Integrative Analysis of Pleomorphic Dermal Sarcomas Reveals Fibroblastic Differentiation and Susceptibility to Immunotherapy

This article is featured in Highlights of This Issue, p. 5541

Pleomorphic dermal sarcomas (PDS) are cutaneous malignant tumors arising in UV-exposed locations and are typically diagnosed in elderly individuals. While the majority of PDS can be treated by curative excisions, local recurrences and distant metastases occur in about one-third of these patients (1–3). However, no standard treatment regimens are currently available for late-stage PDS.

Although the prevalence of PDS remains largely unknown, these tumors are frequently observed within the group of cutaneous sarcomas. Therefore, PDS is generally believed to be of mesenchymal lineage (4). However, recent data demonstrate similarities between PDS and cutaneous squamous cell carcinoma (SCC; refs. 5, 6). This may suggest that PDS originates from dedifferentiated SCCs of the skin (6). Nevertheless, the PDS cell of origin still remains elusive. Clinically, there is a strong rational to discriminate PDS from SCC, as PDS exhibits a less favorable prognosis in comparison with SCC (7, 8).

So far, little is known about molecular alterations in PDS (9, 10). Thus, there is a substantial clinical need to uncover systemic treatment options for patients with advanced stages of PDS.

We analyzed samples from a total of 28 individuals of primary PDS [23 males and five females; median age: 80 years (58–94 years)] from UV-exposed locations (head/neck, n = 26; shoulder, n = 2). All cases were treatment naïve. Clinically, 23 of 28 patients (82.1%) had no tumor progress (local or distant relapse) over a time period of 31.8 ± 18.6 months, whereas five patients (17.9%) showed a disease progression after a mean of 17.0 ± 13.0 months (t test, two-sided; |$P\ = \ 0.067$|⁠). The study protocol was conducted in accordance with the ethical guidelines of the 1975 Declaration of Helsinki. All investigations were performed after approval of the institution's human research review committee of the University of Cologne, Germany (registration No. 15-307). Informed written consent was obtained from each subject.

Six 10-μm sections were each macrodissected for DNA and RNA isolation using hematoxylin-eosin stained slides with marked tumor and normal regions as a reference. Regions were marked by a pathologist (A. Quaas, D. Helbig, R. Buettner, S. Klein). DNA and RNA were isolated using the Maxwell LEV DNA FFPE Kit or Maxwell RSC RNA FFPE Kit, then further processed on the semiautomated Maxwell LEV Instrument and Maxwell RSC Instrument, respectively (Promega) according to manufacturer's instruction. RNA extraction included DNAse digestion. DNA was quantified using Qubit dsDNA BR Assay Kit and both Qubit RNA BR Assay Kit and NanoDrop spectrophotometer was used for RNA quantification (All from Invitrogen).

Whole-exome sequencing (WES) was performed from genomic DNA, using the SureSelect Human All Exon V6 (Agilent) according to the manufacturer's instructions. Obtained exome libraries were paired-end sequenced on a NovaSeq (2 × 150 bp) platform (Illumina). We achieved a median sequencing depth of 84 in the tumors and 86 in corresponding normal tissues (Supplementary Table S3).

Two primers were used to detect DDX31 R72K mutations: DDX31-R72K-F1 (5′-TACAGCTGGAGCCGTTCCG-3′), DDX31-R72K-R1 (5′-CCATGGTCTGCGTGGGTG AC-3′), DDX31-R72K-F2 (5′-CGGGACGAGTCCGTACAGC-3′), DDX31-R72K-R2 (5′- GGTGCGCAGACTGCTGGG-3′). PCR was conducted using Platinum Taq DNA polymerase (Invitrogen) in C1000 Touch Thermal Cycler (Bio-Rad). PCR products were purified using QIAquick PCR Purification Kit (Qiagen) and sent for sequencing (Eurofins Scientific).

Somatic single-nucleotide variants (SNVs) of cutaneous melanoma (CM), cutaneous SCC, and cutaneous basal cell carcinoma (BCC) were obtained from a recently published study (11).

