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27:10Endocrine-Related Cancer

L de Mestier et al. MGMT status in digestive NET R391–R405

-20-0227

REVIEW

Critical appraisal of MGMT in digestive NET treated with alkylating agents

Louis de Mestier 1,2, Anne Couvelard2,3, Anela Blazevic4, Olivia Hentic1, Wouter W de Herder 4, Vinciane Rebours1,2, Valérie Paradis2,3, Philippe Ruszniewski1,2, Leo J Hofland4 and Jérôme Cros 2,3

1Department of Gastroenterology-Pancreatology, ENETS Centre of Excellence, Beaujon University Hospital (APHP), and Université de Paris, Clichy, France2Centre of Research on Inflammation, INSERM U1149, Paris, France3Department of Pathology, ENETS Centre of Excellence, Bichat/Beaujon University Hospitals (APHP), and Université de Paris, Clichy/Paris, France4Division Endocrinology, Department of Internal Medicine, ENETS Centre of Excellence, Erasmus Medical Center, Rotterdam, The Netherlands

Correspondence should be addressed to L de Mestier: louis.demestier@aphp.fr

Abstract

The efficacy of alkylating agents (temozolomide, dacarbazine, streptozotocin) in patients with advanced neuroendocrine tumors (NETs) has been well documented, especially in pancreatic NETs. Alkylating agents transfer methyl adducts on DNA bases. Among them, O6-methylguanine accounts for many of their cytotoxic effects and can be repaired by the O6-methylguanine-methyltransferase (MGMT). However, whether the tumor MGMT status could be a reliable biomarker of efficacy of alkylating agents in NETs is still a matter of debate. Herein, we sought to provide a critical appraisal of the role of the MGMT status in NETs. After reviewing the molecular mechanisms of repair of DNA damage induced by alkylating agents, we aimed to comprehensively review the methods of determination of the MGMT status and its impact on prognosis, prediction of objective response and progression-free survival in patients with advanced digestive NETs treated by alkylating agents. About half of pancreatic NETs are MGMT-deficient, as determined by impaired tumor MGMT expression or by MGMT promoter methylation. Overall, while published studies are heterogeneous and mostly limited in size, they advocate that MGMT deficiency may be a relevant biomarker for increased objective response rate, prolonged progression-fee survival and overall survival in patients with advanced NETs treated by alkylating agents. While these data require confirmation in prospective controlled studies, future research should focus on the standardization of MGMT status assessment. Additional mechanisms of repair of DNA damages induced by alkylating agents should be explored in order to identify biomarkers complementary to MGMT and targets for potential antitumor synergy, such as PARP.

Introduction

Well-differentiated digestive neuroendocrine tumors (NETs) are associated with distant metastases in 60–80% of patients (Pavel et al. 2016). In these patients, therapeutic options include somatostatin analogs, cytotoxic or targeted agents, liver-directed therapies and

peptide-radionuclide receptor therapy (Frilling et  al. 2014, Pavel et  al. 2016). No clear recommendation exist regarding therapeutic sequences, given the lack of predictive biomarkers or trials comparing these treatments (Walter et al. 2012, Frilling et al. 2014, Pavel et al. 2016,

Endocrine-Related Cancer (2020) 27, R391–R405

10

Key Words

f neuroendocrine tumors

f DNA repair enzymes

f biomarkers

f antineoplastic agents/alkylating

f dacarbazine

f temozolomide

f streptozotocin

27

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de Mestier et  al. 2020a). Chemotherapy remains a therapeutic cornerstone as it can benefit to patients with aggressive tumors (i.e. progressive, with high tumor burden and/or high Ki67 index), or in a neoadjuvant setting aiming to obtain tumor shrinkage (Frilling et al. 2014, Pavel et al. 2016). Of note, chemotherapy is especially relevant in pancreatic NETs (PanNETs), which are more chemo-sensitive than intestinal NETs.

The most widely used chemotherapy regimens in PanNETs rely on alkylating agents, such as streptozotocin (STZ) (combined with 5-fluorouracil (5FU) or doxorubicin (Dilz et  al. 2015, Clewemar Antonodimitrakis et  al. 2016)), dacarbazine (DTIC) (alone or combined with 5FU (Walter et  al. 2015, de Mestier et  al. 2019)) and its oral prodrug temozolomide (TMZ) (alone or combined with capecitabine (CAP) (Koumarianou et al. 2015, de Mestier et al. 2020b). However, chemotherapy has been scarcely compared to other therapeutic options yet.

Although biomarkers predictive of the efficacy of alkylating agents would be of major interest in order to help selecting the best candidates, there is currently no such biomarker validated for clinical practice. Over the last decade, an increasing number of publications have explored whether the tumor expression of O6-methylguanine-DNA methyltransferase (MGMT) could influence the efficacy of alkylating agents (Koumarianou et  al. 2015). However, the published studies were heterogeneous, with an inconstant association of low MGMT tumor expression with a better efficacy of TMZ (Kulke et  al. 2009, Walter et  al. 2015, Cros et  al. 2016). Such discrepancies might be explained by methodological differences.

The objectives of this paper were to comprehensively review the evidence-based role of MGMT as a predictive biomarker of the efficacy of alkylating agents in digestive

well-differentiated NET and to draw perspective regarding its possible use in clinical practice. This review will mainly focus on PanNETs, which stand as the main indication of chemotherapy among digestive well-differentiated NET. To provide a comprehensive and up-to-date review, we searched PubMed, Web of Science and Google Scholar electronic databases for relevant articles published between 2000 and 2020, with no language restrictions, using the following keywords ‘neuroendocrine tumor’, ‘temozolomide’, ‘dacarbazine’, ‘streptozotocin’, ‘biomarker’, ‘O6-methylguanine-DNA methyltransferase’ and/or ‘MGMT’ and excluding case reports. Relevant studies were also identified through the bibliographic references of selected articles.

