) (Gamboa et al. 2020) were statistically associated with poor survival. In the group of 'structural proteins', reduced expression of α-internexin and increased expression of CK19 (Schmitt et al. 2007) were statistically associated with poor survival (Liu et al. 2014). In the group of 'signaling proteins', expression of UCH-L1 (Song et al. 2017), nuclear survivin (Ekeblad et al. 2012), HuD (Kim et al. 2018), STK33 (Zhou et al. 2019), and certain polymorphisms of VEGFR3 (Jimenez-Fonseca et al. 2018b ) might be prognostic. In addition, a composite score based on the expression of MGMT, NDRG-1, and PHLDA-3 was found predictive of survival (Viudez et al. 2016).
Summary of potential biomarkers for future research.
| Proposed function | Biomarker | Potential clinical application |
|---|---|---|
| Genome stability | ALT, ATRX/DAXX, ATM, CHK2, LINE-1 methylation | May predict response to therapy that targets DNA repair mechanisms and predict recurrence following resection |
| mRNA stability | HuD | Potential therapeutic target |
| Stress response | HSP90, HMOX-1 promoter | Potential therapeutic target |
| Tumor proliferation | UCH-L1, survivin | Potential therapeutic target |
| Tumor migration | STK33 | Potential therapeutic target |
| Structural protein | CK19, α-internexin | Tools that may aid diagnosis |
| Byproduct of cancer | CgA, NSE, CRP, AP, elastase, GGT-to-lymphocyte ratio, circulating tumor cells, NETest score | Tools that may aid diagnosis and disease monitoring |
| Immune activation | Tertiary lymphoid structure, LMR | May predict response to immune-based therapies |
| Immune suppression | Regulatory T cells, tumor-associated macrophages, tumor-infiltrating neutrophils, tumor-infiltrating platelets, NLR | May predict response to immune-based therapies |
Discussion
Emerging biomarkers
In the class of genetic and epigenetic markers, ALT seems to be the most promising marker with two multivariate studies showing consistent results (Kim et al. 2017, Singhi et al. 2017). The ALT pathway is likely harnessed by tumor cells to attain unchecked proliferation (Cesare & Reddel 2010) and was shown to be associated with mutations of ATRX or DAXX (Marinoni et al. 2014, Kim et al. 2017, Scarpa et al. 2017). ATRX interacts with DAXX to deposit H3.3 histone chaperone in telomeres; mutations in ATRX and DAXX are mutually exclusive and result in telomere deregulation, leading to chromosomal instability (Drane et al. 2010, Goldberg et al. 2010, Lewis et al. 2010, Wong 2010, Wong et al. 2010). The prognostic role of ATRX/DAXX is, however, inconsistent because of discordant findings. Jiao et al. first reported that ATRX and DAXX were mutated in 29 of 68 PNETs and their mutations were associated with prolonged survival (Jiao et al. 2011). On the other hand, more recent studies suggest that ATRX and DAXX mutations were associated with worse clinical outcomes (Roy et al. 2018, Cives et al. 2019). Similarly, ATRX/DAXX expression based on IHC studies also yielded conflicting results (Marinoni et al. 2014, Kim et al. 2017, Park et al. 2017, Roy et al. 2018). These discordant findings are likely secondary to inclusion of localized vs metastatic tumors and genetic variances in PNETs, warranting validation studies in a larger cohort with clearly delineated characteristics.
The approval of immune checkpoint inhibitors (ICIs) for the treatment of solid tumors has fueled biomarker research in the field of immuno-oncology. Deficient mismatch repair, high microsatellite instability, high tumor mutational burden, and PD-L1 expression are established biomarkers that predict clinical benefits of ICIs, but they occur infrequently in PNETs with a reported rate <3% (Salem et al. 2018). Based on IHC studies, intratumoral infiltration by T regulatory cells and macrophages was associated with poor survival in PNETs (de Reuver et al. 2016, Cai et al. 2019). These cells likely suppress the function of cytotoxic T cells and promote tumor development (Gabrilovich & Nagaraj 2009, Shevach 2018). Although ICIs have shown limited antitumor activity in NECs and high-grade NETs (Patel et al. 2020), ongoing clinical trials may shed light on the role of immune microenvironment in PNETs.
Multiple studies showed that elevated CgA and CRP levels were statistically associated with reduced survival, but clinical utility of these two serum markers is limited by low specificity, poor reproducibility, and lack of standardized testing methods and universal cut-off points. Other peripheral blood-based biomarkers such as NLR face similar challenges as testing methods, and cut-off points tend to differ by institution. In the class of cellular markers, the expression of HSP90 (Gamboa et al. 2020), α-internexin (Liu et al. 2014), UCH-L1 (Song et al. 2017), HuD (Kim et al. 2018), and STK33 (Zhou et al. 2019) seem promising biomarkers for prognosis, but mechanistic studies are needed to understand their roles in tumorigenesis.
