Background: Observations play a pivotal role in the progress of science, including in pathology. The cause of a disease such as cancer is analyzed by breaking it down into smaller organs, tissues, cells, and molecules. The current standard cancer diagnostic procedure, microscopic observation, relies on preserved morphological characteristics. In contrast, molecular analyses explore oncogenic pathway activation that leads to genetic mutations and aberrant protein expression. Such molecular analyses could potentially identify therapeutic targets and has gained considerable attention in clinical oncology. Summary: This review summarizes the cardinal biomarkers of the p53 pathway, p53, p16, and mouse double minute 2 (MDM2), in the context of traditional surgical pathology and emerging genomic oncology. The p53 pathway, which is dysregulated in more than a half of all cancers, can be applied in several diagnostic settings. A four-classification model of immunophenotype for p53 pathway gene status, tumor types with a high frequency of abnormalities for each p53 pathway gene, and a minimal p53 pathway immunohistochemical panel is also described. Key Messages: Immunohistochemistry of oncogenic signals should be interpreted according to molecular findings based on genomic oncology, in addition to the microscopic findings of diagnostic pathology.

What is the basis for pathological diagnosis? In the case of tumors, the site of origin and histological type are essential, corresponding to macroscopic and microscopic information, respectively. Basically, the latest tumor classification defined by the anatomic site is selected first. Afterward, the histological type is selected from the cellular phenotype. However, such diagnostic methods are sometimes impotent in small specimens with an unknown primary cancer or no clinical information. Drawing explainable conclusions from microscopic findings in such exceptional situations requires a clear diagnostic rationale or algorithm that goes beyond the tacit knowledge of personal experience or sense.

Molecular characteristics, as determined by biomarkers, are emerging as the third fundamental element of tumor classification. Biomarkers are used for disease risk stratification, differential diagnosis, prediction of therapeutic efficacy and toxicity, predictive prognosis, and monitoring [1]. Genetic biomarkers have already been incorporated into pathological diagnosis in some cancers, and tissue agonistic markers, such as NTRK fusion and/or tumor mutation burden, are becoming widespread [2]. These cross-tumor biomarkers will undoubtedly become essential for tumor diagnosis and therapeutic decision making.

Upon speculating the universal biomarkers that could incorporate the diagnosis of tumor pathology, the author focused on the p53 pathway, which is one of the major cancer signaling pathways. First, this review briefly summarizes two different diagnostic approaches to biomarkers, cancer genome profiling test using next-generation sequencing (NGS) and in situ validation, such as immunohistochemistry. Subsequently, the author summarizes the accumulating data on representative p53 pathway genes, TP53, CDKN2A, and MDM2, and discusses how to exploit such biomarkers in several practical diagnostic settings.

Recently, genomic analysis using NGS has been applied in clinical oncology to detect abnormalities in cancer-related genes comprehensively [3]. The main objective of the cancer genome profiling tests is to detect actionable alterations associated with the molecular treatment of metastatic solid tumors. While it may be clinically sufficient to pick out the actionable ones from a dispersed set of mutations, the comprehensive profiling of cancer includes nonactionable driver events and, in some cases, genetic variants of unknown significance.

Comprehensive genomic cancer profiling studies are being conducted by international scientific consortia in at least three phases. The Cancer Genome Atlas (TCGA) Research Network reported genomic information and analysis of more than 30 cancer types, comprising both common and rare types, over the decade beginning in the late 2000s [4]. In addition, the Pan-Cancer Atlas (PCA) Project explored three main topics, cell-of-origin patterns [5], oncogenic processes [6], and signaling pathways [7]. Furthermore, the Pan-Cancer Analysis of Whole Genome (PCAWG) project analyzed the whole genomes of cancers to elucidate the unclear aspects of noncoding DNA sequences [8]. Integration of such findings could facilitate tumor classification, as large-scale data linking pathology and cancer genomics would enhance our understanding of the specific roles of cancer-related genes and the interrelationships among them.

Regarding diagnostics, genotype-based molecular classification has been reported to be less discriminative than phenotype-based classification for 12 major cancer types [9] or 33 pan-types [5]. Cancer harbors approximately four coding point mutations under positive selection [10]. Conversely, the majority of individual cancer-related genes have only low- (<2%) or intermediate- (2−20%) frequency mutation rates in pan-cancer cohorts [11]. Currently, conventional histological analysis remains the mainstay of cancer diagnosis, and because of the scarcity of data, genomic variation analysis is the next best thing.

However, when considering the significance of mutated genes coexisting in cancer, cell biological signaling pathways yield insights into cancer genetics. According to the signaling pathway analysis of the PCA project [7], the canonical oncogenic pathways are classified into 10 groups: p53, Wnt/β-catenin, receptor tyrosine kinase (RTK)-RAS, Notch, Hippo, transforming growth factor β (TGFβ), Myc, Nrf2, PI-3-kinase/AKT (PI3K), and cell cycle pathways. Among them, abnormalities of the p53 pathway have been detected in more than a half of the tumors and are mainly caused by the TP53 and CDKN2A genes. These p53 pathway genes are almost always included in NGS analyses and provide clues that facilitate the comprehensive understanding of the co-occurrence and mutual exclusivity of the genetic variants and their relationships with cancer type.

