Skip to main content
Download PDF

Whole-exome sequencing identifies distinct genomic aberrations in eccrine porocarcinomas and poromas

Orphanet Journal of Rare Diseases volume 20, Article number: 70 (2025) Cite this article

Abstract

Background

Eccrine porocarcinoma (EPC) is a rare malignant skin tumor arising from the eccrine gland. Investigations into the genomic landscape of EPC have uncovered potential drivers of its development and progression. However, there is limited information on the discrepancies between EPC and its benign counterpart, eccrine poroma (EP).

Methods

Formalin-fixed paraffin-embedded (FFPE) samples from 15 EPCs and 5 EPs were retrieved from Helsinki Biobank and Finnish Clinical Biobank Tampere. One EPC was found to be digital papillary adenocarcinoma in review of diagnoses. Whole-exome sequencing was used to conduct a comprehensive analysis to elucidate the genomic features of EPCs and EPs.

Results

There was general heterogeneity within EPCs and EPs, with discrepancies such as exclusive TP53, NCOR1, and CDKN2A mutations in EPCs and a higher mutational load in EPCs than in EPs. Furthermore, we identified alterations in pathways associated with cell adhesion and the extracellular matrix in EPCs, while pathways associated with ketone body and amino acid metabolism were altered in EPs. The MAPK and Ras signaling pathways were enriched in genes mutated only in EPCs.

Conclusions

EPCs and EPs are generally heterogeneous tumor entities with a few distinct discrepancies from each other. The findings from this study emphasize the need to further verify the roles of disrupted genes and pathways in the initiation and progression of EPCs and EPs.

Background

Eccrine porocarcinoma (EPC) is a rare malignant skin tumor arising from the acrosyringium, the intraepidermal spiral duct of the eccrine gland [1]. The presentation of EPC and EP is highly variable, making diagnosis challenging. EPC and EP typically appear as solitary erythematous, skin-colored, or violaceous nodules or papules, which may be ulcerated or prone to bleeding [2,3,4]. EPC mostly affects people in the seventh to ninth decades [5, 6]. The benign counterpart of EPC, eccrine poroma (EP), usually affects middle-aged to elderly people [3]. Although the etiology of EPC is still not fully understood, chronic immunodeficiency may be a risk factor [5]. Approximately 18–50% of EPCs originate from EP [7,8,9], with chronic ultraviolet exposure and immunosuppression hypothesized to play a role in malignant transformation [6]. From 2007 to 2017, the age-standardized rate of EPCs was 0.06 and 0.04 per 100 000 person-years for men and women in Finland, respectively [2]. The corresponding rate was 0.25 per 100 000 person-years from 2013 to 2018 in the United Kingdom [10] and 0.05 per 100 000 person-years from 2000 to 2018 in the USA [11]. Although the histopathologic features of EPC are varied, some common characteristics include poromatous basaloid cells, nuclear atypia, increased mitotic activity, and necrosis. Additionally, the tumor cells frequently show ductal differentiation [12]. Although the histopathologic features of EP are varied, common features include the presence of poroid or cuticular cells [3].

The primary treatment for EP and early-stage EPC is surgical resection. Mohs micrographic surgery is increasingly used due to its lower recurrence rate compared with conventional wide local excision [12]. Currently, there are no standardized therapy regimens for patients with EPC after surgery. Metastasis is associated with a high mortality rate [7], necessitating investigations for novel therapeutic targets.

Knowledge on the genomic landscape of EPC and EP has recently increased due to accumulating research findings. Involvement of TP53 tumor suppressor has been suggested since Harms et al. reported recurrent mutations of TP53 in EPCs [13]. Furthermore, a high mutation frequency of TP53 in EPCs has been reported in multiple publications [14,15,16,17,18]. Bosic et al. reported TP53 mutations exclusively in EPCs [14]. TP53, a well-known tumor suppressor gene, has been implicated in more than 50% of all cancers [19]. TP53 exhibits a multifaceted role as a cell-cycle regulator, apoptosis inducer, transcription factor, and transcription repressor [20]. Other notable candidate driver genes in EPCs include EGFR [13, 18, 21], CDKN2A [13, 14, 16,17,18, 21], and PIK3CA [22, 23]. EGFR encodes the epidermal growth factor receptor, which functions as an intrinsic tyrosine kinase. Aberrations in EGFR are implicated in various cancers, with amplification being the most common. This has been reported in glioblastoma multiforme [24], breast carcinomas [25], non-small cell lung cancers [26], and colorectal cancers [27]. Loss of the tumor suppressor gene CDKN2A is a critical event in the tumorigenesis and progression of various cancer types, including melanoma [28,29,30]. The encoded protein p16 acts as an inducer of cell cycle arrest in G1 and G2 phases by inhibiting CDK4/6 [31, 32], while p14 functions as a stabilizer of p53 [33]. The phosphatidylinositol 3-kinase (PI3K) signaling pathway plays a crucial role in regulating cell growth, proliferation, motility, angiogenesis, and survival [22]. Mutations in PIK3CA are frequently observed in various cancers, particularly in breast cancer [34, 35]. HRAS mutations have been reported at similar frequencies in both EPCs and EPs [13, 14]. RAS proteins regulate proliferation, differentiation, cell adhesion, and cell migration through activation of the mitogen-activated protein (MAP) kinase pathway [36]. There is also a growing interest in fusion genes following the recent discovery of cell-transforming YAP-MAML2 and YAP1-NUTM1 fusions and fusions involving the PAK2 gene in EPCs and EPs [37]. Genomic instability resulting from changes in the homologous recombination repair pathway has also been implicated [17]. Multiple studies have reported a high overall mutation rate attributed to UV-induced mutational signatures [13, 14, 17, 18]. However, there is a lack of studies investigating possible driver mutations that exclusively define the development of the malignancy or malignant transformation from EP to EPC at the genomic level.

