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Precision Oncology in Rare Endocrine and Neuroendocrine Neoplasms: Experiences and Challenges of the CCCMunichLMU Molecular Tumor Board
Klara Dorman 1,2 . Christoph J. Auernhammer3,11 . Christine Spitzweg3,11 . Ralf Schmidmaier3,12 . Svenja Nolting3,14.
Matthias Kroiss3,12 . Martin Reincke3,12 . Christian Schulz4,11 . Martin Angele5,11 . Jens Werner5,11.
Christine Schmid-Tannwald6,11,12 . Josefine Rauch7,11 . Mathias Zacher8,11 . Thomas Knosel2,9,11 . Jörg Kumbrink2,9 .
Andreas Jung2,9 . Frederick Klauschen2,9 . Amanda Tufman 13 . Danmei Zhang1,2 . Lena Weiss1 . Stefan Boeck1,2,10 .
Michael von Bergwelt-Baildon 1,2,11 . Volker Heinemann1,2 . C. Benedikt Westphalen1,2,11 . Kathrin Heinrich1,2,11 [D
Accepted: 4 May 2025 / Published online: 30 June 2025
@ The Author(s) 2025, corrected publication 2025
Abstract
Background Comprehensive genomic profiling (CGP) has become more generally accessible to patients with rare cancer, but data on the results and benefits are limited.
Objective Our objective was to gain a real-world understanding of the molecular landscape and targeted treatment options in neuroendocrine tumors, neuroendocrine carcinomas, adrenocortical carcinomas, pheochromocytomas, and carcinoids.
Patients and Methods In this retrospective cohort study, we analyzed CGP results and clinical data from patients with neu- roendocrine tumors, neuroendocrine carcinomas, adrenocortical carcinomas, pheochromocytomas, and carcinoids who were discussed in the CCCMunich LMU Molecular Tumor Board (MTB) between May 2017 and April 2023.
Results In total, 104 patients with endocrine and neuroendocrine neoplasms were discussed in the MTB. CGP was techni- cally successful in 99 patients. The most commonly mutated genes were TP53 (29.3%), RB1 (11.1%), and KRAS (10.1%). The highest overall prevalence of pathogenic alterations was detected in neuroendocrine carcinomas (76.9%) and carcinoids (83.3%), and the lowest prevalence of pathogenic alterations was seen in adrenocortical carcinoma (37.5%). Of the 99 patients with successful CGP, 35 received a treatment recommendation from the MTB based on the CGP results. Of these, ten patients ultimately received the recommended treatment. Of the ten treated patients, four experienced a longer progression-free survival under the targeted treatment than under their previous treatment.
Conclusions One-third of patients with rare endocrine and neuroendocrine neoplasms who underwent CGP had a druggable alteration and received a treatment recommendation from the MTB. However, only 28.6% of these patients were treated accordingly. Our experience highlights the unmet medical need for targeted treatment options in patients with rare cancers.
1 Introduction
Neuroendocrine neoplasms (NENs) are a rare and hetero- geneous subset of tumors that originate from cells with a neuroendocrine phenotype and can occur at many different anatomical sites [1]. The International Agency for Research on Cancer and World Health Organization expert consensus proposal recommends a uniform classification for NENs. Depending on their differentiation, NENs can be divided
into neuroendocrine tumors (NETs, well-differentiated) and neuroendocrine carcinomas (NECs, poorly differentiated). Furthermore, NETs should be graded as low grade, interme- diate grade, and high grade (G1, G2, and G3, respectively), depending on mitotic count, ki67, and presence of necrosis, whereas NECs are always high grade [2]. Pheochromocyto- mas are another subgroup of NETs that originate in the adre- nal medulla and can produce catecholamines [3]. The most frequent anatomical site for NENs is the digestive system, followed by the lung [4]. Along with the profound heteroge- neity of NENs, oncogenic molecular drivers and epigenetic profiles also vary greatly [5, 6].