Immunohistochemistry can also detect abnormalities in oncogenic pathways by targeting biomarkers derived from cancer-related genes [12]. Unlike NGS, clinical immunohistochemistry is basically a singleplex assay for evaluating protein expression. However, the greatest advantage is the opportunity to analyze biomarker expression at the cellular level, revealing spatial information about the tumor and its microenvironment.

The immunohistochemistry results, which can be evaluated either qualitatively or quantitatively, can be classified into four staining patterns in the case of the p53 protein [13]. These are called overexpressed, null, ectopic, and reactive immunophenotypes and allow for the evaluation of other biomarkers (Fig. 1).

Fig. 1.

Four immunophenotypes in markers for oncogenic signal, with p53 as an example. a, b Overexpression pattern. Ovarian high-grade serous carcinoma cells (a) show diffuse p53 positivity in the nucleus (b). c, d Null pattern. Converse, to the overexpression pattern, uterine serous carcinoma cells (c) are completely absent of nuclear expression of p53 (d). e, f Ectopic pattern. Another uterine serous carcinoma (e) expressing p53 in the cytoplasm as well as in the nucleus (f). g, h Reactive pattern. Heterogeneous p53 expression in the ovarian serous borderline tumor cells (g) is similar with that of adjacent normal cells (h). a, c, e, g With H&E staining. b, d, f, h With p53 immunostaining.

Fig. 1.

Four immunophenotypes in markers for oncogenic signal, with p53 as an example. a, b Overexpression pattern. Ovarian high-grade serous carcinoma cells (a) show diffuse p53 positivity in the nucleus (b). c, d Null pattern. Converse, to the overexpression pattern, uterine serous carcinoma cells (c) are completely absent of nuclear expression of p53 (d). e, f Ectopic pattern. Another uterine serous carcinoma (e) expressing p53 in the cytoplasm as well as in the nucleus (f). g, h Reactive pattern. Heterogeneous p53 expression in the ovarian serous borderline tumor cells (g) is similar with that of adjacent normal cells (h). a, c, e, g With H&E staining. b, d, f, h With p53 immunostaining.

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First, overexpression immunophenotype is a diffuse positive expression found in tumor cells (Fig. 1a, b). Interestingly, the signal intensity of this pattern is often more substantial than that of a reactive pattern. This diffuse and strong expression pattern is interpreted as an activated oncogenic signal, probably due to gain-of-function mutation and/or copy number amplification. Second, the null immunophenotype refers to diffuse negative expression and, ideally, no expression in tumor cells (Fig. 1c, d). This pattern typically indicates the loss of function of tumor suppressor genes, nonsense mutation, or copy number loss. Third, ectopic immunophenotype is a positive signal detected in an unusual location (Fig. 1e, f). This dysregulated signal arises from genetic alternations, including gene rearrangement. These three immunophenotypes easily distinguish neoplastic lesions from non-neoplastic populations.

In contrast, reactive immunophenotype means heterogeneity of positive cell ratio and signal intensity (Fig. 1g, h). This heterogeneous pattern is interpreted as mostly normal or nonspecific because it suggests controlled protein expression. Reactive immunophenotype refers to intact genotype (i.e., wild-type pattern) of the corresponding gene but fails to deny clonality of the cell population of interest, except for a few cases or cancer types. The concept of the four immunophenotypes is essential for interpretation of the immunohistochemistry result. Table 1 summarizes the tumor-associated molecules in this review, their representative antibody clones, and the immunophenotypes derived from them [14‒17].

Table 1.

Interpretation of immunophenotypes present in this review

GeneProductRepresentative clonesRef.Overexpression/diffuse positiveNull/diffuse negativeEctopicReactive/partially positive
TP53 p53 DO-1, DO-7 [14TP53 mutation TP53 mutation TP53 mutation Normal 
CDKN2A p16 E6H4 [15HPV-associated, cellular senescence Loss of p16 Not applicable Normal 
MTAP MTAP 2G4 [16Normal Loss of 9p21 Not applicable Likely abnormal 
MDM2 MDM2 IF2 [17MDM2 amplification Normal Not applicable Likely MDM2 amplification 
GeneProductRepresentative clonesRef.Overexpression/diffuse positiveNull/diffuse negativeEctopicReactive/partially positive
TP53 p53 DO-1, DO-7 [14TP53 mutation TP53 mutation TP53 mutation Normal 
CDKN2A p16 E6H4 [15HPV-associated, cellular senescence Loss of p16 Not applicable Normal 
MTAP MTAP 2G4 [16Normal Loss of 9p21 Not applicable Likely abnormal 
MDM2 MDM2 IF2 [17MDM2 amplification Normal Not applicable Likely MDM2 amplification 

HPV, high-risk human papillomavirus; Ref., references.

Similar to immunohistochemistry, in situ hybridization (ISH), which targets nucleic acid sequences, is a biomarker test that preserves cancer spatial information [18]. One type of ISH uses fluorophores (i.e., FISH), which can detect structural variants of the genome that are difficult to detect by short-read NGS, including gene amplification, deep deletions, and translocations; nevertheless, it can detect only one known structural variant per assay. Therefore, NGS, immunohistochemistry, and FISH should each be used depending on the situation.