In the present study, we sought to describe the genetic features of EPCs and EPs using whole-exome sequencing (WES). To our knowledge, this is the first WES study with multiple EPCs and EPs.

Methods

Patient samples

Following approval from the institutional ethics committee of Helsinki University Central Hospital [HUS/358/2018], formalin-fixed paraffin-embedded (FFPE) samples from 13 EPCs and 5 EPs diagnosed in 1987–2013, along with corresponding clinical data, were collected from the Helsinki biobank. Two of the EPC tissue samples were recurrences from the former excision; the others were primary tumors. DNA was extracted from the FFPE samples. Additionally, two EPC FFPE samples were collected from the Finnish Clinical Biobank Tampere [BB2018-006]. The diagnoses of 15 EPCs and 5 EPs were reviewed by an experienced pathologist (Tom Böhling). One sample collected as an EPC was found to be a digital papillary adenocarcinoma (DPA). The DPA sample was included in our study due to its rarity and the prospect of enriching our study with novel insights.

DNA extraction from FFPE samples

For DNA extraction, two or three 1-mm core punches were taken from the tumor area of the FFPE blocks with an automated tissue microarrayer (TMA Grand Master, 3DHISTECH Ltd., Budapest, Hungary). Genomic DNA was extracted using a QIAsymphony DSP DNA mini kit (catalog number 937236, Qiagen GmbH, Hilden, Germany) and a QIAsymphony instrument following the manufacturer’s protocols.

Sample selection for exome sequencing

The mean concentration of DNA samples collected was 28.4 ng/µl (range 0–98.9 ng/µl). One EPC DNA sample was excluded from WES due to low DNA yield.

Whole-exome sequencing and bioinformatics

DNA samples from 13 EPCs, 5 EPs, and one DPA were subjected to WES at the sequencing unit of the Institute of Molecular Medicine Finland. A total of 50 ng of DNA per sample was processed according to the Twist Human Core Exome EF Multiplex Complete kit manual (catalog number 100252, Twist Bioscience, San Francisco, CA, USA). 4 µl of 15 µM unique dual-index oligos with unique molecular barcodes (Integrated DNA Technologies, Coralville, IA, USA) were used for ligation. Library quantification and quality check were performed using LabChip GX Touch HT High Sensitivity assay (catalog number CLS760672, Revvity, Waltham, MA, USA). Libraries were pooled into 7- to 8-plex reactions according to concentration. Exome enrichment was performed using Twist Comprehensive Exome probes. The captured library pools were quantified for sequencing using KAPA Library Quantification Kit (catalog number KK4824, KAPA Biosystems, Wilmington, MA, USA) and LabChip GX Touch HT High Sensitivity assay. WES was performed with an Illumina NovaSeq system using a S4 flow cell with lane divider (Illumina, San Diego, CA, USA) and v1.5 chemistry. The read length for the paired-end run was 2 × 151 bp.

The protein-coding mutations were filtered through the following steps: the WES samples were analyzed with Illumina Dragen v3.8.4 using the GRCh38 reference genome. Reads were trimmed and sample analysis including variant calling was performed with tumor-only options. A comprehensive description of Dragen is available in a recent study [38]. After analysis, the called variants were annotated using Annovar [39]. Variants were further filtered by applying the following criteria: (1) variants were within the target; (2) somatic variants in Dragen evaluation; (3) unless splice variants are in question, variants were not synonymous; (4) variants were not outside of genes or within introns; (5) population frequency in gnomAD or in the gnomAD European (Finnish) population ≤ 0.05; (6) only PASS mutations; (7) REVEL [40] ≥ 0.5 for nonsynonymous single nucleotide variants (SNVs); and (8) “NA” (not applicable) for variants in genes with no REVEL values, stop-gains, insertions, and deletions, as REVEL does not apply a value for these variants. All variants from each sample that passed the filtering were provided in a text file format uploaded to Zenodo (DOI: https://doiorg.publicaciones.saludcastillayleon.es/https://doiorg.publicaciones.saludcastillayleon.es/10.5281/zenodo.10701230). The codes used in this study are available on GitHub (https://github.com/Rare-Cancers-Research-Group/Porocarcinoma).

Mutational signature analysis

SigneR R package was used to determine the appropriate number of signatures that sufficiently represent our dataset [41]. Based on the analysis, the optimal number of signatures was 5 (see Additional file 1). Five de novo mutational signatures were extracted from a 96-class mutational context using the “decipherMutationalProcesses” function of MutSignatures R package [42].

The de novo signatures were then matched with COSMIC signatures v.3.3 [43] using the “matchSignatures” function of MutSignatures R package. All COSMIC signatures with a proposed etiology of “unknown” with no other annotations or “possible sequencing artefacts” were excluded from the matching analysis.

The samples were then subjected to clustering based on the signature components using ClustVis 2.0 [44].

Genetic overview of genes with previously reported mutations in EPC and EP

A list of oncogenes, previously identified as recurrently mutated, was selected for analysis (Additional file 2) to investigate their mutation frequency in our series. The selection was based on previously reported sequencing studies on EPC and EP [13,14,15,16,17]. The data from studies using Sanger sequencing and those based on a single sample were excluded from the selection, except for the study by Thibodeau et al. [21], which performed whole genome sequencing and RNA sequencing on a single EPC sample and reported amplification and elevated expression of GSK3B.