Key Points
The spectrum of molecular alterations in rare endocrine and neuroendocrine malignancies is broad. The most common pathogenic alterations found in our cohort of patients with neuroendocrine tumors, neuroendocrine carcinomas, adrenocortical carcinomas, pheochromocy- tomas, and carcinoids were TP53 (29%), RB1 (11%), and KRAS (10%) mutations.
The most frequent treatment recommendation by the Molecular Tumor Board was immune checkpoint inhibi- tion. Although 35 of 99 patients received a molecularly informed treatment recommendation from the Molecu- lar Tumor Board, only one-third of these received the recommended targeted treatment, resulting in a survival benefit in four of ten of these patients.
Therapeutic options of chemotherapy and immunotherapy in metastatic extrapulmonary NECs of gastroenteropancre- atic and other rare primary tumor locations are limited [4, 7, 8]. In contrast, treatment of gastroenteropancreatic NETs encompasses somatostatin analogs, peptide receptor radio- nuclide therapy, chemotherapy in NETs of pancreatic ori- gin, mammalian target of rapamycin inhibitors, and tyros- ine kinase inhibitors [4, 6, 9, 10]. Treatment of metastatic pheochromocytoma involves chemotherapy, radionuclide therapies, and tyrosine kinase inhibitors [11]. Treatment strategies for lung typical and atypical carcinoids are similar to those for NETs but with specific guideline recommenda- tions [12].
Although some of these approaches are targeted thera- pies, the treatment decisions are usually not based on the molecular pathology of the tumors, but rather the anatomi- cal site, grading, growth dynamics, and somatostatin recep- tor status assessed by imaging of the NEN, as well as the patient’s comorbidities and preferences [6].
In some tumor entities, such as cholangiocarcinoma and lung adenocarcinoma, comprehensive genomic profiling (CGP) is routinely recommended to detect targetable altera- tions [13, 14]. Although CGP is not routinely recommended in NENs, European Society of Medical Oncology (ESMO) recommendations include determining the tumor mutational burden (TMB) in well-to-moderately differentiated NETs [13].
To gain a better understanding of the molecular pro- files, therapeutic options, and potential differences within
the heterogeneous group of NENs, we collected real-world data from our Molecular Tumor Board (MTB) from 2017 to 2023. Although adrenocortical carcinoma (ACC) is a rare endocrine neoplasm and treatment also differs [15, 16], we included this entity in our analysis because of the high thera- peutic need and rarity. We report the retrospective analysis of CGP results and medical charts of 104 patients with rare endocrine tumors and NETs, including NET, NEC, pheo- chromocytoma, carcinoids, and ACC. Hereafter, we col- lectively refer to these tumor entities as NENs to improve readability.
2 Material and Methods
2.1 Molecular Tumor Board
The University Hospital Munich’s interdisciplinary MTB consists of clinicians, pathologists, tumor geneticists, and experts for precision oncology and takes place weekly. It is a platform to discuss patients’ CGP results and targeted treat- ment options [17]. Treatment recommendations are based on CGP results, the clinical situation and medical history of the patient, and literature research. After the interdisciplinary discussion, recommended treatments include approved tar- geted therapies, available clinical trials, and off-label treat- ments. Recommendations are annotated with the evidence level according to the ESMO Scale for Clinical Actionability of molecular Targets and the National Center for Tumor Dis- eases. The treating physician then discusses the results of the MTB, and the decision whether to follow the recommenda- tions is ultimately made by the physician and the patient.
2.2 Patient Population
In this retrospective cohort study, patients with NET, NEC, ACC, pheochromocytoma, and carcinoids who were dis- cussed in the University Hospital Munich’s MTB between May 29, 2017, and April 3, 2023, were included in the analy- sis (Fig. 1). During this period, 2587 cases of various tumor entities were discussed in the MTB. The study was approved by the local ethics committee of the Ludwig-Maximilians- University Munich (project number 21-0869). Median follow-up was 20.8 months (range 0.3-158.7). Molecular testing had been recommended beforehand, either by the entity-specific tumor board or by the coordinator of the precision oncology program, or patients already had CGP results and had been referred by external physicians for discussion.