A counter-cellular stress response, p53 pathway, consists of the principal transcriptional factor p53, its target genes, and several critical p53 regulators, including p16 and MDM2 (mouse double minute2 homolog) [7] (Fig. 2). This pathway responds to cell stress by activating various cellular signaling activities in the context and interacting with other oncogenic signaling classes, such as cell cycle pathway [19]. As a member of the DNA damage checkpoint, p53 blocks G1/S cell cycle regulation by expressing p21 encoded by CDKN1A [20].

Fig. 2.

Signaling by p53 pathway and cell cycle pathway. Arrows indicate promotion. Left tack means inhibition. The dashed line separates the p53 pathway (upper left) from the cell cycle pathway (lower right).

Fig. 2.

Signaling by p53 pathway and cell cycle pathway. Arrows indicate promotion. Left tack means inhibition. The dashed line separates the p53 pathway (upper left) from the cell cycle pathway (lower right).

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Based on the finding that stress induces cell aging, the p53 pathway can also be interpreted as a series of responses related to senescence. The triad of cellular senescence is cell cycle arrest, apoptosis resistance, and altered transcription [21]. Depending on the cellular context, these phenotypes can inhibit or promote oncogenesis. While the “quiescent cell-like” state is the opposite of a proliferative lesion, evasion of apoptosis is one of the hallmarks of cancer. Stresses that induce senescence include oncogene aberrations, DNA damage, telomere evasion, the narrowly defined p53 pathway, and the retinoblastoma pathway mediated by RB transcriptional corepressor 1 (RB1) at G1/S checkpoint in the cell cycle that is involved in the regulation of cellular senescence [22].

The TP53 gene, which encodes for p53, was initially mapped to chromosome 17p13 in 1986 [23]. The alias “guardian of the genome” was named by David Lane, who identified p53 through his work on the SV40 virus [24]. Germline variants of TP53 are associated with a cancer-predisposing condition known as Li-Fraumeni syndrome [25].

TP53, TP63 [26], and TP73 [27] were identified as members of the TP53/p53 family based on sequence similarity in the DNA-binding domain [28]. The two p53 homologs are associated with cancer, but their roles in the maintenance and development of germline and somatic cells have been investigated. Two p63 protein isoforms encoded by TP63, full-length TAp63α, and shortened ΔNp63, also called p40 [29], are associated with proliferative stratified epithelial cells and are therefore used as immunohistochemical markers, particularly for lung squamous cell carcinoma and basal cell/myoepithelial markers [30]. Conversely, p73 is essential for ciliogenesis [31]; however, p73-related molecules have not been used in diagnostic pathology as cell differentiation markers.

TP53 mutations are crucial to cancer in various aspects. Epidemiologically, since half of the advanced cancers carry TP53 mutations [32], it is practical to classify cancers into two populations according to the mutation status. Limited to specific cancer types, TP53 mutations may be a prognostic marker [33], probably due to differences in cellular context [34]. Genomically, TP53-mutant cancers are associated with copy number alterations due to genomic instability, distinguished from somatic mutation-driven cancers [35]. These copy number alterations include whole-genome doubling [36] and chromothripsis [37]. Morphologically, polyploid giant cancer cells [38] and multipolar mitosis [39] are possible clues of TP53 mutation.

TP53 mutation frequency is not uniform across tumors but depends on histological types and/or anatomical locations (Table 2) [7, 40‒66]. While TP53 mutations are often considered late events, as in adenoma-carcinoma sequence of conventional colon cancer [67], they can also occur early in certain tumor subtypes [68]. Such TP53 mutation-driven cancers are high-grade serous carcinoma (HGSC) [69], inflammatory bowel disease-associated colorectal cancer [70], esophageal cancer [71], and vulvar cancer [72]. These cancers naturally have a high frequency of TP53 mutations and already have the mutations in their non-invasive cancer stage. In addition, extensive molecular genetics and pathological investigations have showed that TP53 mutations are present even in seemingly normal cell populations, including p53 signature [73] and clonal hematopoiesis [74]. Identifying minimal TP53-mutated clones represents a novel potential strategy for preventing, diagnosing, and treating TP53 mutation-driven cancers.

Table 2.