Filtering of protein-coding mutations

To explore potential novel driver genes and pathways, the mutated genes meeting the aforementioned filtering criteria underwent additional refinement steps. Genes that were recurrently mutated only in EPCs (6/13 or more), only in EPs (2/5 or more), and in both 6/13 or more EPCs and 2/5 or more EPs (≥ 40% of both tumor types) were selected into separate lists. Genes on these lists were further filtered using the Genome Aggregation Database (gnomAD) v2.1.1 [45]. The gnomAD database provides information on predicted gene pathogenicity. Z-scores indicate tolerance to missense and synonymous mutations, with higher scores reflecting greater intolerance. Conversely, pLI scores (ranging from 0 to 1) predict tolerance to loss-of-function variants, with pLI ≥ 0.9 indicating high intolerance. Following gnomAD guidelines, a cutoff pLI score ≥ 0.9 and missense Z-score ≥ 3.1 were applied [45]. The selected genes were then analyzed using the EnrichR analysis tool (accessed on 7 April 2024) [46,47,48] to explore possible driver pathways. Four pathway databases, namely Bioplanet 2019, KEGG 2021 Human, WikiPathway 2023 Human, and Reactome 2022, were utilized for this analysis.

Statistical analysis

The association of the non-continuous parameters with the continuous parameters was evaluated using the Mann–Whitney U test. The association between the non-continuous parameters was evaluated using the Pearson χ2 test or the Fisher’s test as appropriate. Statistical analysis was performed with SPSS software (IBM SPSS Statistics for Windows ver.29). P values < 0.05 were considered statistically significant.

Results

Patient overview

Patient and tumor data are summarized in Table 1. There was no statistically significant association between EPC or EP and age (p = 0.615), tumor size (p = 0.080), sex (p = 0.314), or tumor location (p = 0.519).

Table 1 Patient data of sequenced samples
Full size table

Overview of WES data

A mean read depth of 86.0x (range 30.8–178.6) was achieved. A total of 94.8% (range 73.3–97.5) of the targeted regions were covered to at least 20x. The mean total of aligned reads was 50.9 million (range 19.8–104.1 million). The following rules were applied for counting reads: (1) duplicate reads were ignored; (2) overlapping mates were double-counted; (3) reads with MAPQ > 0 were included. MAPQ = 0 reads were filtered.

A mean of 112 212 (range 44 772–237 956) variants were called, from which 94.4% (range 92.9–95.5) were SNVs. The mean total of variants was larger in EPCs (121 078, range 44 772–237 956) compared with EPs (91 447, range 67 695–118 807). After applying the seven filters mentioned above, the mean number of variants was reduced to 3559 (range 199–8503) for EPCs and 2044 (range 1146–2697) for EPs. In the DPA sample, the total number of variants was 100 793, which was reduced to 2252 after filtering. The following analyses were conducted with these filtered datasets.

Mutational signatures of EPC and EP

Five de novo mutational signatures, labeled “sign1-5” were identified with a notable prevalence of C > T mutations (Fig. 1A). Sign 4 had more T > C and C > G mutations than signs 1–3, while sign 5 had even fewer C > T mutations and more T > C, C > G, C > A, and T > G mutations. When compared with the COSMIC v.3.3 single-base substitutions (SBS) database, SBS 7a and 7b UV-signatures exhibited significant similarity to signs 1–4. Both UV signatures had some similarity to sign 5. SBS 30 signature (base excision repair deficiency) was highly similar to all de novo signatures. Additionally, signatures such as SBS11 (alkylating agents) and SBS32 (azathioprine exposure) exhibited similarities especially with sign 1–4. SBS6 (defective DNA mismatch repair) and SBS5 (clock-like signature) resembled sign 5 (Fig. 1B).

Fig. 1
figure 1

Mutational signature analysis. A Five de novo signatures were identified. B Comparison of five de novo signatures to COSMIC single-base substitutions database. C Clustering of the samples based on signature components. EPC samples are indicated in red letters; EP samples are indicated in blue letters; and DPA sample is indicated in green

Full size image

The samples were clustered based on the signature components (Fig. 1C). Among the five EPs, two (P26 and P32) had a high weight for sign 1, one (P38) had relatively high weights for both sign 1 and sign 2, and two (P23 and P24) had a high weight for sign 2. DPA had a high weight for sign 1. The other samples were EPCs. Notably, there were no discernible differences in distribution patterns between EPs and EPCs.

Genetic overview of genes with previously reported mutations in EPC and EP

The mutation frequency analysis of the curated genes (Additional file 2) identified exclusive mutations of TP53 in EPCs (38%) (Fig. 2). TP53 stop-gain SNVs were identified in three EPC samples, and nonsynonymous SNVs were identified in four EPC samples, with no indels observed. Other mutations exclusively observed in EPCs included NCOR1 (46%), GSK3B (15%), ATM (8%), CDKN2A (8%), KRAS (8%), and MUC16 (8%). NCOR1 contained stop-gain mutations in two, nonsynonymous SNVs in three, and frameshift insertions in two samples. One sample harbored a CDKN2A nonsynonymous SNV. One EPC sample had four MUC16 stop-gain mutations. All mutations detected in GSK3B were nonsynonymous SNVs.

