Free Neuropathology 5:6 (2024)

Letter

Searching for a cut-off point for p53 immunohistochemistry as evidence of TP53 mutations

Ilay Caliskan¹, Rufei Lu¹, Cristine Szu Lyn Ding^1,2, Francineide Sadala de Souza^1,3, Arie Perry¹, Tarik Tihan¹

1. Department of Pathology, Neuropathology Division, UCSF Medical Center, San Francisco, CA 94143, USA
2. Current Address: Division of Anatomical Pathology, Changi General Hospital, Singapore, 529889, Singapore
3. Current Address: Associação das Pioneiras Sociais, Patologia Cirúrgica, Asa Sul – Brasília 70335901, DF - Brazil, Brazil

Corresponding author:
Tarik Tihan · Neuropathology Division · Room M551 · Department of Pathology · University of California San Francisco · 505 Parnassus Avenue · San Francisco · CA 94143 · USA
tarik.tihan@ucsf.edu

Submitted: 23 January 2024
Accepted: 29 February 2024
Copyedited by: Jerry Lou
Published: 12 March 2024

https://doi.org/10.17879/freeneuropathology-2024-5337

Keywords: p53, Immunohistochemistry, TP53 mutation, Glioma

Numerous publications and revised grading strategies reiterate the relevance of TP53 gene mutations for adult diffuse gliomas^1–3. These studies identify TP53 mutation as a frequent alteration in IDH-mutant astrocytomas along with other critical genes, such as IDH1, IDH2, and ATRX. The 2021 WHO classification of Central Nervous System (CNS)⁴ designates "strong" nuclear expression of p53 protein in greater than 10 % of tumor cells, which implies the presence of TP53 mutations⁴ as a desirable criterion for IDH-mutant astrocytomas. Other studies have also recognized similar percentages in adult diffuse gliomas as evidence of TP53 mutations. To incorporate TP53 alterations into the diagnostic work-up, these studies used immunohistochemistry as a viable alternative to provide the type and grade of diffuse astrocytomas⁵, and others recommended p53 protein immunohistochemistry as a reliable means of predicting TP53 mutations^6,7,8. While a few studies report an association with treatment response, most studies do not find TP53 mutation status or positive immunohistochemical staining helpful in determining biological behavior or type of adult diffuse glioma⁸.

Contrary to the studies that expressed enthusiasm for p53 immunohistochemistry as a predictive marker for TP53 mutational status⁷, studies as far back as 25 years ago pointed to the discordance between p53 protein expression and TP53 mutations⁹. It was clear from these earlier studies that most gliomas with focal p53 nuclear immunopositivity do not harbor TP53 mutations⁹. Certain alterations in the TP53 gene, such as exon 4 mutations or missense mutations, are more likely to lead to strong positive staining¹⁰, while truncating mutations (e.g., nonsense, splice site, or frameshift) are associated with absence or barely detectable p53 protein immunopositivity ("null phenotype")¹¹.

Even among the few studies that propose p53 staining as a practical prognostic marker, there is little agreement on the optimal cut-off value. Some studies presuming TP53 mutations based on p53 immunostaining simply used "positive" staining while others did not report definitions for p53 positivity, further confounding the literature^11–13. One of the most recent comparative studies claimed 10 % positivity as a cut-off point to predict TP53 mutation based on receiver operating characteristic curve (ROC) analysis¹², quoted by the WHO 2021 classification⁴.

To determine a practical value for p53 immunostaining in everyday neuropathology practice and to validate whether a 10 % cut-off can be reliably used to suggest TP53 mutation (as suggested by WHO 2021), we analyzed a group of diffuse gliomas for p53 staining and TP53 mutations. We identified all adult diffuse gliomas and diffuse midline gliomas for which analysis of p53 immunohistochemistry and TP53 mutation was performed during a five-year period. The inclusion criteria were: 1) diagnosis of adult diffuse glioma or diffuse midline glioma between 2015 and 2020, 2) available p53 (DO-7 clone) immunohistochemistry staining, and 3) available analysis with next generation sequence analysis (NGS), referred to as "UCSF500 NGS" panel¹⁴, which includes a comprehensive assessment of all TP53 coding exons and most other genes known to be recurrently altered in CNS tumors¹⁵. Clinical and radiological features were obtained from electronic patient records. All cases were re-analyzed for p53 protein labeling index (LI) to ensure uniform assessment based on strong nuclear staining (equal to or greater than the intensity of positive control tissue) of p53 protein in tumor cells (Figure 1). The positive control tissue was validated by UCSF500 NGS for TP53 mutation at the UCSF Histology and Clinical Cancer Genomics Laboratory. The p53 LIs were determined by the available original reported LI, which was achieved by capturing a digital image of the area with highest labeling and counting at least 500 cells in the captured image, followed by the re-assessment of the image by two of the authors independently to estimate the LI within a 5 % maximum variance. Genetic alterations involving the tumor and germline were determined as previously described^15,16. Statistical analyses were performed using R version 4.2.2. A receiver operator characteristic curve (ROC) was analyzed using cutpointr R package (version 1.1.1). Violin plot and pie chart were generated using ggplot2 R package (version 3.3.5). The study was approved by UCSF Committee for Human Research (UCSF CHR 10-01252).