Stratification of tumors by the frequency of TP53 mutation

Probability group (PG)TP53 mutation probabilityTumor types (TP53 mutation frequency with references)
PG1: Almost certainly 90%–100% Uterine carcinosarcoma (91% [7]), IDH-mutated low-grade glioma (92% [7]), lung large cell neuroendocrine carcinoma (92% [40]), stomach and esophageal squamous cell carcinoma (94% [7]), high-grade serous carcinoma (95% [7]), lung small cell carcinoma (98% [41]) 
 80%–89% HPV-negative head and neck squamous cell carcinoma (81% [7]), lung squamous cell carcinoma (84% [7]) 
PG2: Likely 70%–79% Stomach and esophageal cancer with CIN (71% [7]) 
 60%–69% Oral squamous cell carcinoma (60% [42]), skin basal cell carcinoma (61% [43]), pancreatic ductal adenocarcinoma (66% [7]), ampullary carcinoma (66% [44]), myxofibrosarcoma/undifferentiated pleomorphic sarcoma (66% [7]), leiomyosarcoma (69% [7]) 
PG3: Unpredictable 50%–59% Lung adenocarcinoma (50% [7]), urothelial bladder carcinoma (50% [7]), colorectal carcinoma (52% [45]), POLE-mutated stomach and esophageal cancer (56% [7]) 
 40%–49% Invasive ductal breast cancer (44% [46]), gall bladder carcinoma (47% [47]), desmoplastic melanoma (48% [48]) 
PG4: Likely not 30%–39% Stomach and esophageal cancer with MSI (31% [7]), hepatocellular carcinoma (32% [7]), chromophobe renal cell carcinoma (32% [7]), glioblastoma (35% [7]) 
 20%–29% Adrenocortical carcinoma (20% [7]), angiosarcoma (20% [49]), endometrial carcinoma (29% [50]) 
 10%–19% HPV-positive head and neck squamous cell carcinoma (10% [7]), cholangiocarcinoma (11% [7]), natural killer NK/T-cell lymphoma (12% [51]), malignant peripheral nerve sheath tumor (13% [52]), Wilms tumor (14% [53]), genomically stable stomach and esophageal cancer (14% [7]), IDH-wild-type low-grade glioma (14% [7]), diffuse large B-cell lymphoma (14% [7]), prostate adenocarcinoma (16% [7]), cutaneous melanoma (17% [7]), pleural mesothelioma (17% [7]), adult T-cell leukemia/lymphoma (18% [54]), cervical adenocarcinoma (19% [7]) 
PG5: Almost not 0%–9% Uveal melanoma (0% [7]), Grade I–II meningioma (0% [55]), schwannoma (0% [56]), papillary thyroid carcinoma (1% [7]), T-lineage acute lymphoblastic leukemia (1% [57]), testicular germ cell tumor (1−2% [7]), hindbrain ependymoma (2% [58]), pheochromocytoma and paraganglioma (2% [7]), papillary renal cell carcinoma (2% [7]), clear cell renal cell carcinoma (3% [7]), pancreatic neuroendocrine tumor (3% [59]), ameloblastoma (4% [60]), IDH-mutated low-grade glioma with 1p/19q co-deletion (4% [7]), thymoma (4% [7]), dedifferentiated liposarcoma (4% [7]), breast fibroepithelial tumor (4% [61]), adenoid cystic carcinoma (5% [62]), medulloblastoma (5% [63]), chronic lymphocytic leukemia (5% [64]), EBV-positive stomach and esophageal cancer (7% [7]), cervical squamous carcinoma (7% [7]), invasive lobular breast carcinoma (8% [46]), MDS and MDS/MPN (8% [65]), multiple myeloma (8% [66]), acute myeloid leukemia (9% [7]) 
Probability group (PG)TP53 mutation probabilityTumor types (TP53 mutation frequency with references)
PG1: Almost certainly 90%–100% Uterine carcinosarcoma (91% [7]), IDH-mutated low-grade glioma (92% [7]), lung large cell neuroendocrine carcinoma (92% [40]), stomach and esophageal squamous cell carcinoma (94% [7]), high-grade serous carcinoma (95% [7]), lung small cell carcinoma (98% [41]) 
 80%–89% HPV-negative head and neck squamous cell carcinoma (81% [7]), lung squamous cell carcinoma (84% [7]) 
PG2: Likely 70%–79% Stomach and esophageal cancer with CIN (71% [7]) 
 60%–69% Oral squamous cell carcinoma (60% [42]), skin basal cell carcinoma (61% [43]), pancreatic ductal adenocarcinoma (66% [7]), ampullary carcinoma (66% [44]), myxofibrosarcoma/undifferentiated pleomorphic sarcoma (66% [7]), leiomyosarcoma (69% [7]) 
PG3: Unpredictable 50%–59% Lung adenocarcinoma (50% [7]), urothelial bladder carcinoma (50% [7]), colorectal carcinoma (52% [45]), POLE-mutated stomach and esophageal cancer (56% [7]) 
 40%–49% Invasive ductal breast cancer (44% [46]), gall bladder carcinoma (47% [47]), desmoplastic melanoma (48% [48]) 
PG4: Likely not 30%–39% Stomach and esophageal cancer with MSI (31% [7]), hepatocellular carcinoma (32% [7]), chromophobe renal cell carcinoma (32% [7]), glioblastoma (35% [7]) 
 20%–29% Adrenocortical carcinoma (20% [7]), angiosarcoma (20% [49]), endometrial carcinoma (29% [50]) 
 10%–19% HPV-positive head and neck squamous cell carcinoma (10% [7]), cholangiocarcinoma (11% [7]), natural killer NK/T-cell lymphoma (12% [51]), malignant peripheral nerve sheath tumor (13% [52]), Wilms tumor (14% [53]), genomically stable stomach and esophageal cancer (14% [7]), IDH-wild-type low-grade glioma (14% [7]), diffuse large B-cell lymphoma (14% [7]), prostate adenocarcinoma (16% [7]), cutaneous melanoma (17% [7]), pleural mesothelioma (17% [7]), adult T-cell leukemia/lymphoma (18% [54]), cervical adenocarcinoma (19% [7]) 
PG5: Almost not 0%–9% Uveal melanoma (0% [7]), Grade I–II meningioma (0% [55]), schwannoma (0% [56]), papillary thyroid carcinoma (1% [7]), T-lineage acute lymphoblastic leukemia (1% [57]), testicular germ cell tumor (1−2% [7]), hindbrain ependymoma (2% [58]), pheochromocytoma and paraganglioma (2% [7]), papillary renal cell carcinoma (2% [7]), clear cell renal cell carcinoma (3% [7]), pancreatic neuroendocrine tumor (3% [59]), ameloblastoma (4% [60]), IDH-mutated low-grade glioma with 1p/19q co-deletion (4% [7]), thymoma (4% [7]), dedifferentiated liposarcoma (4% [7]), breast fibroepithelial tumor (4% [61]), adenoid cystic carcinoma (5% [62]), medulloblastoma (5% [63]), chronic lymphocytic leukemia (5% [64]), EBV-positive stomach and esophageal cancer (7% [7]), cervical squamous carcinoma (7% [7]), invasive lobular breast carcinoma (8% [46]), MDS and MDS/MPN (8% [65]), multiple myeloma (8% [66]), acute myeloid leukemia (9% [7]) 