Fig. 2
figure 2

Mutation frequency of genes per patient. The heat map displays the frequency of mutations for each gene across different tumor types (EPC, orange; EP, blue). N, number; NGS, next generation sequencing; WES, whole exome sequencing; * Included due to reported amplification and high expression of GSK3B [21]

Full size image

The mutation frequency of APC was significantly higher in EPCs (85%) than in EPs (20%). None of the selected genes had exclusive mutations in EPs, although ERBB4 had a higher mutation frequency in EPs (80%) than in EPCs (31%). Most selected genes had similar mutation frequencies between EPCs and EPs. The DPA harbored mutations in ABL1, APC, CDKN2A, ERBB4, FAT2, KMT2D, LRP1B, NCOR1, PDGFRA, PTCH1, RET, SETD2, TP53, and TTN.

Association of WES mutations with biological pathways

The protein-coding mutations that passed the aforementioned filtering (uploaded to Zenodo, DOI: https://doiorg.publicaciones.saludcastillayleon.es/https://doiorg.publicaciones.saludcastillayleon.es/10.5281/zenodo.10701230) were further examined using four pathway databases (Bioplanet 2019, KEGG 2021 Human, WikiPathway 2023 Human, Reactome 2022) to explore possible driver pathways.

The top 10 terms enriched in the genes mutated exclusively in EPCs are shown in Table 2, and those enriched exclusively in EPs are shown in Table 3. All the statistically significant terms can be found in Additional file 3. Among the genes mutated only in EPCs, pathways associated with cell adhesion, extracellular matrix, fatty acid metabolism, microRNA, and PDGF ranked at the top. Ras and MAPK signaling pathways were also on the list of statistically significant terms. In the list of genes mutated only in EPs, pathways associated with ketone body metabolism, the tricarboxylic acid cycle, and amino acid metabolism ranked at the top.

Table 2 Top 10 pathway terms in EPCs
Full size table
Table 3 Top 10 pathway terms in EPs
Full size table

Discussion

In this study, we sought to explore the exon characteristics of EPCs and EPs to provide novel insights into potential driver mutations and pathways and to examine the genetic aspects that differentiate EPCs from EPs.

WES revealed a notable prevalence of UV-induced mutational signatures in both EPCs and EPs, which is consistent with previous findings on UV-induced mutations in skin malignancies [13, 14, 17, 18]. Additionally, our de novo signatures exhibited a striking similarity to the SBS30 signature associated with base excision repair deficiency, which may be attributed to inactivating mutations in NTHL1 [49]. Nevertheless, NTHL1 mutations were only observed in a limited number of cases (2 EPCs and 1 EP) within our cohort, raising questions regarding the general association of SBS30 with our study population. Notably, some samples had a higher weight of a clock-like signature, consistent with the generally advanced age of onset in EPCs [5, 6]. Additionally, SBS6 (defective DNA mismatch repair) exhibited similarities with all de novo signatures, indicating potential microsatellite instability in both EPCs and EPs.

Among the previously reported genes with mutations in EPs and EPCs, exclusive mutations in EPCs were observed in NCOR1 (46%), TP53 (38%), ATM (8%), CDKN2A (8%), GSK3B (15%), KRAS (8%), and MUC16 (8%), while no genes exhibited exclusive mutations in EPs. Many genes, including PTCH1, KMT2D, LRP1B, ABL1, CACNA1S, CSMD3, FAT2, PBRM1, PIK3CA, ARID3A, EGFR, ERBB2, HRAS, ZFHX4, and TTN, were recurrently mutated in both EPCs and EPs.

Nuclear receptor corepressor 1 (NCOR1) was among the most mutated genes. The gene was recurrently mutated in EPCs across all previous studies that examined it. However, no prior studies have investigated NCOR1 mutations in EPs. NCOR1 is a transcriptional corepressor that represses the tolerogenic program in dendritic cells, and its depletion leads to upregulation of tolerogenic genes [50]. In bladder cancer, NCOR1 alterations correlate with the efficacy of immune-checkpoint blockade therapy, indicating its potential as an immunotherapy biomarker [51]. Given the successful treatment outcomes with immune-checkpoint inhibitors and imiquimod in a few EPC cases [52, 53], further investigation into the predictive value of NCOR1 for immunotherapy response in EPC patients and subsequent clinical trials are warranted. CDKN2A was mutated exclusively in EPCs in all studies that examined the gene. Additionally, ATM and KRAS mutations were exclusively found in EPCs in other studies as well.

Pathway analyses revealed a significant enrichment of terms associated with the extracellular matrix (ECM) and cell adhesion in EPCs. ECM remodeling plays a significant role in the development, progression, invasion, and therapeutic resistance of skin cancers [54,55,56]. Alterations in cell adhesion molecules, such as integrins, cadherins, and selectins, have also been widely reported in skin cancers [57, 58]. The stiffness of the ECM caused by remodeling prevents the penetration of many antitumor treatments, and treatments targeting ECM elements, such as collagen and hyaluronic acid, are already in clinical use [59]. The enrichment of terms associated with ECM and cell adhesion was not seen in the genes mutated recurrently only in EPs, in which terms associated with metabolism were enriched.

Although not ranked in the top 10 list of terms, the MAPK and Ras signaling pathways were enriched in genes mutated only in EPCs. For the Ras-family genes, KRAS was mutated in only one EPC sample, while HRAS and NRAS mutations were relatively common in both EPCs and EPs. However, mutations were not detected in the hotspot regions of Ras-family genes. A recent study suggested that somatic mutations of MAP2K1 activate the MAPK pathway in EPCs, and MAPK pathway alterations are a late event in EPC progression [18]. Our findings suggest that the Ras and MAPK signaling pathways may play a role in the development of EPCs.