2.3 Sequencing Assays
The various sequencing assays performed at the accredited pathology of the University Hospital Munich have been described in a previous report [17]. With the addition of broader panels and the analysis of the TMB, the number of assays for molecular testing has increased over recent years. Since December 2021, the most frequently used panel at our center has become the TruSightOncology500 assay, cover- ing 523 genes for assessment of DNA and RNA variants, TMB, and microsatellite instability [18]. Other sequenc- ing assays that were used in this analysis are Oncomine, Oncomine Comprehensive, Oncomine Comprehensive TMB, Oncomine Comprehensive Assay v3, FoundationOne, Archer FUSIONPlex, and the AmpliSeq for Illumina BRCA panel. The allele frequency threshold for inclusion in the analysis was set at 5%.
2.4 Follow-Up
We retrospectively reviewed patients’ medical charts, pathol- ogist reports, and MTB statements to collect baseline char- acteristics, treatment outcomes, results of molecular testing, and MTB treatment recommendations. We also collected whether the recommendations were put into practice and/or which other treatments the patients received after the MTB.
2.5 Statistical Analysis
We used IBM SPSS Statistics version 29.0 and Microsoft Excel version 16.84 to perform descriptive and statistical analysis and generate graphs and tables. We calculated the survival time from the date of initial diagnosis to the date of death or last contact. We used the Kaplan-Meier method to estimate survival curves and statistically compared them
using the Log-rank test. Statistical significance was deter- mined as p-values < 0.05. To evaluate the clinical benefit of targeted therapies, we calculated the progression-free sur- vival (PFS) ratio by dividing the PFS during targeted treat- ment by the PFS of the last prior systemic therapy [19]. We used a cut-off of > 1.3 as an indicator of clinical benefit, in accordance with previous studies [20, 21].
3 Results
3.1 Patient Characteristics
During the study period, 104 patients with NEN were dis- cussed in the MTB. Most patients had NECs (n = 52), fol- lowed by NETs (n = 36). The median age of all patients at initial diagnosis was 54.2 years (range 19-82), and the patients with pheochromocytoma and ACC were young- est at initial diagnosis (37.1 and 36.0 years, respectively). The most common location of the primary tumor was the pancreas (32.7%). Of all patients, 67.3% presented with synchronous metastatic disease. The median time between initial diagnosis and diagnosis of metastatic disease was 4.8 months (range 0.0-78.0), and patients were discussed in the MTB a median of 24.5 months (range 1.0-158.0) after initial diagnosis. The included patients with NEC of the lung had large-cell NEC in six cases and small-cell lung cancer in one case. Of the six patients with carcinoid tumor, five had an atypical carcinoid. Baseline characteristics of the 104 included patients are presented in Table 1. Testing was done with liquid biopsy in only two cases. In all other patients, testing was performed on tissue from the primary tumor (27.9%) or metastatic tissue (68.3%). CGP was successful in 91.3% of cases; the reason for unsuccessful testing was insufficient quality of tumor material. In some cases, testing
Patients with neuroendocrine neoplasms discussed in MTB between May ‘17 and April ‘23 n=104
n=5 CGP testing not successful
Analysis of CGP results n=99
NET n=34
NEC n=50
ACC n=7
Pheochromocytoma n=2
Carcinoid n=6
was repeated with different material, and-ultimately-CGP results from 99 patients (95.2%) were obtained.
3.2 Molecular Alterations
Of the 99 patients with technically successful CGP, a pathogenic alteration was found in 71 patients (71.7%). A median of one pathogenic alteration was detected (range 0-12). Figure 2 shows an oncoplot of the altered genes among all patients with technically successful CGP. The most commonly mutated genes in this population were TP53 (29.3%), RB1 (11.1%), and KRAS (10.1%). In total, 31.3% of tumors harbored alterations that were not pre- sent in any other case in the study population. The highest prevalence of pathogenic alterations was detected in NEC (76.9%) and carcinoids (83.3%), whereas the lowest preva- lence of pathogenic alterations was seen in ACC (37.5%). Targetable molecular alterations were found in 40.0% of NEC cases, 33.3% of NET G1 cases, 42.9% of NET G2 cases, and 52.9% of NET G3 cases.