CIN, chromosomal instability; HPV, high-risk human papillomavirus; MDS, myelodysplastic syndrome; MDS/MPN, myelodysplastic/myeloproliferative neoplasms; MSI, microsatellite instability.

TP53 mutations can be visualized by immunostaining and can be interpreted in the four patterns as described in Fig. 1. Typically, the antibodies used in practice are DO-1 or DO-7, which recognize the N-terminal transactivation domain [14]. Analysis of female adnexal HGSCs with high TP53 mutations showed that p53 overexpression and null and cytoplasmic (ectopic) immunophenotypes are mainly associated with missense, frameshift, and nuclear localization signaling domain mutations, respectively [13]. Conversely, reactive p53 is typically wild-type TP53 but is also rarely associated with splicing site mutations or truncating mutations. Köbel et al. observed that the p53 immunophenotype of most HGSCs is associated with the predicted TP53 mutation pattern; however, in approximately 9% of cases, there is a discordance between genotype and immunophenotype [13]. While indels and splicing mutations in the DNA-binding domain rarely exhibit overexpression patterns, the majority of nonsense and splicing mutations are associated with null patterns. Nevertheless, immunostaining for p53 is a helpful tool for estimating the presence or absence of TP53 mutations and the type of mutation.

Specific tumors are challenging to interpret with the p53 immunophenotype; therefore, other criteria should be used. First, even p53-positive cell rates as low as >10% in gliomas are associated with TP53 mutations [75]. Second, a precursor lesion of acute myeloid leukemia [76], myelodysplastic syndrome (MDS) with 5q deletion, is significantly associated with TP53 mutation [77]. In MDS, those detected with TP53 mutations express p53 in 40% of cells. p53 expression in more than 0.5% of cells has a poor prognosis [78]. Third, cutaneous malignant melanomas occasionally have increased or aberrant p53 expression [79]. The aberrant expression does not inevitably correlate with TP53 mutations [80], and the expression of wild-type p53 may favor melanoma [81]. Interestingly, the Spitz tumor, a distinctive nevus cell neoplasm, has been reported to have a TP53-NTRK1 fusion gene [82], but its p53 immunotypes are not yet evident.

Apart from p53 immunostaining, there have been attempts to evaluate the copy number of chromosome 17 by FISH, mainly in hematologic neoplasms [83]. Biallelic inactivation of the tumor suppressor gene is considered significant; however, in chronic lymphocytic leukemia, even a monoallelic TP53 mutation or deletion is clinically important. The deletion of 17p by FISH must be confirmed in at least 20% of cells [84], whereas it is deemed remarkable that at least 10% of variant allele frequency is detected by NGS [85]. In contrast, most TP53-mutated solid cancers harbor other TP53 inactivations, such as loss of heterozygosity and decreased wild-type TP53 transcription [86]. Therefore, immunohistochemistry alone is sufficient for solid cancers to evaluate p53 abnormalities, but NGS and FISH can point to specific sequences and structural variants of the cancer genome.

The human genetic locus at 9p21 [87], CDKN2A, encodes two distinct proteins, p16 INhibitor of CDK4 (INK4A) [88] and p14-Alternative Reading Frame (ARF) [89]. The tumor suppressor, p16, is a gatekeeper of G1 phase, which inhibits cyclin D-dependent kinase (CDK4/CDK6) that phosphorylates RB1 [88, 90]. In contrast, p14 increases p53 activity through MDM2 suppression [91]. Interestingly, p16-RB1 signaling also regulates p53 through p14 expression [89]. This two-faced tumor suppressor gene involves p53 and the cell cycle pathways p14-MDM2-p53 signaling and p16-cyclin D-CDK4/CDK6-RB1 signaling. The hereditary cancer disease related to this tumor suppressor gene is familial atypical multiple mole melanoma syndromes (FAMMM) [92].

CDKN2A is the second cancer suppressor gene found to be abnormal after TP53, and dysfunction of p16 is one of the most frequent events in cancer. As such, attempts to visualize p16 by immunohistochemistry are already widespread [15]. Based on the molecular mechanism, the abnormal patterns of the p16 immunophenotype can be classified into three categories: p16 loss due to genetic and epigenetic abnormalities, p16 overexpression associated with persistent high-risk human papillomavirus (HPV) infection, and p16 overexpression reflecting cellular senescence due to dysregulation of several oncogenes or TP53.