Sequencing reads were aligned to the human reference genome (NCBI build 37/hg19) by using version 0.7.13-r1126 of the BWA mem aligner. Corresponding read pairs were then masked as potential PCR duplicates and areas of overlapping pairs of reads were excluded. Somatic alterations (substitutions, insertions, deletions, copy numbers), as well as cellularity estimates were determined by our in-house cancer genome analysis pipeline (12, 13).

To estimate the contribution of previously validated mutational signatures in PDS, melanoma, BCC, and SCC tumor types, we used the catalogued signatures extracted with SigProfiler from WES data (11). The SNV counts were classified into 96 categories based on their immediate 5′ and 3′ sequence context (14). Normalization was performed as such that for each of the 96 categories, the counts were multiplied to a ratio between the exome and the genome occurrence of the respective trinucleotidic class. In our model, the SNV count matrix |$A$| is represented as product of two nonnegative matrices |$W$| and |$H$|⁠: |$A \approx W \cdot H$|⁠. Here, |$H$| is the mutational signature matrix and |$W$| is the so-called emission matrix reflecting the number of mutations associated to each signature of every sample. To fit the catalogued signatures |$H$| to the normalized SNV counts |$A$|⁠, the emission matrix |$W$| was imputed. The optimization of |$W$| was performed by minimizing the Frobenius norm |$\parallel A - W \cdot H\parallel {{\rm{\ }}_F}$|⁠, while maintaining the constraint |${W_{ij}} \ge 0$| (⁠|$i\ = \ 1, \cdots ,{n_g}$| and |$j\ = \ 1, \cdots ,{n_k}$|⁠). The set of reference signatures best explaining |$A$| was selected such the Frobenius norm was notably reduced.

Spatial mutational density (SMD) correlates to the cancer type reflecting its cell of origin. For highly mutated cancer types also, exonic region from the WES data can be used (15). We compared the SMD of PDS, CM, BCC, and SCC tumors. For each sample, a collection of 10 Mb windows (n = 295) across the genome were used by counting the number of point mutations in each window. This number was then normalized to the total number of mutations. A feature selection step was performed using a restricted Boltzman machine, from which we extracted 150 features. The selected features were then embedded and plotted in two dimensions using t-stochastic neighbor embedding (t-SNE; ref. 16).

For quantification of CD4 and CD8 inflammatory cells, we digitalized IHC slides using a NanoZoomerS360 Hamamatsu whole-slide-image (WSI) slide scanner at 40× magnification. Subsequent images were segmented, and quantification of immune cell populations were performed on WSI using a watershed algorithm. For cellular classification, a neural network was trained.

IHC stainings were performed using the BOND MAX from Leica) according to the manufacturer's protocol. All immunostainings were scored independently by trained pathologists (A. Quaas, R. Buettner, S. Klein), as well as a dermatopathologist (D. Helbig).

Similar to other tumors with chronic UV exposure, we found that PDS exhibit an extremely high tumor mutational burden (TMB) with an average of 42.7 nonsynonymous variants per mega base (Fig. 1A). This particularly high mutational burden ranks among the most highly mutated cancers, even exceeding the mutation frequency of CM and at a comparable level to cutaneous SCC (Supplementary Fig. S1).