3.3 Targeted Treatment Recommendations
Of the 99 patients with successful CGP, 35 (35.4%) received a treatment recommendation from the MTB (19 with NEC, 13 with NET, three with atypical carcinoids). Of these, ten ultimately received the recommended treatment (Table 2). The most frequently proposed treatment option was check- point inhibition in patients with either high TMB or patho- genic molecular alterations that have been described as asso- ciated with sensitivity to checkpoint inhibition [22, 23]. Four of the treated patients experienced a clinical benefit under the targeted treatment (PFS ratio > 1.3), four patients were lost to follow-up, and two patients had a PFS ratio of 0.19 and 1.14. One patient with atypical carcinoid with high TMB reached a PFS ratio of 8.16 during checkpoint inhibition and was still receiving treatment at data cut-off. The reasons that the remaining patients did not receive the recommended tar- geted treatment options included rapid deterioration of the patient or death before the recommendation could be imple- mented, refusal of cost coverage by the insurance company,
| Characteristic | All pts (n = 104) | NETª (n = 36) | NEC (n=52) | Pheochr. (n = 2) | ACC (n = 8) | Carcinoidb (n = 6) |
|---|---|---|---|---|---|---|
| Age (years) | 54.2 (19-82) | 53.5 (23-78) | 59.1 (27-82) | 37.1 (26-47) | 36.0 (19-59) | 61.9 (27-69) |
| Sex | ||||||
| Male | 60 (57.7) | 22 (61.1) | 32 (61.5) | 1 (50) | 3 (37.5) | 2 (33.3) |
| Female | 44 (42.3) | 14 (38.9) | 20 (38.5) | 1 (50) | 5 (62.5) | 4 (66.7) |
| Status at last follow up | ||||||
| Deceased | 54 (51.9) | 14 (38.9) | 32 (61.5) | 2 (100) | 4 (50) | 2 (33.3) |
| Alive | 50 (48.1) | 22 (61.1) | 20 (38.5) | – | 4 (50) | 4 (66.7) |
| Tumor stage at initial diagnosis | ||||||
| Locally limited | 34 (32.7) | 9 (25.0) | 16 (30.8) | 2 (100) | 5 (62.5) | 2 (33.3) |
| Metastatic | 70 (67.3) | 27 (75.0) | 36 (69.2) | – | 3 (37.5) | 4 (66.7) |
| Tumor stage at time of MTB | ||||||
| Locally limited | 10 (9.6) | 3 (8.3) | 7 (13.5) | – | – | – |
| Metastatic | 94 (90.4) | 33 (91.7) | 45 (86.5) | 2 (100) | 8 (100) | 6 (100) |
| Location of primary tumor | ||||||
| Adrenal glands | 10 (9.6) | – | – | 2 (100) | 8 (100) | – |
| Colorectal | 11 (10.6) | 2 (5.6) | 9 (17.3) | – | – | – |
| Lung | 12 (11.5) | – | 7 (13.5)c | – | – | 4 (66.7) |
| Pancreas | 34 (32.7) | 23 (63.9) | 11 (21.2) | – | – | – |
| Small intestine | 4 (3.8) | 3 (8.3) | 1 (1.9) | – | – | – |
| Unknown | 13 (12.5) | 4 (11.1) | 9 (17.3) | – | – | 1 (16.7) |
| Other | 20 (19.2) | 4 (11.1) | 15 (28.8) | – | – | 1 (16.7) |
Data are presented as median (range) or n (%)
ACC adrenocortical carcinoma, MTB Molecular Tumor Board, NEC neuroendocrine carcinoma, NET neuroendocrine tumor, Pheochr. Pheochro- mocytoma, pt(s) patient(s)
ªThree pts with low-grade NET, 15 pts with intermediate-grade NET, 18 pts with high-grade NET
bFive pts with atypical carcinoid, one pt with typical carcinoid
“Six pts with large-cell NEC, one pt with small-cell lung cancer
90
tumor mutational burden
80
70
60
50
40
30
20
% mutated
10
0
50,00%
0,00%
29%
TP53
11%
RB1
10%
KRAS
6%
PTEN
5%
PIK3CA
5%
CTNNB1
5%
BRAF
4%
MYC
4%
ATM
3%
MUTYH
3%
MEN1
3%
FBXW7
3%
BRCA2
3%
APC
2%
TSC2
2%
SETD2
2%
PBRM1
2%
MET
2%
HRAS
2%
DAXX
2%
CCND2
2%
CCND1
2%
BRCA1
2%
ARID1A
2%
ALK
31%
Other
Sex
Age
Primary site
Ki67%
Differentiation
male
female
>50
≤50
Pancreas
Mid GI
Lower GI
Lung
Adrenal gl.