Reparation of DNA lesions induced by alkylating agents

STZ, DTIC and TMZ are triazene SN1 alkylating agents that transfer an alkyl (or methyl) carbon group on DNA bases. As monofunctional agents, they modify a single site in DNA and thus cannot induce interstrand crosslinks (Fu et al. 2012). They target ring nitrogen atoms and extracyclic oxygen groups in DNA bases, inducing essentially the formation of N7-methylguanine (7meG), N3-methyladenine (3meA) and O6-methylguanine (O6meG) (Fig. 1) (Drabløs et al. 2004). By itself, 7meG is not cytotoxic but may induce spontaneous depurination that is toxic and mutagenic. Conversely, the 3meA lesion can block DNA polymerases, inhibit DNA synthesis and is thereby highly cytotoxic. O6meG can mispair with thymine during DNA replication, causing many of the mutagenic and cytotoxic effects of alkylating agents (Ochs & Kaina 2000). In addition, alkylating agents can induce

Figure 1Main DNA lesions caused by SN1 alkylating agents (streptozotocine, dacarbazine and temozolomide) on purine bases ALKBH, AlkB homologue family; BER, base excision repair; NER, nucleotide excision repair; MGMT, O6-methylguanine-DNA methyltransferase; MMR, mismatch repair; TLS, translesion DNA synthesis.

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the methylation of N1-adenine (1meA) and N3-cytosine (3meC) in ssDNA (during replication, transcription or recombination), thereby causing replication-blocking and mispairing lesions (Fu et al. 2012).

The biological response to alkylating agents is complex, because one single alkylating agent can cause a variety of DNA lesions while several repair mechanisms can be elicited on alkylation damage, through an overlap of substrates, compensating pathways and crosstalk between them. The main repair systems include direct DNA repair involving the ALKB homologue family and MGMT, in addition to the multistep nucleotide excision repair (NER) and base excision repair (BER) pathways (Fig. 2). Supplementary repair systems include the mismatch repair pathway (MMR) which can identify and process the O6meG:T mispairs. Double-strand DNA breaks can be repaired by homologous recombination (HR), which uses homologous DNA (sister chromatids) as a model to resynthesize DNA across the break, or by the non-homologous end joining (NHEJ) system, which joins the DNA ends with little/no homology. The translesion DNA synthesis (TLS) pathway uses low-fidelity DNA polymerases to bypass lesions inaccessible to high-fidelity DNA polymerases, thereby relieving DNA replication blockage. Of note, NHEJ and TLS are subject to low fidelity with potential mutagenicity.

Since alkylating damages on DNA require multiple repair pathways, any impairment of one of these pathways can affect the cellular sensitivity to alkylating-based chemotherapies (Fu et al. 2012). The MGMT alkyl-transferase repair protein directly reverses O6meG lesions in DNA without intermediary step or the generation of

DNA strand breaks (Kaina et al. 2007, Pegg 2011, Fu et al. 2012). It catalyzes the transfer of the methyl adduct onto a cysteine in its active site, thereby removing the lesion from the DNA. The loss of MGMT expression impairs cell survival after exposure to alkylating agents, such as demonstrated in Mgmt−/− mice (Hansen et  al. 2007). Conversely, the protective effect of MGMT against alkylating agents has been demonstrated in several models, by increasing the amount of direct repair activity in the cell. Notably, several types of malignancies, including brain, breast, colon and lung malignancies, have increased MGMT activity compared with corresponding normal tissue (Christmann et  al. 2011). While the relationship between MGMT activity and clinical outcomes is ill-determined for most of cancer models, low MGMT expression or epigenetic silencing of MGMT expression by promoter methylation are correlated with improved therapeutic responses to alkylating chemotherapies in several brain tumors (Hegi et al. 2005, Christmann et al. 2011).

In addition to MGMT, multiple pathways influence the cellular effects of O6meG lesions induced by alkylating agents, such as the TLS, the MMR and the HR pathways (Fu et al. 2012). The TLS pathway can overcome O6meG-induced blocked DNA replication forks but, owing to its low-fidelity repair mechanism, replication past by TLS yields the generation of O6meG:T mispairs. The latter is recognized by MSH2 and MSH6, forming the MUTSα unit of the MMR pathway (Casorelli et  al. 2008). The MMR complex may then remove and replace the mismatch thymine by another thymine opposite the O6meG. These cycles of futile DNA processing promote the formation of replication fork collapse leading to cytotoxic

Figure 2Main mechanisms of repair of the DNA lesions induced by SN1 alkylating agents ALKBH, AlkB homologue family; BER, base excision repair; NER, nucleotide excision repair; HR, homologous recombination; MMR, mismatch repair; TLS, translesion DNA synthesis.

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double-strand DNA breaks that trigger apoptosis (Mojas et  al. 2007, Fu et  al. 2012). Hence, alkylation-induced cell death relies on the MMR-dependent recognition of O6meG lesions, and MMR-deficient cells are resistant to the cytotoxic effects of SN1 alkylating agents (Hickman & Samson 2004, Klapacz et  al. 2009, Fu et  al. 2012). O6meG adducts that escape MMR processing are highly mutagenic, oncogenic and can lead to the chemo-induce selection of resistant cancer clones (Bugni et al. 2009).

The HR pathway can overcome MMR-induced double-strand DNA breaks, and thus stand as another cellular survival mechanism against the cytotoxic effects of O6meG (Roos et al. 2009). Consistently, cells that are deficient in the HR pathway display increased sensitivity to SN1 alkylating agents (Fu et al. 2012). Nevertheless, the HR pathway induces sister chromatid exchanges, resulting in additional mutagenicity and cellular damages.

Post-transcriptional regulation of MGMT and polymorphisms in MGMT

Understanding how the MGMT gene is regulated is crucial for two aspects: (1) it may open therapeutic avenues if a mechanism of MGMT expression can be targeted and/or if MGMT silencing can be achieved and (2) if there are important post-transcriptional mechanisms controlling the MGMT level, the methylation of MGMT promoter may not be a good surrogate for the amount of enzyme present in the tumor. Most of the mechanisms exposed below were described in glioblastoma cell lines or tumors and may not be completely applicable to NETs as their biology is radically different.