By determining genes that were significantly enriched for point mutations across our set of 28 whole-exome sequenced PDS, we detected damaging, loss-of-function mutations of TP53 in all cases (Fig. 1A). In addition, 93% of the lesions harbored either a CDKN2A/B mutation (68%) or deletion (25%), or both (46%), while 7% of the 28 cases showed even a biallelic loss. In comparison, other skin tumors (SCC, BCC, CM) showed a significantly lower mutation frequency of CDKN2A/B (Fisher exact test; PDS vs. SCC: |$P\ = \ 8\ \times \ {10^{ - 5}}$|⁠; PDS vs. BCC: |$P\ = \ 3\ \times \ {10^{ - 20}}$|⁠; PDS vs. CM: |$P\ = \ 7\ \times \ {10^{ - 12}}$|⁠). Further significantly mutated genes in PDS also included DNHD1, GNAS, RTN1, RTL1, ZBTB7A, NCKAP5L, and FAM200A. All of these genes were also found to be mutated in SCC tumors, but at a significantly different distribution (Wilcoxon matched-pairs signed-rank test; |$P\ = \ 5\ \times \ {10^{ - 4}}$|⁠). Both, BCC and CM showed barely any mutations in these genes (Fig. 1A). Furthermore, we found a recurrent p.R72K mutation affecting DDX31 in five cases, which was confirmed by dideoxy sequencing (Supplementary Fig S2A). In an independent validation cohort of PDS, we found one of 25 cases harboring the DDX31^R72K mutation. The recurrent mutation DDX31^R72K was also found in three of 68 SCC tumors (4.4%), as well as in BCC (1%) and CM (0.17%; Supplementary Fig S2B). As this mutation occurs outside of a known functional domain, its biological consequence remains elusive.

We further identified alterations of genes regulating the PDGFR pathway. These included amplifications of PDGFRA/KIT in three cases and mutations in one sample affecting the kinase domain of KIT (Supplementary Table S1). Additional recurrent copy-number alterations in PDS included the amplification of 2p25.3 (Fig. 1B), harboring TRAPPC12. TRAPPC12 mRNA expression was associated with a significantly higher gene expression in tumors with 2p25.3 amplifications, when compared with unaltered cases (⁠|$P\ = \ 0.0236$|⁠; Supplementary Fig. S3A). In addition, high gene expression levels of TRAPPC12 were associated with worse survival in CM available in the The Cancer Genome Atlas dataset (Supplementary Fig. S3B). In addition to homozygous deletions in CDKN2A/B in two cases, we found recurrent deletions in 8p23.3-4 (Fig. 1B).

By investigating the mutational signatures of PDS, we found both UV-induced signatures 7a and 7b (Fig. 1A). In addition, three cases of PDS revealed signature 44, which has been associated with defective DNA mismatch repair (Supplementary Fig. S3C). To further investigate the similarities and differences between other UV-induced cutaneous malignancies, we compared the mutational signatures of PDS, SCC, BCC, and CM (Fig. 1A). While signature 44 was more prominent in SCC, both signatures 7a and 7b were found at a similar proportion in PDS. Neither SCC, BCC nor CM showed a similar presence of both UV-induced signatures 7a and 7b in our analysis.

Of the selected genes shown in Fig. 1A, PDS shared the most similarity with SCC. Therefore, we performed transcriptome sequencing of SCC (n = 6) and PDS (n = 21) to assess differences in their gene expression levels (Supplementary Table S2). By hierarchical clustering of the gene expression profile, we clearly could confirm a separation of both entities (Fig. 2A; Supplementary Fig. S4A). To further show that PDS is a distinct entity with a defined mutational landscape in comparison with other skin malignancies (BCC, CM, and SCC), we analyzed local mutation rate patterns. It has recently been shown that mutation rate patterns can be used to accurately predict the tissue type of the respective tumor (15, 17). Therefore, we adapted the concept of local mutation rate variation to WES data, which then allowed us to compare a variety of acquired somatic mutations of PDS tumors between other skin malignancies. We confirmed that PDS indeed clustered as a distinct entity, separating from tumors of epithelial origin (SCC, BCC) and neural crest originated entities (CM; Fig. 2B). As the cluster of SCC was adjacent to PDS (Fig. 2B), we then performed a differential gene expression analysis of these two entities, followed by a gene set enrichment analysis of differentially and highly expressed genes (⁠|$P\$|<|$\ 0.05;{\rm{\ adjusted\ for\ multiple\ testing}}$|⁠). Using the ARCHS tissue-specific gene sets this analysis yielded a mesenchymal, fibroblastic differentiation of PDS, in contrast to the epithelial SCC (Fig. 2C). To further validate these findings, we performed NanoString of a subset of samples used for transcriptome sequencing due to tissue limitations (SCC: n = 6; PDS: n = 10; Supplementary Fig. S4B–S4D). This analysis confirmed the previous finding of the mesenchymal differentiation of PDS.