Additionally, the PDGF signaling pathway was enriched in genes mutated only in EPCs. Multiple PDGFRA and PDGFRB mutations were found in exons 12 and 14, where recurrent mutations have been reported in other tumors [60,61,62]. One EPC harbored a p.R561C mutation, which is associated with activation of PDGFR-β signaling in familial myofibroma patients [63].

Mutations were found in multiple genes mutated in EPCs in the DPA sample. Such genes included CDKN2A, ERBB4, NCOR1, PDGFRA, and PTCH1. Interestingly, the sample showed a high weight on sign 1 in the mutation signature analysis, suggesting involvement of UV exposure. DPA is a rare malignant tumor originating from the eccrine sweat glands [64]. Human papillomavirus 42 integration and expression of HPV42 oncoproteins in DPAs have been reported [65]. Our results suggest that DPAs share some genetic features with EPCs and EPs.

Our study was limited by the small cohort size due to the rarity of EPCs. Such a cohort size often makes the detection of rare variants challenging [66]. Investigating the association between mutations and prognosis was not possible due to the small sample number. Furthermore, our study lacked healthy tissue controls for WES, thereby precluding the identification of germline and somatic variations from each other.

Overall, WES provided valuable insights into the general heterogeneity of EPCs and EPs, revealing at the same discrepancies between the two tumor types, such as exclusive mutations of TP53, NCOR1, CDKN2A, and GSK3B in EPCs and a higher mutational load in EPCs than in EPs. Additionally, we identified alterations in pathways associated with cell adhesion and extracellular matrix in EPCs, while pathways associated with ketone body and amino acid metabolism were altered in EPs. The MAPK and Ras signaling pathways were enriched in genes mutated only in EPCs. These findings elucidate the possible genetic mechanisms involved in the malignant transformation of EPs or the development of de novo EPCs. More evidence supporting the involvement of disrupted genes and pathways in tumorigenesis and the growth of EPCs, potentially through the utilization of cell lines or in vivo studies, is necessary to facilitate development of targeted therapeutic strategies and to increase understanding of this understudied tumor.

Conclusions

EPCs and EPs are generally heterogeneous tumor entities, but there are also distinct discrepancies between them, such as exclusive TP53, NCOR1, and CDKN2A mutations in EPCs and a higher mutational load in EPCs than in EPs. Moreover, EPCs harbored alterations in pathways associated with cell adhesion and the extracellular matrix, in addition to the MAPK and Ras signaling pathways. The results revealed genes and signaling pathways that may play a role in EPC development. Validation of their involvement may provide novel treatment options for EPCs, which currently lack standardized therapy regimens after surgery.

Availability of data and materials

The data generated in this study are available from the Helsinki biobank (Project ID: HBP2018003) and the Finnish Clinical Biobank Tampere (pirha.fi/tampereen-biopankki, Project ID: BB2018-006) upon request. The codes used in this study are available on GitHub (https://github.com/Rare-Cancers-Research-Group/Porocarcinoma). All variants obtained from the WES analysis that passed filtering are documented in the text file formats uploaded to Zenodo (DOI: https://doiorg.publicaciones.saludcastillayleon.es/https://doiorg.publicaciones.saludcastillayleon.es/10.5281/zenodo.10701230).

Abbreviations

EPC:

Eccrine porocarcinoma

EP:

Eccrine poroma

DPA:

Digital papillary adenocarcinoma

FFPE:

Formalin-fixed paraffin-embedded

WES:

Whole-exome sequencing

ECM:

Extracellular matrix

SNV:

Single nucleotide variant

SBS:

Single-base substitutions

References

  1. González-López MA, Vázquez-López F, Soler T, Gómez-Diéz S, Garcia YH, Manjón JA, et al. Metastatic eccrine porocarcinoma: a 5.6-year follow-up study of a patient treated with a combined therapeutic protocol. Dermatol Surg. 2003;29(12):1227–32.

    PubMed  Google Scholar 

  2. Merilainen AS, Pukkala E, Bohling T, Koljonen V. Malignant eccrine porocarcinoma in Finland during 2007 to 2017. Acta Derm Venereol. 2021;101(1):adv00363.

    PubMed  Google Scholar 

  3. Kyrmanidou E, Fotiadou C, Kemanetzi C, Trakatelli MG, Trigoni A, Patsatsi A, et al. Eccrine poroma: pathogenesis, new diagnostic tools and association with porocarcinoma-a review. Diagnostics (Basel). 2023;13(16):2689.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Tsiogka A, Koumaki D, Kyriazopoulou M, Liopyris K, Stratigos A, Gregoriou S. Eccrine porocarcinoma: a review of the literature. Diagnostics (Basel). 2023;13(8):1431.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Salih AM, Kakamad FH, Baba HO, Salih RQ, Hawbash MR, Mohammed SH, et al. Porocarcinoma; presentation and management, a meta-analysis of 453 cases. Ann Med Surg (Lond). 2017;20:74–9.

    Article  PubMed  Google Scholar 

  6. Belin E, Ezzedine K, Stanislas S, Lalanne N, Beylot-Barry M, Taieb A, et al. Factors in the surgical management of primary eccrine porocarcinoma: prognostic histological factors can guide the surgical procedure. Br J Dermatol. 2011;165(5):985–9.

    Article  PubMed  Google Scholar 

  7. Nazemi A, Higgins S, Swift R, In G, Miller K, Wysong A. Eccrine porocarcinoma: new insights and a systematic review of the literature. Dermatol Surg. 2018;44(10):1247–61.