Loss of p16 is associated with deletion of the genetic locus 9p21 [93], methylation of the promoter region [94], and genetic mutations associated with loss of function [95]. Accordingly, the p16-null immunophenotype can be interpreted as a loss of tumor suppressor function (Table 3) [96‒100]. Instead, since CDKN2A function is due to decreased copy number rather than genetic mutation, FISH helps to diagnose mesotheliomas and melanomas in which the CDKN2A locus is frequently lost [101, 102]. Furthermore, loss of CDKN2A locus and p16 expression may be negative prognostic factors in the case of gliomas [103, 104]. Similarly, p16 silencing by promoter hypermethylation is frequently observed in a variety of cancers. Although p16 is one of the classical CpG island methylator phenotype markers for colorectal cancer [105], such epigenomic abnormalities cannot be detected on histological sections.

Table 3.

Aberrant immunophenotypes associated with specific tumor types

Targeting signalTumor types with references
p53 See Table 2 
p16 Null: pancreatic adenocarcinoma [97], pancreatic adenosquamous carcinoma [99], IPMN [97], ampullary adenocarcinoma [97], biliary carcinoma [97], malignant melanoma [96], pleural mesothelioma [98], peritoneal mesothelioma [98], glioma (not otherwise specified) [100
Overexpression: HPV-related cancers (head and neck squamous cell carcinoma, uterine cervical cancer, vulvar squamous cell carcinoma, anal and perianal squamous cell carcinoma) [106], endometrial polyp [107], atypical polypoid adenomyoma [108], liposarcoma [109], leiomyosarcoma [109], rhabdomyosarcoma [109], chondrosarcoma [109], bile duct adenoma [110], intrahepatic cholangiocarcinoma, small duct type [110], so-called high-grade serous carcinoma [109], adenoid cystic carcinoma [111], noninvasive papillary urothelial carcinoma [109], various high-grade neuroendocrine carcinoma [109], mesothelioma [109
MTAP Null: pancreatic adenocarcinoma [97], PanIN [112], ampullary adenocarcinoma [97], biliary carcinoma [97], malignant melanoma [113], malignant mesothelioma [16], glioma (not otherwise specified) [100
MDM2/MDM2 Well-differentiated and dedifferentiated liposarcomas [17], low-grade central osteosarcoma [114], dedifferentiated osteosarcoma [114], parosteal osteosarcoma [114], GLI1-associated sarcoma [115], uterine adenosarcoma [116], malignant Leydig cell tumor [117
Targeting signalTumor types with references
p53 See Table 2 
p16 Null: pancreatic adenocarcinoma [97], pancreatic adenosquamous carcinoma [99], IPMN [97], ampullary adenocarcinoma [97], biliary carcinoma [97], malignant melanoma [96], pleural mesothelioma [98], peritoneal mesothelioma [98], glioma (not otherwise specified) [100
Overexpression: HPV-related cancers (head and neck squamous cell carcinoma, uterine cervical cancer, vulvar squamous cell carcinoma, anal and perianal squamous cell carcinoma) [106], endometrial polyp [107], atypical polypoid adenomyoma [108], liposarcoma [109], leiomyosarcoma [109], rhabdomyosarcoma [109], chondrosarcoma [109], bile duct adenoma [110], intrahepatic cholangiocarcinoma, small duct type [110], so-called high-grade serous carcinoma [109], adenoid cystic carcinoma [111], noninvasive papillary urothelial carcinoma [109], various high-grade neuroendocrine carcinoma [109], mesothelioma [109
MTAP Null: pancreatic adenocarcinoma [97], PanIN [112], ampullary adenocarcinoma [97], biliary carcinoma [97], malignant melanoma [113], malignant mesothelioma [16], glioma (not otherwise specified) [100
MDM2/MDM2 Well-differentiated and dedifferentiated liposarcomas [17], low-grade central osteosarcoma [114], dedifferentiated osteosarcoma [114], parosteal osteosarcoma [114], GLI1-associated sarcoma [115], uterine adenosarcoma [116], malignant Leydig cell tumor [117

HPV, high-risk human papillomavirus; IPMN, intraductal papillary mucinous neoplasms; PanIN, pancreatic intraepithelial neoplasia.

Because the 9p21 locus contains the CDKN2A, CDKN2B, and MTAP genes, deletion could result in multiple co-occurring genetic abnormalities. CDKN2B encodes a CDK4/CDK6 inhibitor, p15 INK4B, regulated by TGFβ signaling [118]. However, p15 has not been used as an immunohistological marker in clinical pathology. In contrast, 5′-deoxy-5′-methylthioadenosine phosphorylase (MTAP) [119] has been used as a surrogate marker for p16 loss because of indirect evidence of 9p21 deletion [112]. Therefore, MTAP-deficient cell populations in pancreas [97, 112], melanocytes [113], mesothelium [16], and central nervous system [100] can be interpreted as CDKN2A-depleted neoplastic clones (Table 3); however, some of the reduced MTAP expressions may be related to other molecular mechanisms, such as methylation of the promoter region [113].

Another immunophenotype, p16 overexpression, which appears atypical for a tumor suppressor gene, is a surrogate marker for HPV. Cancers associated with HPV, such as type 16 and type 18, have different molecular characteristics than HPV-negative cancers [120‒123]. The HPV oncoprotein E6 induces p53 degradation via the ubiquitin-proteasome system, whereas E7 inactivates or induces degradation of RB1 protein through complex formation [106]. These two oncogenic signals synergistically disrupt the cell cycle, and p16 overexpression can be explained by a negative feedback mechanism due to abnormal E7-RB1 interaction [124]. Thus, the HPV-associated cancers exhibit few TP53 and CDKN2A mutations [123]. In addition, these cancers exhibit p16 diffuse nuclear and cytoplasmic expression [125‒128], also called block positive [129], except for a minor discordance between p16 and HPV mRNA expression status [130]. Conversely, cancers without HPV in these anatomical sites are typically associated with alterations of the TP53 and CDKN2A genes. In summary, the dualistic tumor classification by HPV is underpinned by differences in the molecular mechanisms, leading to the p53 pathway dysregulation.