To directly characterize the differentiation of PDS, we made use of the large variation of tumor purity across the samples (Supplementary Fig. S4E) to extract tumor-specific genes from mRNA expression data. To this end, we ranked each gene by its correlation between expression and tumor purity and selected statistically significant genes (⁠|$P\ >\ 0.05$|⁠; adjusted for multiple testing) for gene set enrichment analysis. We found that fibroblasts showed the highest enrichment score (Fig. 3A). Altogether, these results strongly support the hypothesis that PDS originate from fibroblasts.

To find potential linage-specific genes in PDS, we repeated the same procedure from the previous paragraph, but used the ARCHS gene set for kinase coexpression in the enrichment analysis. This yielded PDGFRA/B as being the most enriched kinase (Fig. 3B). Furthermore, we could confirm this result in our NanoString dataset (Supplementary Fig. S4D). To show that this difference is also seen at the protein level, we stained for PDGFRA/B. While all PDS lesions (n = 28) were positive for PDGFRB (Fig. 3C and E), only a fraction of cases showed PDGFRA expression (Supplementary Fig. S4G). The universal PDGFRB expression was further validated in an independent cohort of 13 additional PDS tumors (Fig. 3E). In contrast, all tested SCC tumors (n = 85) were negative for PDGFRB (Fig. 3D and E), including five cases of undifferentiated SCC (Supplementary Fig S4F). This strongly suggests that PDGFRB could be used as a linage marker to specifically discriminate SCC from PDS.

The high mutational burden of PDS suggests a potential susceptibility to immunotherapeutic approaches. Thus, patients with advanced stages of PDS might benefit from treatment with programmed cell death 1 (PD1)/programmed cell death ligand 1 (PD-L1) inhibitors, such as pembrolizumab, nivolumab, the anti–CTLA-4 antibody ipilimumab, or a combination of these agents. While two of our patients treated with pembrolizumab still remain in remission (patient No. 1, complete response, 21-month follow-up; patient No. 2, complete response, 4-month follow-up), another individual with locally progressed PDS responded well (patient No. 3, local tumor meltdown after single infusion) to the same treatment (Supplementary Fig. S5). Collectively, these three cases suggest a high efficacy of immune checkpoint blockade in advanced stage PDS. Furthermore, we quantified CD4- and CD8-positive T cells, for marginal and central tumor zones, as well as PD-L1 expression on tumor cells (Fig. 4A–C). This analysis revealed only six cases with low amounts of both CD4/CD8-positive cells and low PD-L1 expression (immunologically cold tumors), while the remaining 22 cases showed either inflamed or immunogenic characteristics (immunologically hot tumors). We could not detect a significant difference of TMB between immunologically hot and cold tumors (⁠|$P\ = \ 0.7983$|⁠; Fig. 4C). A further differential gene expression analysis between immunologically hot and cold tumors revealed upregulation of TIGIT in the immunologically hot tumors (Fig. 4D). In addition, we found a substantial correlation between TIGIT and CD8A (Fig. 4E).

Besides TP53 mutations in all PDS cases analyzed (n = 28), we found alterations of CDKN2A/B in about 90% of the cases (Fig. 1A). Loss of CDKN2A/B can be detected in many cancer entities and has been associated with disease progression in SCC and CM (24–27). It has been shown that tumors with a loss of CDKN2A/B may benefit from CDK4/6 inhibitors, such as palbociclib, abemaciclib or ribociclib, all approved for the treatment of metastasized breast cancer (24, 28–30). Further therapeutically relevant alterations in PDS included amplifications of PDGFRA [with confirmed PDGFRA expression on protein level (Supplementary Fig. S4F)] and mutations within the kinase domain of KIT (Fig. 1A). Clinically, several drugs have proven to induce long-term remissions in PDGFR-expressing cancers, such as gastrointestinal stromal tumors, dermatofibrosarcoma protuberans, or myeloid malignancies (31–35).