Unknown
Other
>10
≤10
NEC
NET
Carcinoid
ACC
Pheochr.
and treating physician’s choice; however, in many cases the specific reason was not known.
3.4 Outcome
Median overall survival (mOS) across all included patients was 45.6 months (95% confidence interval [CI] 26.8-64.5). Although patients who received a recommended targeted treatment had a numerically longer mOS than patients who received a treatment recommendation but were not treated accordingly (102.0 vs. 44.6 months, respectively; Fig. 3), this difference was not statistically significant (p = 0.232). In patients with NEC, mOS was 19.3 months (95% CI 11.9-26.7). Female patients with NEC had a numerically longer mOS than male patients (43.1 vs. 18.9 months, respectively); however, this difference was also not statisti- cally significant (p = 0.156). Patients with NET had an mOS of 82.0 months (95% CI 28.1-135.9).
the upper panel; the lower panel shows a heatmap of the patients’ sex, age, primary site, Ki67%, and differentiation of the tumor. ACC adrenocortical carcinoma, Adrenal gl. adrenal glands, GI gastroin- testinal tract, NEC neuroendocrine carcinoma, NET neuroendocrine tumor, Pheochr. pheochromocytoma
4 Discussion
This retrospective cohort study analyzed patients with NET, NEC, ACC, pheochromocytoma, and carcinoids whose CGP results were discussed in the University Hospital Munich MTB. Patients with pheochromocytoma or ACC had a lower median age at diagnosis (37.1 and 36.0 years, respectively) than those in the other subgroups. As only a few patients with pheochromocytoma, ACC, or carcinoids were included in the analysis, this limits the representability of these sub- groups; however, a median age < 50 years at diagnosis has been reported previously for pheochromocytoma and ACC [24, 25].