Ramakrishnan et al. (2011) demonstrated early in the TCGA dataset that, whereas MGMT promoter methylation is associated with low MGMT mRNA expression, there is a wide range of MGMT mRNA when the promoter is unmethylated and a low correlation between mRNA and protein levels, which suggests important post-transcriptional mechanisms. Since then, multiples factors were described to directly or indirectly control MGMT expression such as Wnt, STAT3, MEK, GSK3β and NFκB (Lavon et  al. 2007, Sato et  al. 2011, Kohsaka et al. 2012, Wickström et al. 2015). In addition, multiple miRNA were described to target MGMT mRNA such as miR-181d-5p, miR-409-3p, miR142-3p and miR-602, some of them being specific of an elongated form of the MGMT mRNA (Zhang et  al. 2012, Kreth et  al. 2013, Kushwaha et al. 2014, Khalil et al. 2016, Lee et al. 2018). It was also described that histone modifications (acetylation of

H3K9) could regulate MGMT expression, independently of promoter methylation (Kitange et al. 2012). This is of particular interest as multiple histone modifiers are under clinical development. Finally, multiple studies in several types of malignancies described the relationship between MGMT promoter methylation and a polymorphism present in the promoter (rs16906252), although the underlying mechanism remains unknown. Patients with homozygous TT genotype were more likely to have their MGMT promoter methylated than patients with TC/CC genotypes and had a longer survival when treated by alkylating agent (Candiloro & Dobrovic 2009, Hawkins et al. 2009, Kristensen et al. 2011, Leng et al. 2011, Rapkins et  al. 2015). More recently, Hsu et  al. (2017) reported a novel SNP (rs1625649) located in the promoter region and associated with its methylation and low MGMT protein level. Taken together, all these mechanisms highlight the complexity of MGMT regulation and may explain the discrepancies observed between promoter methylation and protein level.

Methodology of evaluation of the MGMT status

Many different techniques have been proposed to evaluate the MGMT status and there is no consensus yet on how to best determine this biomarker, impairing its wide use in clinical practice. Most of the studies that attempted to define the best evaluation method were performed in glioblastoma-derived cell lines or samples. The ideal method would be to measure MGMT enzymatic activity directly within the tumor but because it requires fresh or frozen tissues, it is only restricted to in vitro mechanistic studies (Robinson et al. 2010). In the glioblastoma field, the most used method is the evaluation of the MGMT promoter methylation as an inverse surrogate of the MGMT status while in the NET field, immunohistochemistry (IHC) has been proposed by several groups, alone or together with promoter methylation assessment. MGMT promoter methylation clearly depicts how much work is still needed to standardize this biomarker. First, there is still a debate regarding the regions and the CpGs that are indeed controlling MGMT expression (beyond the scope of this review, discussed in Yu et al. 2019). This is of major importance as several methods of evaluation are ‘targeted’, that is, they only assess the methylation status of a finite number of CpG. Second, several methods have been proposed to evaluate MGMT promoter methylation, such as methylation-specific PCR (MS-PCR),

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methylation-specific multiplex ligation-dependent probe amplification (MS-MLPA) and pyrosequencing. It should be noted that in all studies that compared these techniques, the endpoint was not the ‘true methylation status’ (defined by methylation chips for instance) but patient survival. In all studies, pyrosequencing showed the best association with survival and is now the most widely used technique in neuro-oncology (Christians et  al. 2012, Quillien et  al. 2012). It is quantitative and can be performed in routine on formalin-fixed paraffin-embedded samples. The cut-off value usually reported in the studies is 8–10%. This cut-off gives the best rate of agreement with (q)MS-PCR, the technique that was used in most early clinical trials and also has a good predictive value in TMZ-treated patients with glioblastoma (Mikeska et al. 2007, Felsberg et al. 2011, Quillien et al. 2012). In NET, there is no study that attempted to define a ‘NET-specific cut-off’. Walter et al. (2015) and Cros et al. (2016) used the 7–8% threshold, while Raj et  al. (2017) used a higher value (20%) although it is unclear why such cut-off was chosen. This technique requires a specific equipment and a significant technical ‘hand-on time’ for bisulfite conversion and pyrosequencing. A possible issue with this technique is when tumor cells are very scarce and/or when the samples highly contaminated by non-tumor cells. Targeted evaluation of DNA methylation by next-generation sequencing (NGS) following bisulfite conversion is not yet performed in clinical practice but because of the availability of NGS in oncology platforms, this approach could replace pyrosequencing, should its calibration be robustly achieved.

On the contrary, IHC is a fast and cheap technique, available in all pathology laboratories, which allows to measure MGMT expression only in tumor cells. This approach is seducing as it truly reflects the amount of enzyme (but not its activity) regardless of all the possible mechanisms of regulation of its expression. MGMT staining is classically nuclear but additional cytoplasmic staining may also be seen. It should be noted that in most studies, only the nuclear staining was quantified and recorded. In addition, because most non-tumor cells (e.g. hepatocytes, acinar cells or inflammatory cells) express MGMT, they can be used as internal control for IHC in cases of complete loss of MGMT within tumor cells. Yet, it also suffers from a complete lack of consensus regarding the clone that should be used and how the enzyme should be quantified (Table 1). Similarly, the level of expression in non-tumor cells could be used to standardize the IHC between laboratories. Neto et al. compared several clones with the amount of MGMT mRNA defined by RT-PCR in

a series of breast tumors (Neto et al. 2011). The authors concluded that the SPM287 clone (Santa Cruz) was the most sensitive and specific. Yet, in the NET field, the MT3.1 clone has been the most widely reported in studies, while the different clones have never been compared (Kulke et al. 2009, Schmitt et al. 2014, Cros et al. 2016, Girot et al. 2017, Krug et al. 2017, Hijioka et al. 2019) (Table 1). While there is a consensus on the cut-off for CpG methylation levels, there is none on IHC. Most studies dichotomized NETs in MGMT-deficient and MGMT-proficient with a very stringent cut-off (MGMT-deficient: no expression at all or in less than 10% of the cells). An alternative is to use a continuous scoring system to better reflect all the possibilities of MGMT expression, such as the H-score (% of positive cells (0–100) × intensity (0–3), score (0–300) (Cros et  al. 2016)), the immunoreactive score (% of positive cells (0–4) × intensity (1–3), score (0–12) (Yang et  al. 2014)) or the Allred score which is similar (Allred et al. 1993, Cives et al. 2016).