    Article  PubMed  Google Scholar 

  8. Brown CW Jr, Dy LC. Eccrine porocarcinoma. Dermatol Ther. 2008;21(6):433–8.

    Article  PubMed  Google Scholar 

  9. Robson A, Greene J, Ansari N, Kim B, Seed PT, McKee PH, et al. Eccrine porocarcinoma (malignant eccrine poroma): a clinicopathologic study of 69 cases. Am J Surg Pathol. 2001;25(6):710–20.

    Article  PubMed  Google Scholar 

  10. Joshy J, van Bodegraven B, Mistry K, Craig P, Rajan N, Vernon S, et al. Epidemiology of porocarcinoma in England 2013–2018: a population-based registry study. Clin Exp Dermatol. 2023;48(7):770–7.

    Article  PubMed  Google Scholar 

  11. Ragi SD, Moseley I, Ouellette S, Rao B. Epidemiology and Survival of eccrine porocarcinoma by sex in the United States: a surveillance, epidemiology, and end results database analysis. Dermatol Surg. 2023;49(1):97–9.

    Article  PubMed  Google Scholar 

  12. Miyamoto K, Yanagi T, Maeda T, Ujiie H. Diagnosis and management of porocarcinoma. Cancers (Basel). 2022;14(21):5232.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Harms PW, Hovelson DH, Cani AK, Omata K, Haller MJ, Wang ML, et al. Porocarcinomas harbor recurrent HRAS-activating mutations and tumor suppressor inactivating mutations. Hum Pathol. 2016;51:25–31.

    Article  PubMed  Google Scholar 

  14. Bosic M, Kirchner M, Brasanac D, Leichsenring J, Lier A, Volckmar AL, et al. Targeted molecular profiling reveals genetic heterogeneity of poromas and porocarcinomas. Pathology. 2018;50(3):327–32.

    Article  PubMed  Google Scholar 

  15. Sekine S, Kiyono T, Ryo E, Ogawa R, Wakai S, Ichikawa H, et al. Recurrent YAP1-MAML2 and YAP1-NUTM1 fusions in poroma and porocarcinoma. J Clin Invest. 2019;129(9):3827–32.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Cavalieri S, Busico A, Capone I, Conca E, Dallera E, Quattrone P, et al. Identification of potentially druggable molecular alterations in skin adnexal malignancies. J Dermatol. 2019;46(6):507–14.

    Article  PubMed  Google Scholar 

  17. Denisova E, Westphal D, Surowy HM, Meier F, Hutter B, Reifenberger J, et al. Whole-exome sequencing in eccrine porocarcinoma indicates promising therapeutic strategies. Cancer Gene Ther. 2021. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41417-021-00347-z.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Westphal D, Garzarolli M, Sergon M, Horak P, Hutter B, Becker JC, et al. High tumour mutational burden and EGFR/MAPK pathway activation are therapeutic targets in metastatic porocarcinoma. Br J Dermatol. 2021. https://doiorg.publicaciones.saludcastillayleon.es/10.1111/bjd.20604.

    Article  PubMed  Google Scholar 

  19. Leroy B, Anderson M, Soussi T. TP53 mutations in human cancer: database reassessment and prospects for the next decade. Hum Mutat. 2014;35(6):672–88.

    Article  PubMed  Google Scholar 

  20. Berkers CR, Maddocks OD, Cheung EC, Mor I, Vousden KH. Metabolic regulation by p53 family members. Cell Metab. 2013;18(5):617–33.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Thibodeau ML, Bonakdar M, Zhao E, Mungall KL, Reisle C, Zhang W, et al. Whole genome and whole transcriptome genomic profiling of a metastatic eccrine porocarcinoma. NPJ Precis Oncol. 2018;2(1):8.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Dias-Santagata D, Lam Q, Bergethon K, Baker GM, Iafrate AJ, Rakheja D, et al. A potential role for targeted therapy in a subset of metastasizing adnexal carcinomas. Mod Pathol. 2011;24(7):974–82.

    Article  PubMed  Google Scholar 

  23. Le LP, Dias-Santagata D, Pawlak AC, Cosper AK, Nguyen AT, Selim MA, et al. Apocrine-eccrine carcinomas: molecular and immunohistochemical analyses. PLoS ONE. 2012;7(10):e47290.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Brennan CW, Verhaak RG, McKenna A, Campos B, Noushmehr H, Salama SR, et al. The somatic genomic landscape of glioblastoma. Cell. 2013;155(2):462–77.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Bhargava R, Gerald WL, Li AR, Pan Q, Lal P, Ladanyi M, et al. EGFR gene amplification in breast cancer: correlation with epidermal growth factor receptor mRNA and protein expression and HER-2 status and absence of EGFR-activating mutations. Mod Pathol. 2005;18(8):1027–33.

    Article  PubMed  Google Scholar 

  26. Bethune G, Bethune D, Ridgway N, Xu Z. Epidermal growth factor receptor (EGFR) in lung cancer: an overview and update. J Thorac Dis. 2010;2(1):48–51.

    PubMed  PubMed Central  Google Scholar 

  27. Ooi A, Takehana T, Li X, Suzuki S, Kunitomo K, Iino H, et al. Protein overexpression and gene amplification of HER-2 and EGFR in colorectal cancers: an immunohistochemical and fluorescent in situ hybridization study. Mod Pathol. 2004;17(8):895–904.

    Article  PubMed  Google Scholar 

  28. Cancer Genome Atlas N. Genomic classification of cutaneous melanoma. Cell. 2015;161(7):1681–96.

  29. Monahan KB, Rozenberg GI, Krishnamurthy J, Johnson SM, Liu W, Bradford MK, et al. Somatic p16(INK4a) loss accelerates melanomagenesis. Oncogene. 2010;29(43):5809–17.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Haferkamp S, Becker TM, Scurr LL, Kefford RF, Rizos H. p16INK4a-induced senescence is disabled by melanoma-associated mutations. Aging Cell. 2008;7(5):733–45.