Our study group was composed of 99 patients with an average age of 44.5 years (range 1-81 years). There were 49 female and 50 male patients. Tumor types and locations are presented in Table 1. Seventy-six (76) of the samples were from primary tumors (first resection) while 23 were from recurrent tumors. Two glioblastomas had earlier biopsies at another hospital without additional treatment before the patient was transferred to our institution. Seventy-two (72) of the primary tumors were CNS WHO grade 4, six were CNS WHO grade 3, and twenty-one tumors were CNS WHO grade 2. There were 52 TP53-mutant and 47 TP53-wildtype tumors and the distribution of these mutations among tumor types is presented in Table 1. Forty-two (42) tumors had missense mutations, while three others had missense combined with frameshift mutations. There were 3 tumors with frameshift mutation-only (Figure 2B).

The median p53 immunohistochemistry labeling index for tumors harboring TP53 mutation was 60 % (Range: 0-95 %) with an interquartile range (IQR) of 66.25 %, while the median value for those without TP53 mutations was 10 % with an IQR of 10.5 % (Range: 0-30 %). When 42 tumors with missense mutations were considered, the median labeling index was calculated as 77.5 % (mean 64.24 %), and the median labeling index for cases with frameshift mutations was 12.5 % (mean 21 %). However, there was a wide range of labeling indices for missense (1-95 %) mutations. Of note, out of 11 tumors with absent p53 staining, 6 had TP53 mutations (two frameshift, two missense, one splice site mutation, and one structural variant).

When the cut-off values for p53 immunohistochemistry were considered, the positive predictive value of 10 % labeling index was calculated as 60.4 %, while the negative predictive value at the same cut-off point was 70.6 %. An ROC analysis based on the 99 patients demonstrated that strong p53 staining in more than 40 % of tumor cells provided the most accurate prediction of mutation based on Youden index (Figure 2). Using this cutoff value, positive predictive value (PPV), negative predictive value (NPV), sensitivity, and specificity were 100 %, 73.6 %, 67 %, and 100 %, respectively. Most of the inaccuracy could be attributed to tumors with frameshift mutations, which frequently demonstrated the null cell pattern with absent immunostaining (with p53 LI of <1 %). After excluding the tumor with frameshift mutations, an ROC analysis redemonstrated that the best cutoff for p53 LI as still 40 % with PPV, NPV, sensitivity, and specificity calculated as 100 %, 81 %, 88 %, and 100 %, respectively.

These findings suggest, as previously demonstrated by many, that p53 immunostaining cannot be taken as prima facia evidence of TP53 mutations. Furthermore, our data suggest that the cut-off value recommended by the CNS WHO classification to predict TP53 mutations may be inaccurate and should be reevaluated. This issue underscores the problem of accepting results of single studies without appropriate validation and confirmation by more than one independent scientific observation for the purposes of standardized classification systems that would be used worldwide.

We believe that previously suggested p53 labeling index of 10 % as predictive of TP53 mutation significantly overestimates mutations, which could potentially lead to erroneous conclusions in the absence of molecular confirmation. Considering possible variations when performed in different laboratories and using different antibodies, this cut-off value becomes even less predictive. For practical purposes, we recommend the use of >40 % as a more robust and predictive cut-off value for p53 immunohistochemistry, while still acknowledging that there will always be outliers regardless of the cut-off number utilized. Finally, the absence of staining does not rule out a TP53 mutation, and thus should be interpreted with caution.

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