Yet, in the last two studies, while MGMT staining was recorded as a continuous value, the authors used a single stringent cut-off (Yang et al. 2014, Cives et al. 2016). In a previous publication, we used the H-score and reported that only a stringent cut-off (H-score <50) was associated with radiological objective response, but that a higher value (H-score <100) could be used to define MGMT-deficient NET and still selected patients with prolonged progression-free survival (PFS) (Cros et al. 2016). The use of a continuous scoring system, while more adaptative to the expected clinical benefit, would require the choice of the clone and methods of immunohistochemistry and scoring to be standardized. Current trials on the predictive value of MGMT promoter methylation and immunohistochemistry should help to clarify the best method to assess the MGMT status (Lemelin et al. 2019).

Which method is best to assess the MGMT status has yet to be determined. Overall, IHC is well correlated to the promoter methylation in pancreatic and lung NETs, although the number of samples tested was often small (Schmitt et al. 2014, Walter et al. 2015, Cros et al. 2016, Vatrano et  al. 2020). Several studies highlighted below used promoter methylation and/or IHC and demonstrated inconsistently the prognostic or predictive value of either, both or none. More than one technique on the same samples were performed in seven studies but comparative results were only presented in five (Schmitt et  al. 2014, Walter et  al. 2015, Cros et  al. 2016, Girot et al. 2017, Raj et al. 2017). MS-PCR and pyrosequencing were only compared in one study and showed a good correlation (88% of concordant cases, κ = 0.6)

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(Walter et  al. 2015). Regarding promoter methylation and IHC, Schmidt  et  al. reported a 66% concordance (6/9 samples but correlation not significant P = 0.5), a result similar to that of Walter  et  al. (89% concordance pyrosequencing/IHC, κ = 0.7; 84% MS-PCR/IHC κ = 0.6) and higher than Girot  et  al. (50% concordance; 10/20 samples) and Raj et al. (54% concordance; 15/28). Taken together, these results highlight the importance of involvement of post-transcriptional regulations and the need for a standardized method to assess MGMT status.

MGMT status in digestive NETs

We identified 14 studies that studied the association between the MGMT status and response to alkylating

agents and/or survival in digestive NET (Table 1) (Ekeblad et  al. 2007, Kulke et  al. 2009, Schmitt et  al. 2014, Yang et  al. 2014, Dussol et  al. 2015, Walter et  al. 2015, Cives et al. 2016, Cros et al. 2016, Girot et al. 2017, Krug et al. 2017, Owen et  al. 2017, Raj et  al. 2017, Campana et  al. 2018, Hijioka et al. 2019). All of them were retrospective and only three were multicentric (Yang et al. 2014, Girot et al. 2017, Campana et al. 2018). The techniques used to determine MGMT status were highly heterogeneous. One study measured MGMT promoter methylation only, seven studies measured MGMT IHC expression only and six studies assessed both. The origin of the samples (primary tumor or metastases) was clearly reported in only two studies (Cros et al. 2016, Krug et al. 2017). This may have its importance in the interpretation of data since MGMT expression may be different between primary tumors and metastases (Yang et al. 2014).

Table 1 Studies that explored the role of MGMT status in pancreatic neuroendocrine tumors.

Study Organ of origin TechniqueDefinition for MGMT deficient (low expression or promoter methylation) MGMT deficiency

Ekeblad et al. (2007) Pancreas 35%Lung 39%

IHC (MAB16200, 1:500) <10% cells 43%

Kulke et al. (2009) Pancreas 38% IHC (MT3.1, 1:25) No staining 24%Schmitt et al. (2014) Pancreas (resected) IHC (MT3.1, 1:160) ≤5% cells 66%

MS-PCR Detection+ 56%Pancreas (metastatic) MS-PCR + MSqPCR Detection+, Threshold ≥ 10% 30%

IHC (MT3.1, 1:160) ≤5% cells 44%Yang et al. (2014) Pancreas 48%

Intestine 48%IHC (NR, 1:50) Immunoreactive score ≤ 2 31.6%

Walter et al. (2015) Pancreas 58%Intestine 31%

Pyrosequencing Threshold >8% 24%(pancreas 27%)

MS-PCR Detection+ 12%(pancreas 7%)

IHC (MT23.2, 1:200) <10% cells 33%(pancreas 36%)

Dussol et al. (2015) Pancreas 43%Intestine 33%Lung 14%

Pyrosequencing Threshold >8% 38%(pancreas 54%)

IHC (MT23.2, 1:200) <10% cells 42%Cros et al. (2016) Pancreas Pyrosequencing Threshold ≥7% 52%

IHC (MT3.1, 1:25) H-score ≤50 70%Cives et al. (2016) Pancreas IHC (MS-470-P1, 1:20) No staining 29%

<10% cells 38%Allred score <4 36%

Owen et al. (2017) Pancreas 61% IHC (MS-470-B, dilution NR)

<10% cells 57%

Girot et al. (2017) Pancreas IHC (MT3.1, 1:200) ≤10% cells 59%MS-PCR NR 15%

Raj et al. (2017) Pancreas IHC (NR) No staining 55%Pyrosequencing Threshold ≥20% 14%

Krug et al. (2017) Pancreas 83% IHC (MT3.1, 1:25) No staining 63%(pancreas 55%)

Hijioka et al. (2019) Pancreas IHC (MT3.1, 1:25) <10% cells 46%Campana et al. (2018) Pancreas 45%

Lung 22%MS-PCR Detection+ Pancreas 30% Pyrosequencing Threshold >7%

IHC, immunohistochemistry; MS-PCR, methylation-specific PCR; NR, not reported.