Although p16 overexpression is a surrogate marker of HPV, it is also a cell senescence marker. Senescent cells seldom proliferate; instead, they evade cell death and alter their surrounding environment mainly by secreting various pro-cytokines and chemokines [131]. Such cell characteristics are recognized as secretory phenotype (SASP), and p16, along with β-galactosidase [132], is used as a marker to recognize senescence. In neoplastic lesions, overexpression of p16 could be interpreted as oncogene-induced or tumor suppressor loss-induced senescence caused by abnormalities in HRAS, KRAS, BRAF, TP53, PTEN, RB1, and PTEN [22]. Specifically, p16-expressing lesions without HPV include reactive-like lesions, including endometrial polyp [107], atypical polypoid adenomyoma [108], and bile duct lesions [110], as well as malignant tumors [109], such as adenoid cystic carcinoma [111] (Table 3). To determine whether p16 overexpression is related to HPV or other cellular senescence, a minimal p53 immunohistochemical panel including p16, p53 [133], and a cell proliferation marker Ki-67 [134] represents an optimized immunohistochemical analysis (Table 4). Suppressed cell proliferation and abnormal p53 immunophenotype may serve as clues to senescence.

Table 4.

Minimal immunohistochemical panel of p53 pathway

p53 statusp16 statusMost likely interpretationRelated diagnostic workups
Aberrant Overexpression TP53-mutated tumors with cellular senescence Not applicable 
Aberrant Null TP53-mutated tumors with loss-of-function CDKN2A MTAP immunohistochemistry, CDKN2A FISH 
Aberrant Reactive TP53-mutated tumors, rule out precursor, and reactive lesions Ki-67 immunohistochemistry, cancer gene test 
Reactive Overexpression HPV-related tumors or TP53-wild-type tumors with cellular senescence HPV genotyping 
Reactive Null TP53-wild-type tumors with loss-of-function CDKN2A MTAP immunohistochemistry, CDKN2A FISH 
Reactive Reactive Intact p53 pathway-like cell population, rule out reactive lesions Ki-67 IHC, cancer gene test 
p53 statusp16 statusMost likely interpretationRelated diagnostic workups
Aberrant Overexpression TP53-mutated tumors with cellular senescence Not applicable 
Aberrant Null TP53-mutated tumors with loss-of-function CDKN2A MTAP immunohistochemistry, CDKN2A FISH 
Aberrant Reactive TP53-mutated tumors, rule out precursor, and reactive lesions Ki-67 immunohistochemistry, cancer gene test 
Reactive Overexpression HPV-related tumors or TP53-wild-type tumors with cellular senescence HPV genotyping 
Reactive Null TP53-wild-type tumors with loss-of-function CDKN2A MTAP immunohistochemistry, CDKN2A FISH 
Reactive Reactive Intact p53 pathway-like cell population, rule out reactive lesions Ki-67 IHC, cancer gene test 

HPV, high-risk human papillomavirus.

Aberrant means overexpression, null, and ectopic. For more details, see Figure 1 and Table 1.

MDM2, encoded by MDM2 at 12q15, is an E3 ubiquitin ligase that negatively regulates p53 protein via proteasome degradation [135, 136]. “Double Minute” [137] means a circular extrachromosomal DNA fragment that does not have telomeres or centromeres and is replicated during cell division [138]. As MDM2 abnormalities in cancer are predominantly due to amplification, the significance of its mutations is limited. Since an association between single nucleotide polymorphism, “SNP309” in the promoter region of the MDM2 gene, with tumorigenesis [139] has been suggested, this genotype can be interpreted as an MDM2-related hereditary cancer syndrome. However, the variant in the noncoding region is still challenging to apply to practical pathology.

MDM2 is one of 24 recurrent amplified oncogenes in human cancers and favors circular amplification [140]. MDM2 amplification averages approximately 4% across cancer types but is very frequent in highly differentiated and dedifferentiated liposarcomas [8, 141, 142]. Amplification of the 12q locus is triggered by genomic events called chromothripsis [143]. The extrachromosomal DNA pattern is concordant with the fact that liposarcomas do not tend to be accompanied by amplification of the 12q arm level [141]. Notably, the 12q13-15 region contains other cancer-related genes: a G1/S cell cycle regulator called CDK4 [144] and a member of zinc finger protein family, GLI1 [115]. The vast and complex MDM2 and/or CDK4 amplification structural variants, also called tyfonas [145], are associated with dedifferentiated liposarcomas.

MDM2 amplification can be used mostly for the pathological diagnosis of sarcomas. Again, MDM2 amplification is frequent in well-differentiated and dedifferentiated liposarcomas and can be a definitive diagnosis if consistent with the histology [17]. It is also helpful in diagnosing tumors that are difficult to diagnose, such as low-grade central osteosarcoma [114], dedifferentiated osteosarcoma [114], parosteal osteosarcoma [114], GLI1-associated sarcoma [115], uterine adenosarcoma [116], and malignant Leydig cell tumor [117] (Table 3). Even though MDM2-reactive immunophenotypes have diagnostic significance [17], copy number evaluation of MDM2 by FISH is superior for practical diagnostic work in adipocytic tumors [146].