The most common pathogenic alterations found in our cohort of patients with NENs were TP53 (29%), RB1 (11%), and KRAS (10%) mutations. A different study of patients discussed in an MTB included 114 patients with NET or NEC and also found TP53 and RB1 mutations to be com- mon, but KRAS mutations were markedly less frequent;
| Recommendationª | NEC | NET | Carcinoid |
|---|---|---|---|
| Checkpoint inhibition | |||
| ARID1A mutation | – | 2 | 1 |
| PBRM1 mutation | – | 2 | – |
| High TMB | 6 (3 pts treated, LTFU) | 2 | 1 (treated, |
| PFS ratio 8.16) | |||
| Crizotinib, cabozantinib | |||
| MET amplification | – | 1 | |
| MET mutation | 1 | ||
| Crizotinib | |||
| ROS1 mutation | 1 | ||
| BRAF inhibitor | |||
| BRAF V600E mutation | 2 (treated, PFS ratios 2.33 and 1.14) | 1 | |
| BRAF V600R mutation | – | 1 | |
| BRAF fusion | – | 1 | |
| ALK inhibitor | |||
| ALK fusion | 1 | 1 | |
| Sotorasib | |||
| KRAS G12C mutation | 1 (treated, PFS ratio 0.19) | ||
| PARP inhibitor | |||
| ATM mutation | 1 | – | |
| BRCA1 mutation | – | 2 | |
| BRCA2 mutation | 2 (1 pt treated, LTFU) | ||
| Alpelisib | |||
| PIK3CA mutation | 2 | 1 | |
| Neratinib | |||
| ERBB2 mutation | 1 | ||
| Osimertinib | |||
| EGFR mutation | 1 (treated, PFS ratio 1.43) | ||
| CDK4/6 inhibitor | |||
| CDK4 amplification | 1 | ||
| Everolimus | |||
| PTEN mutation | 1 (treated, PFS ratio 1.43) | ||
| Pralsetinib, selpercatinib | |||
| RET fusion | 1 | ||
| Erdafitinib | |||
| FGFR3 fusion | 1 | ||
| Fulvestrant after ER IHC | |||
| ESR1 mutation | 1 | ||
| Regorafenib | |||
| FLT3 amplification | 1 |
EGFR epidermal growth factor receptor, ER IHC estrogen receptor immunohistochemistry, LTFU long-term follow-up, NEC neuroendocrine carcinoma, NET neuroendocrine tumor, PFS progression-free survival, pt(s) patient(s), TMB tumor mutational burden
ªDepending on the exact MTB recommendation, either a drug class or a specific drug is indicated
instead, MEN1 mutations were more common than in our cohort [26]. The rate of cases in which no pathogenic altera- tion was identified by CGP was comparable, with 72% in our cohort and 74% in the cohort from Boilève et al. [26].
The mutational spectrum of gastroenteropancreatic NEC and NET differs from, for example, MEN1, DAXX, and ATRX mutations, which are frequently found in pancreatic NETs but rarely in gastroenteropancreatic NECs [27]. In
| Group | OS (95% CI), months | |
| Treated | 102.0 (0-232.2) | |
| Not treated | 44.6 (8.2-81.0) | |
1.0
Treated (censored)
Not-treated (censored)
0.8
Survival
0.6
0.4
0.2
0.0
0
20
40
60
80
100
120
140
No. at risk
Months after initial diagnosis
Treated
10
3
2
2
2
2
1
1
Not treated
28
15
9
4
3
2
2
our cohort, no ATRX mutations were found, whereas MEN1 mutations were found in NET and NEC, and DAXX muta- tions were only detected in pancreatic NET.
After receiving a treatment recommendation from the MTB, only 28.6% of patients (10/35) received the recom- mended targeted therapy. In most cases, the reasons for not initiating the recommended treatment were not known, but possible reasons could be deterioration of the patient’s clini- cal state before treatment initiation, patient wishes, treat- ing physician’s choice, or denial of cost reimbursement by the health insurance provider. Patients with other entities discussed in the MTB, such as pancreatic cancer (3.2%), cholangiocellular carcinoma (19.4%), cancer of unknown primary (14%), and gynecological cancers including cer- vical, vaginal, and vulvar cancer (7%), received recom- mended targeted treatments even less frequently, possibly due to a more aggressive tumor biology and therefore faster clinical deterioration of patients [28-31]. Boilève et al. [26] described an implementation rate of 35% in patients with NET or NEC, which is slightly higher than in our patient cohort. Patients treated according to MTB recommendations in the cohort from Boilève et al. [26] mostly had NETs, whereas most of the patients in our cohort who received the targeted treatment had NECs; however, the proportion
of all NECs in the cohort was higher in our analysis. In the future, the implementation rate could possibly be improved upon with the implementation of standardized follow-up of patients after MTB.