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Among the seven studies that analyzed MGMT promoter methylation (Schmitt et al. 2014, Dussol et al. 2015, Walter et al. 2015, Cros et al. 2016, Girot et al. 2017, Raj et  al. 2017, Campana et  al. 2018), five studies used pyrosequencing. Four of them used a threshold of CpG islands methylated between 6 and 8% and described rates of MGMT promoter methylation between 23 and 52% (Dussol et  al. 2015, Walter et  al. 2015, Cros et  al. 2016, Campana et al. 2018). This rate was 14% in the fifth study, which used more stringent criteria (at least 4/5 CpG island methylated at 20% or greater (Raj et  al. 2017)). Three studies used MS-PCR (including one that also performed primer extension-based quantitative PCR) and found rates of MGMT promoter methylation between 12 and 56% (Schmitt et al. 2014, Walter et al. 2015, Girot et al. 2017). Finally, one study found a 30% rate of MGMT promoter methylation mixing both MS-PCR and pyrosequencing (with a >7% threshold) (Campana et al. 2018).

Among the 13 studies assessing MGMT IHC, at least five different antibodies were used, including MT3.1 (n = 6), MT23.2 (n = 2), MS-470-P1, MS-470-B and MAB16200 (n = 1 each), and two studies did not specify the antibody used (Table 1). Additionally, dilution of the antibody was not systematically reported, or variable. For instance, the MT3.1 antibody could be used at very different dilutions, from 1:25 to 1:200. The scoring system used was also heterogeneous. Some authors reported a rate of MGMT-deficient tumors of 24–63% when defined by complete absence of staining (Kulke et al. 2009, Girot et al. 2017, Krug et al. 2017, Raj et al. 2017) and 33–57% when defined by ≤10% of positive cells (Ekeblad et al. 2007, Dussol et al. 2015, Walter et al. 2015, Owen et al. 2017, Hijioka et al. 2019). The studies reporting on more complex scores combining nuclear staining intensity and the percentage of stained cells reported higher rates (70–82%) of MGMT-deficient NETs (Yang et al. 2014, Cros et al. 2016).

Overall, among the five studies that assessed the MGMT status based on MGMT promoter methylation specifically in PanNET patients, representing 219 patients (without the study by Raj et al. (2017) due to too important methodological heterogeneity), the rate of patients with MGMT-deficient tumors ranged from 15 to 70% (pooled mean, 48%) (Schmitt et al. 2014, Dussol et al. 2015, Walter et al. 2015, Cros et al. 2016, Girot et al. 2017, Campana et  al. 2018). MGMT status based on protein expression was available specifically for 415 patients with PanNET included in nine studies (Kulke et al. 2009, Schmitt et al. 2014, Dussol et al. 2015, Cives et al. 2016, Cros et al. 2016, Girot et al. 2017, Krug et al. 2017, Raj et al. 2017, Hijioka et al. 2019). Although significant heterogeneity existed as

mentioned above, the rate of MGMT deficiency ranged from 29 to 70% (pooled mean, 52%). Overall, about 50% of PanNET are MGMT-deficient.

Besides using a heterogeneous methodology for MGMT status evaluation, six studies included only patients with PanNET (Schmitt et al. 2014, Cives et al. 2016, Cros et al. 2016, Girot et al. 2017, Raj et al. 2017, Hijioka et al. 2019), while the other studies included 15–65% of patients with NET from other origins (mainly gastrointestinal tract or lung). Another potentially important bias is the timing between the acquisition of the specimen used to determine the MGMT status and alkylating treatment. Indeed, it can be suspected that other DNA-damaging treatments, such as other types of chemotherapy, might influence the expression and activity of DNA repair pathways, including that of MGMT, although it was never demonstrated. For instance, we previously reported that pretreatment using chemotherapy (HR 1.79, P = 0.03) but not using somatostatin analogs (HR 1.39, P = 0.21) was independently associated with a higher risk of progression under TMZ (de Mestier et al. 2020b). Similarly, Campana  et  al. reported that previous chemotherapy independently increased the risk of progression (HR 1.92, P = 0.014) and death (HR 2.87, P = 0.003) in patients with PanNET treated with TMZ-based therapy (Campana et  al. 2018). Hence, assessing the MGMT status might be relevant only if it is carried out on a sample taken just before initiating the alkylating agent. Among the reviewed literature, this information was never reported and only six studies reported pretreatment (17–39% of patients) with chemotherapy (Schmitt et al. 2014, Cives et al. 2016, Cros et al. 2016, Girot et al. 2017, Owen et al. 2017, Campana et al. 2018). In such patients, the MGMT status should be interpreted with caution, as it might have been altered by previous chemotherapy.

Prognostic role of MGMT expression in PanNETs

Eleven studies explored the prognostic impact of the MGMT status (assessed by IHC or MGMT promoter methylation analysis) in patients with NET from various origins treated with alkylating agents (Kulke et  al. 2009, Schmitt et  al. 2014, Yang et  al. 2014, Dussol et  al. 2015, Walter et  al. 2015, Cives et  al. 2016, Krug et  al. 2017, Owen et  al. 2017, Raj et  al. 2017, Campana et  al. 2018, Hijioka et al. 2019). Most of these studies suggested that MGMT deficiency might be associated with a prolonged OS in this setting (Kulke et al. 2009, Schmitt et al. 2014,

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Yang et  al. 2014, Walter et  al. 2015, Owen et  al. 2017, Campana et al. 2018, Hijioka et al. 2019), although in a number of them the statistical significance was not reached due to low statistical power in relation with the limited number of patients included (Table 2). Nevertheless, most of these studies included patients treated with alkylating agents. In the absence of control groups not treated with alkylating agents, the prognostic impact of the MGMT status, independently from the treatment received, is difficult to distinguish from its predictive value on survival.

In 130 patients with resected non-metastatic PanNET, Schmitt et  al. (2014) reported that those with MGMT-deficient tumors had prolonged disease-free survival (P = 0.005) and disease-specific survival (P = 0.03), although none remained significant at multivariate analysis adjusted on stage, grade and CK19 expression.

Most studies have explored the prognostic impact of the MGMT status in the metastatic setting. Walter et  al. (2015) reported that the MGMT status (whatever the method of evaluation) had no prognostic impact

Table 2 MGMT status in pancreatic neuroendocrine tumors treated by alkylating agents: prediction of response and prognostic impact.