    Article  PubMed  Google Scholar 

  31. Manuel Serrano GJH, Beach D. A new regulatory motif in cell-cycle control causing specific inhibition of cyclin D/CDK4. Nature. 1993;366:704–7.

    Article  Google Scholar 

  32. Serrano M, Lin AW, McCurrach ME, Beach D, Lowe SW. Oncogenic ras provokes premature cell senescence associated with accumulation of p53 and p16INK4a. Cell. 1997;88:593–602.

    Article  PubMed  Google Scholar 

  33. Stott FJ, Bates S, James MC, McConnell BB, Starborg M, Brookes S, et al. The alternative product from the human CDKN2A locus, p14(ARF), participates in a regulatory feedback loop with p53 and MDM2. EMBO J. 1998;17(17):5001–14.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Prasad D, Baldelli E, Blais EM, Davis J, El Gazzah E, Mueller C, et al. Functional activation of the AKT-mTOR signalling axis in a real-world metastatic breast cancer cohort. Br J Cancer. 2024;131(9):1543–54.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Bachman KE, Argani P, Samuels Y, Silliman N, Ptak J, Szabo S, et al. The PIK3CA gene is mutated with high frequency in human breast cancers. Cancer Biol Ther. 2004;3(8):772–5.

    Article  PubMed  Google Scholar 

  36. Mukhopadhyay S, Vander Heiden MG, McCormick F. The metabolic landscape of RAS-driven cancers from biology to therapy. Nat Cancer. 2021;2(3):271–83.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Kervarrec T, Pissaloux D, Tirode F, de la Fouchardiere A, Sohier P, Frouin E, et al. Gene fusions in poroma, porocarcinoma and related adnexal skin tumours: an update. Histopathology. 2023. https://doiorg.publicaciones.saludcastillayleon.es/10.1111/his.15023.

    Article  PubMed  Google Scholar 

  38. Behera S, Catreux S, Rossi M, Truong S, Huang Z, Ruehle M, et al. Comprehensive genome analysis and variant detection at scale using DRAGEN. Nat Biotechnol. 2024. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41587-024-02382-1.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010;38(16):e164.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Ioannidis NM, Rothstein JH, Pejaver V, Middha S, McDonnell SK, Baheti S, et al. REVEL: an ensemble method for predicting the pathogenicity of rare missense variants. Am J Hum Genet. 2016;99(4):877–85.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Rosales RA, Drummond RD, Valieris R, Dias-Neto E, da Silva IT. signeR: an empirical Bayesian approach to mutational signature discovery. Bioinformatics. 2017;33(1):8–16.

    Article  PubMed  Google Scholar 

  42. Fantini D, Vidimar V, Yu Y, Condello S, Meeks JJ. MutSignatures: an R package for extraction and analysis of cancer mutational signatures. Sci Rep. 2020;10(1):18217.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Alexandrov LB, Kim J, Haradhvala NJ, Huang MN, Tian Ng AW, Wu Y, et al. The repertoire of mutational signatures in human cancer. Nature. 2020;578(7793):94–101.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Metsalu T, Vilo J. ClustVis: a web tool for visualizing clustering of multivariate data using principal component analysis and heatmap. Nucleic Acids Res. 2015;43(W1):W566–70.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Gudmundsson S, Singer-Berk M, Watts NA, Phu W, Goodrich JK, Solomonson M, et al. Variant interpretation using population databases: lessons from gnomAD. Hum Mutat. 2022;43(8):1012–30.

    Article  PubMed  Google Scholar 

  46. Chen E, Tan C, Kou Y, Duan Q, Wang Z, Meirelles G, et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinform. 2013;14:128.

    Article  Google Scholar 

  47. Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 2016;44(W1):W90–7.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Xie Z, Bailey A, Kuleshov MV, Clarke DJB, Evangelista JE, Jenkins SL, et al. Gene set knowledge discovery with enrichr. Curr Protoc. 2021;1(3):e90.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Drost J, van Boxtel R, Blokzijl F, Mizutani T, Sasaki N, Sasselli V, et al. Use of CRISPR-modified human stem cell organoids to study the origin of mutational signatures in cancer. Science. 2017;358(6360):234–8.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Ahad A, Stevanin M, Smita S, Mishra GP, Gupta D, Waszak S, et al. NCoR1: putting the brakes on the dendritic cell immune tolerance. iScience. 2019;19:996–1011.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Lin A, Qiu Z, Zhang J, Luo P. Effect of NCOR1 mutations on immune microenvironment and efficacy of immune checkpoint inhibitors in patient with bladder cancer. Front Immunol. 2021;12:630773.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Singh A, Nguyen L, Everest S, Vinogradov M. Metastatic porocarcinoma effectively managed by pembrolizumab. Cureus. 2021;13(11):e20004.

    PubMed  PubMed Central  Google Scholar 

  53. Nagasawa T, Hirata A, Niiyama S, Enomoto Y, Fukuda H. Successful treatment of porocarcinoma with maxacalcitol and imiquimod. Dermatol Ther. 2019;32(2):e12830.