Encouragingly, four of the ten patients who received the recommended targeted treatment benefitted in terms of the PFS ratio. ESMO recommendations include testing of TMB in well-to-moderately differentiated NETs [13]; however, in our cohort, high TMB was detected in six patients with NEC and one patient with atypical carcinoid, who ultimately benefitted by far the most, with a PFS ratio of 8.16 during checkpoint inhibition. However, ESMO recommendations also include testing for tumor-agnostic alterations, includ- ing TMB, in patients with metastatic cancers when access to matched therapies is available [13, 14], and our study highlights the relevance of this. Neither patients with ACC nor those with pheochromocytoma received treatment rec- ommendations from our MTB. This may be due to the low patient numbers in our cohort but emphasizes the need for ongoing research to identify targeted treatment options for these rare tumor entities.
Patients who received a recommended targeted treatment had a numerically longer mOS than those who received a treatment recommendation but were not treated accordingly;
however, this difference was not statistically significant, and larger patient numbers would most likely be needed to show possible significant differences in survival. Nevertheless, considering the survival curves and the PFS ratios, CGP followed by MTB discussion can lead to meaningful clinical benefit in a few selected patients with NENs.
As this was a retrospective single-center study, this analysis has limitations. The inclusion of NET, NEC, ACC, pheochromocytoma, and carcinoids means that the analyzed patients represent a heterogeneous cohort with partially very small subgroups that can only be examined descriptively. Furthermore, given the low implementation rate of the rec- ommended treatments, many patients need to receive CGP testing and subsequent MTB discussion for some to ulti- mately benefit. As described in previous publications from our center, the structured follow-up of patients discussed in the MTB is currently being improved upon and will facilitate future analysis, especially of patient outcomes [17].
5 Conclusion
This analysis showed that CGP can be a useful addition to current treatment options for certain patients with NENs. It shows the need for concepts to improve the implementation rate of treatments recommended after MTB, for example with structured follow-up, support with the application pro- cess for cost reimbursement from health insurance provid- ers, and close communication with clinical trial units. Since the overall survival of patients with NET is relatively long, especially compared with patients with the more aggressive NECs, these patients often receive multiple therapy lines and would likely benefit from more targeted treatment options. However, as our and others’ analyses have shown, pathologi- cal alterations in NENs are very diverse, which complicates the development of targeted treatments suitable for a broader range of patients.
Declarations
Funding Open Access funding enabled and organized by Projekt DEAL. This research received no specific funding, and no funding was provided for publication of this article.
Conflicts of Interest KD has received honoraria from AstraZeneca and support for travel, accommodation, and expenses from Servier, GSK, Bristol Myers Squibb, and AstraZeneca. MK has received speaker honoraria and travel support from Recordati, Eisai, and Lilly. DZ has received travel support from AstraZeneca, Amgen, and BMS and hon- oraria from AstraZeneca. LW has received honoraria from Roche and Servier and support for travel from Amgen and Merck. SB has received honoraria for scientific presentations from Celgene, Servier, and MSD; fees for consultancy or advisory roles from Celgene, Servier, Incyte,
Janssen-Cilag, AstraZeneca, MSD, and Bristol Myers Squibb; and support for travel, accommodation, and expenses from Lilly. MvB has received research support from and serves on the speakers’ bureau for Gilead, Miltenyi Biotec, Merck Sharp & Dohme, Roche, Mologen, Novartis, Astellas, and Bristol Myers Squibb. VH has received re- search funding paid to his institution from Merck, Amgen, and Roche; fees for consultancy and advisory roles from Merck, Amgen, Roche, MSD, Bristol Myers Squibb, MSD Oncology, Novartis, Pierre Fabre, TERUMO, GSK, Servier/Pfizer, AstraZeneca, Oncosil, and Nordic Bioscience; honoraria from Roche, Amgen, Sanofi, Merck, Servier, Pfizer, Pierre Fabre, AstraZeneca, MSD, and Seagen; and support for travel, accommodation, and expenses from Merck. CBW has received honoraria from Amgen, Bayer, BMS, Chugai, Celgene, Falk, GSK, MSD, Merck, Janssen, Ipsen, Roche, Servier, SIRTeX, and Taiho; has served on advisory boards for Bayer, BMS, Celgene, Janssen, MSD, Servier, Shire/Baxalta, Rafael Pharmaceuticals, RedHill, and Roche; has received travel support from Bayer, Celgene, Janssen, RedHill, Roche, Servier, and Taiho; and has received research grants (institu- tional) from Roche. CBW serves as an officer for ESMO, Deutsche Krebshilfe, and Arbeitsgemeinschaft internistische Onkologie and is a member of the EU Commission expert group: Mission Board for cancer. KH has received Honoraria from Amgen, BMS, Lilly, Merck, Roche, Taiho, Servier and streamedup!, fees for consulting or advisory role from Amgen, Servier, MSD (Institutional), Merck and Janssen; has received travel support from Amgen, Merck and Servier. CJA, CSp, RS, SN, MR, CSc, MA, JW, CS-T, JR, MZ, TK, JK, AJ, FK and AT, have no conflicts of interest that might be relevant to the contents of this manuscript.