Notably, two novel testing platforms have emerged in the past decade (Genç et al. 2018, Mandair et al. 2020). The use of CTCs through CellSearch® was approved by the U.S. Food and Drug Administration for monitoring of metastatic breast, colorectal, and prostate cancer. This test captures CTCs of epithelial origin (CD45−, EpCAM+, CK8+, CK18+, and CK19+) from peripheral blood (Allard et al. 2004), and pre-treatment CTC levels were shown to predict survival of patients who were treated for metastatic cancer (Cristofanilli et al. 2004, Cohen et al. 2008, de Bono et al. 2008). Further studies are required to establish its utility in the management of PNETs. NETest is another peripheral blood-based test in which circulating RNAs were isolated from whole blood and quantified by real-time PCR (Modlin et al. 2013). A NET score is calculated based on 51 marker signature which detects NETs with high sensitivity and specificity (Modlin et al. 2014, 2015). Its diagnostic (van Treijen et al. 2018, Al-Toubah et al. 2020, Öberg et al. 2020) and prognostic utilities (Kidd et al. 2016, Pavel et al. 2017, Genç et al. 2018, Liu et al. 2019b, Kidd et al. 2020, van Treijen et al. 2020) have been evaluated by multiple studies, and in general, higher scores were associated with improved diagnostic accuracy and decreased survival. Its role as a predictive biomarker, especially following PRRT, is being further investigated (Bodei et al. 2016, 2018, 2020, Ćwikła et al. 2015), and it may have value in identifying patients with minimal or absent response to PRRT.
Limitations
This systemic review assessed the quality of existing literature and performance of proposed biomarkers. There are significant differences in the design, methodology, statistical analysis, and reporting of included multivariate studies, limiting the scope and strength of our conclusions. Meta-analysis was not conducted due to study heterogeneity and lack of studies on the same biomarker.
As many studies that were conducted before the publication of the 2017 WHO classification system (which acknowledged that both PNETs and pancreatic NECs can have Ki-67 >20% or mitotic index >20) contained a small percentage of grade 3 tumors according to grading systems at the time of publication, these studies could have included both PNETs and NECs based on the revised WHO classification. But as they predominantly comprised of grade 1–2 tumors, their findings are likely applicable to well-differentiated PNETs with a less aggressive clinical course.
In addition, our search did not capture recent molecular studies in PNETs which have improved our understanding of the tumor biology and may provide rationale for a better classification system. Based on whole-genome sequencing of 102 primary tumors, PNETs were found to have a mutational burden of 0.82 mutations per megabase with the most common somatic mutations identified in MEN1, ATRX, and DAXX (Scarpa et al. 2017). Chan et al. showed that tumors with these mutations had upregulated HNF1A expression and suppressed PDX1 expression, resembling the gene signature of alpha cells of pancreatic islets, and they were associated with shorter recurrence-free survival compared to WT tumors (Chan et al. 2018). Cejas et al. classified nonfunctional PNETs into two molecular subtypes that are ARX-positive vs PDX1-positive, which partially resemble alpha and beta cells of pancreatic islets, respectively, and patients with PDX1-positive tumors had longer relapse-free survival (Cejas et al. 2019). Overall, these data suggest that intertumoral heterogeneity of PNETs may be secondary to different cell lineages and should be explored further.
Conclusions
This systemic review followed the REMARK criteria to identify high-quality biomarker studies in PNETs. The amount of work published in this area is encouraging, but bench-to-bedside translation of these study results is primarily hindered by analytical variations, tumor heterogeneity, and lack of validation studies. Unfortunately, none of the forementioned biomarkers have been used consistently in clinical practice, but they present exciting opportunities for future research (Table 2). In our practice, tumor grade, Ki-67 index, and temporal evolution of imaging findings are the mainstay that guide therapeutic decisions, and we see a clear need for predictive biomarkers and therapies with novel mechanisms of action. As a result, we suggest that future biomarker studies should be guided by strong biological rationales and integrated with prospective clinical treatment trials whenever feasible.
Supplementary materials
This is linked to the online version of the paper at https://doi.org/10.1530/ERC-21-0075.
Declaration of interest
B E G R and her direct family members have stock ownership in Butterfly Networks Inc., Quantum Si, Hyperfine Research, AI Therapeutics, Detect Labs, and Tesseract. T R H receives research support (paid to institution) from Thermo Fisher Scientific, Novartis, and Advanced Accelerator Applications (a Novartis company) and has served as a consultant for Novartis, Curium, Ipsen, and ITM.
Funding
B E G R was supported by NIH K08CA151645.
Acknowledgement
The authors would like to thank all the authors of cited references.
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