Study

Treatment

Population evaluable for response (method for MGMT status)

Comparison of MGMT-deficient vs MGMT-proficient subgroups

Objective response rate Median PFS (months) Median OS (months)

Ekeblad et al. (2007) TMZ 23 (IHC) 40% vs 8% (P = 0.13)

NR NR

Kulke et al. (2009) TMZ-based 21 (IHC) 80% vs 0% (P < 0.001)

19.2 vs 9.3(P = 0.11)

>60 vs 19.1 (P = 0.14)

Schmitt et al. (2014) TMZ 8 (MS-PCR+ MSqPCR) Rate NR (P = 0.03) NR NR9 (IHC) Rate NR (P = 0.06) NR NR

Walter et al. (2015) TMZ-based 22%DTIC-based 49%STZ-based 29%

69 (PS) 50% vs 11% (P = 0.003)

26.4 vs 10.8(P = 0.002)

77 vs 43(P = 0.026)

69 (IHC) 62% vs 7% (P < 0.001)

20.2 vs 7.6(P < 0.001)

105 vs 34(P = 0.006)

Dussol et al. (2015) TMZ-based 25% 26 (PS) 40% vs 6% (P = 0.03)

23.8 vs 6.7(P = 0.04)

63.2 vs 68.7(P = 0.88)

DTIC-based 44% 19 (IHC) 38% vs 9% (P = 0.13)

15.7 vs 6.7 (P = 0.14)

58.4 vs 62.5(P = 0.86)

Cros et al. (2016) TMZ-based 29 (PS) NR (P = 0.1) >36 vs 20(P = 0.05)

NR

43 (IHC) 50% vs 15% (P = 0.04)

Median NR(P = 0.01)

NR

Cives et al. (2016) TMZ-based 52 (IHC, no staining) 40% vs 65% (P = 0.10)

14.5 vs 16.8 (P = 0.25)

81.1 vs >100 (P = 0.40)

52 (IHC, <10% cells) 50% vs 63% (P = 0.37)

16.6 vs 14.5 (P = 0.62)

81.1 vs 73.2 (P = 0.41)

52 (IHC, Allred <4) 47% vs 64% (P = 0.25)

16.6 vs 17.4 (P = 0.54)

81.1 vs 73.2 (P = 0.27)

Owen et al. (2017) TMZ-based 14 (IHC) 63% vs 17% (P = 0.18)

16.6 vs 9.5 (P = 0.19)

42.9 vs 18.1 (P = 0.16)

Girot et al. (2017) TMZ-based 22 (IHC) 15% vs 11% (P = 1)

18 vs 9 (P = 0.84)

NR

20 (MS-PCR) 0% vs 17% (P = 1)

18 vs 15(P = 0.85)

NR

Raj et al. (2017) TMZ 77% 36 (IHC) 50% vs 31% (P = 0.26)

NR Median NR(P = 0.47)

DTIC 23% 28 (PS) 25% vs 38% (P = 1)

NR NR

Krug et al. (2017) DTICSTZ-based

24 (IHC) 50% vs 25% (P = 0.24)

12 vs 13(P = 0.82)

NR

Hijioka et al. (2019) STZ 13 (IHC) 83.3% vs 14.3% (P = 0.013)

5.6 vs 4.6(P = 0.50)

27.1 vs 14.7(P = 0.339)

Campana et al. (2018) TMZ-based 95 (MS-PCR or PS) 51.8% vs 17.7%(P = 0.001)

21 vs 8(P = 0.017)

>70 vs 23(P = 0.057)

DTIC, dacarbazine; IHC, immunohistochemistry; MS-PCR, methylation-specific PCR; NR, not reported; PS, pyrosequencing; STZ, streptozotocin; TMZ, temozolomide.

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among 107 patients with NET, 50% of them being treated by alkylating agents. However, in the subgroup of patients treated by alkylating agents, MGMT deficiency was associated with prolonged OS. The prognostic role of MGMT status was more discriminant if assessed by IHC (105 months vs 34 months, P = 0.006) than by pyrosequencing (77 months vs 43 months, P = 0.026). In addition, MGMT promoter methylation was associated with longer OS in patients with PanNET (87.8 months vs 33.3 months, P = 0.006) but not significantly in those with gastrointestinal NET (63.3 months vs 44.1 months, P = 0.45) (Walter et al. 2015). Only one large study provided a methodologically reliable multivariate analysis in the metastatic setting (Campana et  al. 2018). In the latter, the authors reported that patients with MGMT-proficient PanNET (MGMT promoter not methylated) treated with TMZ-based therapy had a 2.91-fold increased risk of death (95% CI (1.24–6.86), P = 0.014) in comparison with the MGMT-deficient PanNET group.

Overall, although the prognostic role of MGMT status was not uniformly reported, possibly due to the uneven quality of studies and the generally limited number of included patients, patients with MGMT-deficient PanNET treated with alkylating agents might have prolonged OS in comparison to those with MGMT-proficient tumors.

Of note, PanNET harboring a ‘hypermethylator’ phenotype may be associated with shorter OS. For instance, Walter et  al. (2015) reported, in a study of 47 patients (21 with PanNET and 26 with ileal NET), that OS was significantly shorter in those with NETs containing >3 methylated genes (of a panel of 12 frequently methylated genes) in comparison with those having ≤3 methylated genes. MGMT methylation was more frequent in hypermethylated NETs. Although in this population OS was not influenced by MGMT methylation, some patients received alkylating agents. This suggests that MGMT methylation could be part of a ‘hypermethylator’ phenotype, associated with a pejorative prognostic impact which may be reversed or at least counterbalanced by a better efficacy of alkylating agents.

MGMT status as a predictive biomarker of morphological response in advanced PanNETs treated with alkylating agents

The antitumor efficacy of alkylating agents in advanced NET in general and in PanNET, in particular, has been well documented. Overall, the most recent series demonstrated

an objective response rate of 30–50% with TMZ-, DTIC- or STZ-based chemotherapy (Dilz et al. 2015, Cives et al. 2016, Clewemar Antonodimitrakis et al. 2016, Krug et al. 2017, Campana et al. 2018, de Mestier et al. 2019, 2020b).