Ethics Approval This retrospective chart review study involving human participants was undertaken in accordance with the principles of the Declaration of Helsinki and its later amendments. The study was approved by the local ethics committee of the Ludwig-Maximilians- University Munich (project number 21-0869).
Consent to Participate Not applicable.
Consent for Publication Not applicable.
Data Availability The datasets generated and analyzed during the cur- rent study are available from the corresponding author upon reasonable request.
Code Availability Not applicable.
Author Contributions All authors contributed to the study conception and design. Klara Dorman and Kathrin Heinrich prepared the material collected and analyzed the data. Klara Dorman wrote the first draft of the manuscript, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Open Access This article is licensed under a Creative Commons Attri- bution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Com- mons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regula- tion or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/.
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Authors and Affiliations
Klara Dorman 1,2 . Christoph J. Auernhammer3,11 . Christine Spitzweg3,11 . Ralf Schmidmaier3,12 . Svenja Nolting3,14.
Matthias Kroiss3,12 . Martin Reincke3,12 . Christian Schulz4,11 . Martin Angele5,11 . Jens Werner5,11 .
Christine Schmid-Tannwald6,11,12 . Josefine Rauch7,11 . Mathias Zacher8,11 . Thomas Knosel2,9,11 . Jörg Kumbrink2,9. Andreas Jung2,9 . Frederick Klauschen2,9 . Amanda Tufman13 . Danmei Zhang1,2 . Lena Weiss1 . Stefan Boeck1,2,10 . Michael von Bergwelt-Baildon 1,2,11 . Volker Heinemann1,2 . C. Benedikt Westphalen 1,2,11 . Kathrin Heinrich1,2,11 [D
☒ Kathrin Heinrich Kathrin.heinrich@med.uni-muenchen.de
1 Department of Medicine III and Comprehensive Cancer Center, LMU University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany
2 German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
3 Department of Medicine IV, LMU University Hospital, LMU Munich, Munich, Germany
4 Department of Medicine II, LMU University Hospital, LMU Munich, Munich, Germany
5 Department of General, Visceral and Transplant Surgery, LMU University Hospital, LMU Munich, Munich, Germany
6 Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany
7 Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
8 Department of Nuclear Medicine, LMU University Hospital, LMU Munich, Munich, Germany
9 Institute of Pathology, Faculty of Medicine, LMU Munich, Munich, Germany
10 Department of Hematology and Oncology, Munich Municipal Hospital Neuperlach, Munich, Germany
11 Interdisciplinary Center of Neuroendocrine Tumors of the GastroEnteroPancreatic System (GEPNET-KUM, ENETS certified Center of Excellence), LMU University Hospital, LMU Munich, Munich, Germany
12 Interdisciplinary Center of Endocrine Tumors (ZET), LMU University Hospital, LMU Munich, Munich, Germany
13 Department of Medicine V, LMU University Hospital, LMU Munich, Munich, Germany
14 Department of Endocrinology, Diabetes and Clinical Nutrition, University Hospital Zurich, USZ, UZH, Zurich, Switzerland