    Article  PubMed  Google Scholar 

  54. Fromme JE, Zigrino P. The role of extracellular matrix remodeling in skin tumor progression and therapeutic resistance. Front Mol Biosci. 2022;9:864302.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Popovic A, Tartare-Deckert S. Role of extracellular matrix architecture and signaling in melanoma therapeutic resistance. Front Oncol. 2022;12:924553.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Sleeboom JJF, van Tienderen GS, Schenke-Layland K, van der Laan LJW, Khalil AA, Verstegen MMA. The extracellular matrix as hallmark of cancer and metastasis: from biomechanics to therapeutic targets. Sci Transl Med. 2024;16(728):eadj3840.

    Article  Google Scholar 

  57. Pogorzelska-Dyrbus J, Szepietowski JC. Adhesion molecules in non-melanoma skin cancers: a comprehensive review. In Vivo. 2021;35(3):1327–36.

    Article  PubMed  PubMed Central  Google Scholar 

  58. D’Arcy C, Kiel C. Cell adhesion molecules in normal skin and melanoma. Biomolecules. 2021;11(8):1213.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Huang J, Zhang L, Wan D, Zhou L, Zheng S, Lin S, et al. Extracellular matrix and its therapeutic potential for cancer treatment. Signal Transduct Target Ther. 2021;6(1):153.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Corless CL, Schroeder A, Griffith D, Town A, McGreevey L, Harrell P, et al. PDGFRA mutations in gastrointestinal stromal tumors: frequency, spectrum and in vitro sensitivity to imatinib. J Clin Oncol. 2005;23(23):5357–64.

    Article  PubMed  Google Scholar 

  61. Huss S, Wardelmann E, Goltz D, Binot E, Hartmann W, Merkelbach-Bruse S, et al. Activating PDGFRA mutations in inflammatory fibroid polyps occur in exons 12, 14 and 18 and are associated with tumour localization. Histopathology. 2012;61(1):59–68.

    Article  PubMed  Google Scholar 

  62. Iwamura R, Komatsu K, Kusano M, Kubo C, Inaba Y, Shiba E, et al. PDGFRB and NOTCH3 mutations are detectable in a wider range of pericytic tumors, including myopericytomas, angioleiomyomas, glomus tumors, and their combined tumors. Mod Pathol. 2023;36(3):100070.

    Article  PubMed  Google Scholar 

  63. Cheung YH, Gayden T, Campeau PM, LeDuc CA, Russo D, Nguyen VH, et al. A recurrent PDGFRB mutation causes familial infantile myofibromatosis. Am J Hum Genet. 2013;92(6):996–1000.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Jabbour AJ, Tangoren IA, Kanik AB. Digital papillary adenocarcinoma: case of a rare malignant cutaneous tumor of the eccrine sweat gland. Case Rep Dermatol. 2021;13(2):422–7.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Leiendecker L, Neumann T, Jung PS, Cronin SM, Steinacker TL, Schleiffer A, et al. Human papillomavirus 42 drives digital papillary adenocarcinoma and elicits a germ cell-like program conserved in HPV-positive cancers. Cancer Discov. 2023;13(1):70–84.

    Article  PubMed  Google Scholar 

  66. Lawrence MS, Stojanov P, Mermel CH, Robinson JT, Garraway LA, Golub TR, et al. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature. 2014;505(7484):495–501.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The Helsinki biobank is acknowledged for provision of research materials. The study benefited from samples and data from the Finnish Clinical Biobank Tampere. NGS library preparation, sequencing, and WES bioinformatics were performed by the Institute for Molecular Medicine Finland FIMM Technology Centre, University of Helsinki.

Funding

Open Access funding provided by University of Helsinki (including Helsinki University Central Hospital). This study was funded by the Jane and Aatos Erkko Foundation (4706174, T.B.), Medicinska Understödsföreningen Liv och Hälsa (4708936, T.B.), Finska läkaresällskapet (4709232, T.B.), Cancer Foundation Finland (4709194, H.S.), Helsinki University Central Hospital (HUCH) Competitive Research Fund (EVO) (TYH2020208, V.K.), and Emil Aaltonen Foundation (210179, M.P.).

Author information

Authors and Affiliations

  1. Department of Pathology, University of Helsinki and Helsinki University Hospital, P.O Box 63, 00014, Helsinki, Finland

    Maya Puttonen, Tom Böhling & Harri Sihto

  2. Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland

    Henrikki Almusa

  3. Department of Plastic Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland

    Virve Koljonen

Authors
  1. Maya Puttonen
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Henrikki Almusa
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Tom Böhling
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Virve Koljonen
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Harri Sihto
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

MP, TB, VK, and HS developed the study concept. TB reviewed the diagnoses. MP prepared the samples for WES. HA conducted the bioinformatic analyses. MP and HS analyzed the data. MP drafted the manuscript. All authors participated in discussions and revisions, contributing to the final version.

Corresponding author

Correspondence to Maya Puttonen.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the institutional ethics committee of Helsinki University Central Hospital [HUS/358/2018].

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1. The model with five de novo signatures showed the lowest Bayesian information criterion

13023_2025_3586_MOESM2_ESM.pdf

Additional file 2. A list of oncogenes selected for the analyses. Corresponding Ensembl gene codes are shown on the right side

13023_2025_3586_MOESM3_ESM.xlsx

Additional file 3. A list of statistically significant terms in each database, p-values, and the genes associated with each term

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits 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 Commons 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 regulation 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/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Puttonen, M., Almusa, H., Böhling, T. et al. Whole-exome sequencing identifies distinct genomic aberrations in eccrine porocarcinomas and poromas. Orphanet J Rare Dis 20, 70 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13023-025-03586-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13023-025-03586-7

Keywords

Orphanet Journal of Rare Diseases

ISSN: 1750-1172