There are scarce preclinical data documenting the relationship between MGMT activity and sensitivity to alkylating agents in NETs. Using the BON1 PanNET MGMT-deficient cell line, Hijioka et al. (2019) performed MGMT lentiviral-transduced overexpression. After treatment with various doses of STZ, the MGMT-overexpressing cell line had a significantly increased viability in comparison with the parental BON1 cell line.

The influence of the MGMT status on the morphological response of advanced PanNET to alkylating agent was explored in the 13 studies reported in Table 2. As reported above, these studies were heterogeneous and most were limited in size. The alkylating agent reported was mostly TMZ, alone or combined (mostly with CAP). Among seven of them, the MGMT status was determined by promoter methylation, either by MS-PCR (Schmitt et al. 2014, Girot et al. 2017), pyrosequencing (Dussol et al. 2015, Walter et al. 2015, Cros et al. 2016, Raj et al. 2017) or both (Campana et al. 2018). Overall, four studies found that MGMT promoter methylation significantly predicted objective response to alkylating agents (Schmitt et  al. 2014, Dussol et al. 2015, Walter et al. 2015, Campana et al. 2018) and one found a statistical tendency (Cros et  al. 2016), while two studies reported no association (Girot et al. 2017, Raj et al. 2017). In the largest (n = 96) – and possibly most informative – of these studies, the objective response rates to were 51.8 and 17.7% in PanNET with and without MGMT promoter methylation, respectively (P = 0.001) (Campana et  al. 2018). Interestingly, this difference remained significant at multivariable analysis (HR 6.53, 95% CI (2.23–19.10), P = 0.001).

Among the 12 studies assessing the MGMT status using IHC, four reported that MGMT deficiency predicted alkylating agents efficacy (Kulke et al. 2009, Walter et al. 2015, Cros et  al. 2016, Hijioka et  al. 2019), six studies reported a non-significant statistical trend (Ekeblad et al. 2007, Schmitt et al. 2014, Dussol et al. 2015, Krug et  al. 2017, Owen et  al. 2017, Raj et  al. 2017) and two studies reported no association (Cives et  al. 2016, Girot et al. 2017). Of note, in one of the two latter studies, the authors found no association of the MGMT expression and objective response to TMZ-based therapy, whatever the methodology used (no staining, <10% positive cells, or Alldred quantitative score) (Cives et al. 2016). However, in this study, the main usual prognostic factors (e.g. Ki67 index) had the opposite effect as expected, questioning

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its external validity and therefore its relevance. None of these studies reported a multivariate analysis.

Interestingly, a recent meta-analysis of 11 of these studies (n = 382 patients included) reported that the proportion of patients achieving objective response was higher in those with MGMT-deficient NET (75/146, 51.4%) than in those without MGMT deficiency (40/236, 16.9%), whatever the method used (Qi & Tan 2020). This corresponded to a five-fold increase in the probability of objective response (95% CI (3.04–8.22), P < 0.001, I2 = 3%). Moreover, this difference remained statistically significant when focusing on the studies using IHC-based (OR 5.27, 95% CI (2.57–10.82), P < 0.001, I2 = 5%) or methylation-based (OR 4.73, 95% (2.38–9.42), P < 0.001, I2 = 3%) determination of the MGMT status.

The predictive impact of the MGMT status seems to be relevant in either SN1 alkylating agent. Walter et  al. (2015) reported that MGMT promoter methylation was predictive of the efficacy of either TMZ (objective response, 100% vs 9%, P = 0.008), DTIC (44% vs 9%, P = 0.05) or STZ (25% vs 15%, P = 0.44), although the latter was not statistically significant. In another study, the same group similarly reported that MGMT promoter methylation could predict the efficacy of alkylating agents (40% vs 6%, P = 0.03) but not of that of the gemcitabine–oxaliplatin combination (27% vs 26%, P = 0.92) (Dussol et al. 2015). Hence, the MGMT status is not predictive of the efficacy of any DNA-toxic chemotherapy but is specific to that of alkylating agents.

MGMT status as a predictive biomarker of progression in advanced PanNETs treated with alkylating agents

Among the ten studies that have explored the influence of the MGMT status on the PFS of patients treated with alkylating agents for an advanced NET, nine assessed MGMT IHC. The two largest studies reported that low MGMT expression was associated with significantly prolonged PFS (Walter et al. 2015, Cros et al. 2016), four studies depicted only a statistical tendency (Kulke et  al. 2009, Dussol et al. 2015, Girot et al. 2017, Owen et al. 2017) and the three last studies reported no impact (Cives et al. 2016, Krug et al. 2017, Hijioka et al. 2019). In the largest study (n = 69), patients with low MGMT expression had a prolonged PFS under alkylating agent in comparison with those with high MGMT expression (20.2 months vs 7.6 months, HR 0.19, 95% CI (0.08–0.48), P < 0.001) (Walter et al. 2015).

While all these studies used various thresholds to determine the MGMT status, the use of continuous scoring systems may reflect the wide range of expression seen in tumors. Interestingly, this allowed to describe that the impact of alkylating agents on PFS was correlated to the importance of MGMT expression. Indeed, in the study by Cros et al. (2016), the 2-year PFS rate was 65, 53 and 45% in case of H-score ≤50, ≤100 or ≤150, respectively. Similarly, more stringent thresholds could predict a higher probability of response.

In addition, four studies reported that MGMT promoter methylation was associated with prolonged PFS (Dussol et  al. 2015, Walter et  al. 2015, Cros et  al. 2016, Campana et  al. 2018), which was not confirmed by another – although smaller – study (Girot et  al. 2017). Again, MGMT promoter methylation may predict prolonged PFS using either STZ (19.5 months vs 7.6 months, P = 0.04), DTIC (26.4 months vs 12.4 months, P = 0.004) or TMZ (16.3 months vs 7.2 months, P = 0.23), although not significantly for the latter (Walter et  al. 2015). Moreover, this study provided a multivariable analysis, in which MGMT promoter methylation significantly decreased the risk of progression (HR 0.29, 95% CI (0.13–0.64), P = 0.002). This result was concordant with the study by Campana et  al. (2018), in which MGMT-proficient tumors had a significantly higher risk of progression (HR 2.62, 95% CI (1.38–5.00), P = 0.003), independently from the Ki67 index and the previous use of chemotherapy.

