p53 Immunohistochemistry Patterns Are Surrogate Biomarkers for TP53 Mutations in Gastrointestinal Neuroendocrine Neoplasms

Abstract

Aims: The aim of this study was to establish p53 immunohistochemistry (IHC) patterns to predict TP53 mutations in gastrointestinal neuroendocrine neoplasms (GI-NENs) and to determine whether p53 IHC patterns could be used for the differential diagnosis of neuroendocrine neoplasms.

Methods: TP53 gene sequencing and p53 IHC were performed on formalin-fixed paraffin-embedded (FFPE) tissue samples from 92 patients diagnosed with GI-NENs from five medical centers.

Results: The cohort included 35 well-differentiated neuroendocrine tumors and 57 poorly differentiated neuroendocrine carcinomas. Gene sequencing revealed 38 wild-type TP53 and 54 TP53 mutations. p53 expression was interpreted as follows: pattern A, p53 was absent from all tumor cells; pattern B, scattered and weak p53 expression in 1-20% of tumor cells; and pattern C was subclassified as pattern C1: variable p53 staining intensity in 21-60% of tumor cells and tumor cell nests with focal strong positive p53 staining and pattern C2: strong p53 staining in more than 60% of tumor cells. p53 IHC patterns were evaluated as a binary classifier where pattern B predicted wild-type TP53, and patterns A and C predicted TP53 mutations. The sensitivity, specificity, and overall accuracy of this binary classification to predict TP53 status were 0.963, 0.868, and 0.924, respectively. p53 IHC patterns were also correlated with TP53 mutation types. Most cases with pattern A harboured loss-of-function (LOF) mutations, whereas patterns B and C tended to indicate wild-type TP53 and gain-of-function (GOF) mutations, respectively. Furthermore, most of the well-differentiated NETs showed pattern B, whereas pattern C2 was more common in poorly differentiated NECs. Finally, staining interpretation between different observers also yielded high reproducibility.

Conclusions: p53 IHC patterns may be used as predictors of TP53 gene mutations and therefore could be potential surrogate markers for TP53 mutations in GI-NENs and could distinguish between well-differentiated NETs and poorly differentiated NECs.

Figure 1

The “lollipop” plot generated by the MutationMapper tool of cBioPortal [31, 32] shows the open box of TP53 gene, as well as the frequency and position of TP53 mutations in GI-NENs in our study. AD: transactivation domain; DBD: DNA binding domain; MD: tetramerization domain.

Figure 2

Different patterns of p53 expression in GI-NENs. (a) Pattern A showed completely absent p53 expression of tumor cell nucleus. Note that scattered stromal cells were weakly positive for p53 (40x). (b) Pattern B showed p53 expression in 1%-20% of tumor cell nuclei with weak intensity (40x). (c) Pattern C1 showed a variable staining intensity with focal tumor cell nests demonstrating strong positivity (40x). (d) Pattern C2 was accompanied by diffuse (61%-100%) and strong nuclear staining (40x).

Figure 3

The relationship between histological subtypes and p53 IHC pattern in GI-NENs. (a, b) A case of well-differentiated NET with wild-type TP53 and pattern B of p53 expression (40x). (c, d) A poorly differentiated NEC case with TP53 missense mutation and pattern C2 (40x).

Figure 4

The Pearson analysis showed a strong correlation between the percentages of p53 immunoreactive cells by different pathologists (R = 0.99, p < 0.001).