Differential expression of somatostatin receptor (SSTR) subtypes across a spectrum of neuroendocrine neoplasms (NENs).
Authors: Lou Emil , Gandhi Nishant , Farrell Alex , Xiu Joanne , Seeber Andreas , Beg Shaalan Shaalan , Monu Minnu , Darabi Sourat , Demeure Michael J. , Abraham Jim , Oberley Matthew James , Marshall John , Soares Heloisa P. 
Published: June 2022

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Background: Targeted therapy of NENs based on the presence of SSTRs fills a unique niche in tumor biology and clinical treatment of patients with solid tumors. SSTRs have multiple isoforms and are collectively expressed in the majority of NENs. However, subtypes are still not routinely tested and thus not assessed for clinical decision-making, especially for patients meriting consideration of targeted radionucleotide therapy. Clarifying the landscape of SSTR subtypes using molecular techniques more sensitive than immunohistochemistry (IHC)-the standard of testing, and identifying associated genomic biomarkers that differ between them, will pave the way for more sophisticated decision-making in the future. Additionally, leveraging transcriptomics to better assess mitotic markers such as Ki-67 to assess tumor grade, would increase diagnostic accuracy. Here we provide initial validation across a spectrum of NENs. Methods:1595 NENs were analyzed using Next Generation Sequencing (592 gene panel, NextSeq), Whole Exome and Transcriptome Sequencing (NovaSeq), and IHC at Caris Life Sciences (Phoenix, AZ). Significance was determined using chi-square, Fisher-Exact or Mann-Whitney U and p-adjusted for multiple comparisons (q<0.05) where applicable. Results: In a subset of 492 NENs with accompanying tumor grading information, a median MKI67 (gene encoding Ki-67) TPM value of 2.27 for low-grade (LG-), and 38.7 for high-grade NENs (HG-NENs) was observed (q<0.05). Using ROC curve analysis, a threshold of MKI67 expression (13.4375 TPM) differentiated LG- from HG-NENs, with a true positive rate of 86.84%, a false positive rate of 11.9% and an AUC of 95% and was subsequently applied to the entire cohort to infer HG/LG. Compared to HG-NENs (n = 862), LG-NENs(n = 733) expressed higher levels of SSTR 1(3.5-fold),2 (2.9-fold) and 5 (1.67-fold) and lower levels of SSTR4 (0.28-fold)(q<0.05). Further, the expression of SSTRs 3 and 4 in HG-NEN (rs= 0.63) and SSTRs 1 and 2 in LG-NENs (rs= 0.64) were positively correlated. Overall, the prevalence of altered TP53, RB1, PIK3CA, APC, KRAS was higher and MEN1 was lower in HG-vs LG-NENs (q<0.05). For each SSTR subtype, we established high and low cohorts based on median expressions. In LG-NENs, increased alterations in TP53 and RB1 were associated with increased expression of SSTRs 1 and 2 and reduced expression of SSTRs 3 and 4. In HG-NENs, increased alterations in APC were associated with increased expression of SSTR 1 and 4 and reduced expression of SSTRs 3 and 5. Additional subtype- and grade-specific alterations were also observed. Conclusions: This study provides evidence that WTS and NGS can be leveraged to predict grade of NENs and define characteristic differences in the genomic landscape across SSTR subtypes in HG and LG NENs. Incorporating the molecular profiling of NENs can thus aid in advancing the development of more tailored therapeutic strategies.