Overall, despite aforementioned heterogeneity of the literature, the pooled analysis of available data advocate that MGMT may be a relevant biomarker predictive of sensitivity to alkylating agents in NETs. In order to further explore this hypothesis, the MGMT-NET randomized phase II study (NCT03217097) aims at evaluating the value of the MGMT status in the prediction of the objective response at 3 months in patients treated with alkylating agents (Lemelin et al. 2019).

Other biomarkers potentially complementary to MGMT for the evaluation of the sensitivity to alkylating agents

MGMT is the first enzyme to be involved in the repair of TMZ-induced DNA damages and probably one of the most important. Yet, other DNA repair systems can play an important role in the resistance to alkylating agents. For instance, the BER pathway is another mechanism involved in the repair of DNA lesions caused by alkylating

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agents and especially N3 and N7 methyl adducts. Within the BER system, alkylpurine–DNA–N-glycosylase (APNG) removes the alkylated DNA bases carrying the 7meG, 3meA or 7meA lesions, creating apurinic sites. In glioblastoma, the APNG level correlated with resistance to TMZ (Agnihotri et al. 2012). However, there is no data in NETs regarding the predictive role of APNG.

Among the subsequent phases of the BER cascade, the limiting step is the activation of the poly-(ADP)-ribose polymerase (PARP). Alkylating agents are among the most potent activators of PARP and increasing interest has emerged in the development of PARP inhibitors as modulators of resistance to alkylating agents such as TMZ. Indeed, PARP inhibitors could potentiate the effect of TMZ by inhibiting the repair of the single-strand breaks generated by the excision of the TMZ-induced N-methylpurine (Curtin & Szabo 2013). Preclinical studies reported a synergistic enhanced sensitivity of the combination of TMZ to PARP inhibitors in various cancer models (Palma et  al. 2009), including glioblastoma (Jue et  al. 2017), small-cell lung cancer (Lok et al. 2017) and melanoma (Toshimitsu et al. 2010), which are models known to be relatively close to NET, and especially in MGMT-deficient, MMR proficient or BRCA-deficient models (Curtin & Szabo 2013, Erice et  al. 2015). Interestingly, resistance to TMZ seems to be overcome by PARP inhibitors in MGMT-proficient and/or MMR deficient models (Higuchi et al. 2020). Several phase I and II studies have reported potential antitumor activity of PARP inhibitors combined to TMZ in patients with recurrent brain tumors (Su et al. 2014, Robins et al. 2016, Blakeley et al. 2019), small-cell lung cancer (Pietanza et al. 2018), melanoma (Plummer et al. 2013, Middleton et al. 2015), colorectal cancer (Pishvaian et al. 2018) and breast cancer (Han et al. 2018). Potentiated myelotoxicity was the most frequent adverse event described. To date, data specific to the combination of PARP inhibitors and alkylating agents in NET models are very scarce. In one study, veliparib dose-dependently enhanced the efficacy of DTIC in the H727 (lung NET) and BON (small-intestine NET) cell lines, with increased DNA damage, cytotoxicity, apoptosis and decreased chromogranin A expression (Somnay et al. 2016).

Among other potential biomarkers, it has been previously shown that a deficient MMR system is also a strong predictor of TMZ resistance (Perazzoli et al. 2015). However, dMMR phenotype was only described in a limited proportion of neuroendocrine carcinomas, but not in NETs (Sahnane et al. 2015, Liu et al. 2016). Hence, there is no rational to assess it in NETs as a predictive biomarker of TMZ resistance. An important finding on

MMR was discovered when comparing post-TMZ vs pre-TMZ glioblastoma samples. It was found that about 15% of patients with recurrence had an hypermutator phenotype mostly because of MSH6 loss (Barthel et al. 2019). Should a similar mechanism existed in NETs, these patients could be good candidates for immunotherapy while classical NET patients are not. However, this remains to be demonstrated in PanNETs. Indeed, in the series by Cros et al. (2016), seven patients progressive under TMZ had a microsatellite stable status.

The homologous recombination system is required for TMZ-induced double strand DNA breaks. Rare cases of PanNETs were reported in the context of germline BRCA2 mutation. However, such cases are very rare, and there is no evidence of any impact on sensitivity to alkylating agents (Scarpa et  al. 2017). Finally, ALT activation and DAXX/ATRX loss have prognostic implication in PanNET. One study explored their impact (determined by telomeric FISH or IHC, respectively) on response to TMZ-CAP and found no association (Cives et al. 2016).

Conclusions

MGMT is the main enzyme responsible for the repair of O6meG lesions in DNA induced by alkylating agents in NETs. Increasing arguments advocate that MGMT deficiency could significantly predict increased objective response rate, prolonged PFS and prolonged OS in patients with NETs (and especially PanNETs) treated with alkylating agents. Nevertheless, many pitfalls are to be overcome in order to be able to generalize the evaluation of the MGMT status to clinical routine. While MGMT promoter methylation may be a reference technique, it may not reflect entirely the MGMT activity in tumor cells, which could be more appropriately assessed by IHC, which, however, requires standardization. Finally, the understanding of intermingled DNA repair pathways has led to identify PARP inhibitors as candidates to overcome resistance to alkylating agents in MGMT-proficient NETs.

Declaration of interestL M: Ipsen (consulting, research grant), Novartis (consulting, research grant), and Pfizer (consulting). A C: Ipsen and Novartis (research grant, consulting). W D H: Ipsen (advisory board, research grant), Novartis (advisory board), AAA (advisory board), and Pfizer (advisory board). O H: Ipsen, Novartis, and Pfizer (consulting). V R: Ipsen (research grant) and Novartis (research grant). P R: Ipsen, Novartis, AAA, Keocyt, and ITM (consulting). L H: Novartis and Ipsen (research grants). J C: Novartis (consulting). The other authors have nothing to disclose.

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FundingThis work did not receive any specific grant from any funding agency in the public, commercial, or not-for-profit sector.

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Received in final form 9 July 2020Accepted 21 July 2020Accepted Manuscript published online 22 July 2020

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