In practical pathology, immunohistochemistry for tumor-related proteins is applied in four main situations: determining neoplastic/dysplastic or not, confirming a specific tumor type, annotating miscellaneous and unclassifiable tumors, and verifying genomic information about the cancer.

To Determine Neoplastic/Dysplastic or Not

To demonstrate that a particular cell population is clonal, some diagnostic strategies use immunostaining to detect at least one abnormal oncogenic signal. If malignancy is suspected, p53 immunostaining is most appropriate, since approximately a half of the pan-cancer cohort also suffers TP53 abnormalities [32]. Even in such cases, identifying an abnormal p53 pattern, especially in small specimens or microscopic lesions, should not be immediately regarded as malignant. p53 abnormalities may be found in posttherapeutic responsive lesions [147], normal-looking clonal expansions, and precancerous lesions with TP53 abnormalities [73, 74, 148]. Simultaneous Ki-67 immunostaining is recommended to confirm proliferating cell distribution and avoid overdiagnosis (Table 4).

In order to predict the results of p53 immunostaining, Table 2 summarizes TP53 mutation rates stratified by histological types. If a tumor is assumed to be in a very high frequency group of mutations (PG1, >90%), a p53-reactive pattern would be an opportunity to reconsider the diagnosis. Conversely, if a tumor is expected to be in a very low frequency TP53-mutation group (PG5, <10%), searching for other histological type-specific markers may prove better. Among the tumors with an intermediate to below-average frequency of TP53 mutations (PG4, 10%–39%) are those that are occasionally difficult to diagnose morphologically, i.e., melanoma, mesothelioma, and adrenocortical carcinoma. In conclusion, the high prevalence of TP53 mutations (50%) in the pan-cancer cohort also suggests that TP53 mutations alone are insignificant in determining the histological type.

To Confirm a Specific Tumor Type

Table 3 summarizes the association between tumor-related proteins and the histological types with a relatively high frequency (although some are modest) of their aberrant expression. Oncogenic signals other than p53 are useful for definitive diagnosis and subtyping of tumors, requiring additional testing based on anatomic site, cell type, clinical information, and other contextual factors. In parallel, co-occurrence and mutual exclusivity of cancer genes should be considered for cost-effective additional testing. For example, consistent with the molecular mechanism, MDM2 amplification is significantly mutually exclusive with TP53 mutations [7], and MDM2-driven tumors rarely require immunohistochemistry for p53. In addition, MDM2 amplification tends to coexist with CDKN2A mutations [7], possibly explained by p53 pathway-independent functions of ARF [149]. This combination of individual biomarkers would further refine tumor classification.

To Annotate Miscellaneous and Unclassifiable Tumors

Contrary to the subsection title, diagnostic strategies using an immunohistochemical panel rich in cancer-related proteins are not recommended for the following reasons. As mentioned, majority of the proteins are not worth investigating considering the mutational frequency of cancer-related genes in the pan-cancer cohort. Even if several alterations are detected from the biomarker tests, pan-tumor molecular classification using them has not been established so far.

The proposed minimal p53 pathway immunohistochemistry panel, outlined in this review, is primarily intended as a diagnostic tool for pathologists in achieving a simplified molecular classification of cancers (Table 4). In terms of therapeutic strategies, immunohistochemistry can play a clinically remarkable role as a test for tumor-agnostic biomarkers, enabling the rapid identification of actionable driver events in tumors. However, it should be noted that currently, p53 pathway components are not applicable to these biomarkers.

To Validate Consistency with Genomic Information

In situ validation assays, such as immunostaining and FISH, are effective in confirming the results of cancer genome-profiling tests because they are proficient in detecting known abnormalities. The signals visualized by these tests provide a spatial distribution of tumor cells not available in genomic testing and can be applied to detect microscopic residual or recurrent lesions after treatment. Combining genomic profiles with morphological images will allow us to track tumor changes over time and better understand cancer progression. In particular, p53 is the most frequently defective cancer-related protein; thus, it is useful for tracking clones when TP53 mutations are found.

Even in the coming era of biomarker-based clinical oncology, traditional tumor pathology is essential because it provides contextual readouts of cancer. Such information includes precise cancer cell distribution, cell fraction, and coexisting noncancerous cells; they are interpretable as TNM stage, tumor purity, and tumor microenvironment. In contrast, molecular analysis reveals background about cancer cells from a different perspective: cancer-related gene mutation, genomic structural variants, transcriptional and proteomic network frameworks, and the prerequisite epigenetic landscape. Therefore, it is necessary to establish a pathological diagnostic system that incorporates implemented biomarker tests and to revise the anatomical site-specific classification from a cross-tumor perspective. Such hierarchical classification of anatomic sites, cell types, and biomarkers, as in the concept of the human reference atlas [150], would require the status of canonical oncogenic signaling, such as the p53 pathway, in addition to tumor agnostic biomarkers.

Y.H. received honoraria for lecturing from AstraZeneca.

There is no support/funding relevant to this review.

Y.H. conceptualized the topics of the review, prepared all figures and tables, and wrote the manuscript.

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