Recently, it has been demonstrated that variations of the local mutation rate are specific to the cell of origin (15, 17). By applying this method to a collection of PDS, SCC, CM, and BCC tumors, we could show that PDS constitutes a distinct entity that is compatible with a fibroblast differentiation (Figs. 2B, C, and 3A). Moreover, PDGFRB appeared uniformly expressed in PDS (n = 41) but lacked expression in all well-differentiated and undifferentiated SCC tumors (n = 85; Fig. 3C–E). Therefore, PDGFRB could specifically discriminate between PDS and SCC, implying its diagnostic potential as a future linage marker.

High (TMB) has been associated with favorable response to immune checkpoint blockade (18). While two of our patients treated with pembrolizumab (19) still remain in remission, a third individual showed response after treatment with an anti-PD1 antibody (Supplementary Fig. S5). Interestingly, we did not observe any difference in the mutational burden of PDS tumors after classifying them into immunologically hot and cold tumors, based on CD8 infiltration and PD-L1 expression (Fig. 4C). Therefore, additional immunogenic markers, including PD-L1 and CD8, might be necessary to identify inflamed PDS tumors that might be susceptible for immune checkpoint inhibition. Furthermore, we also observed deleterious PTEN mutations in three cases (Fig. 1A), which have been associated with intrinsic resistance to immune checkpoint inhibitors in other entities (20–22)—reasoning to assess these alterations prior to potential immunotherapeutic interventions in PDS. Two of our three cases that showed complete response to pembrolizumab were also characterized using WES and image analysis. These two patients could be assigned to the “immunologically hot category,” while no PTEN mutations were detected.

In addition, we found a strong correlation between CD8A and TIGIT in PDS (Fig. 4E). This may further provide a rational to explore an additional treatment option in advanced PDS, as TIGIT is currently being investigated as novel immune checkpoint in clinical trials (23).

In summary, we performed the first comprehensive characterization of PDS and found that these tumors have an extremely high mutational load. Our analyses revealed a strong indication that PDS shows a fibroblastic differentiation with PDGFRB as a specific linage-specific marker. Furthermore, PDS showed a high susceptibility to immune checkpoint blockade, which is supported by the treatment response to an anti–PD-1 antibody (pembrolizumab) in three cases. Finally, our results pinpoint TIGIT as a potential novel target to treat advanced-stage PDS.

H.C. Reinhardt reports grants from Deutsche Forschungsgemeinschaft and German Ministry for Education and Research during the conduct of the study. No potential conflicts of interest were disclosed by the other authors.

S. Klein: Conceptualization, resources, data curation, software, formal analysis, supervision, funding acquisition, validation, investigation, visualization, methodology, writing-original draft, project administration, writing-review and editing. A. Quaas: Conceptualization, formal analysis, investigation, methodology, writing-original draft. K.-W. Noh: Formal analysis, validation. M. Cartolano: Data curation, software. N. Abedpour: Data curation, software, formal analysis. C. Mauch: Conceptualization, investigation. J. Quantius: Data curation, investigation. H.C. Reinhardt: Resources, writing-original draft. R. Buettner: Conceptualization, formal analysis, supervision, project administration. M. Peifer: Conceptualization, resources, data curation, software, formal analysis, supervision, investigation, visualization, methodology, writing-original draft, writing-review and editing. D. Helbig: Conceptualization, validation, writing-original draft.

We thank Wiebke Jeske and Susann Zupp for technical assistance performing the TMA and IHA staining.

This work was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation: SFB829 to D. Helbig and C. Mauch; SFB1399 to M. Peifer, R. Buettner, A. Quaas, and H.C. Reinhardt), the German Cancer Aid (Mildred-Scheel Professorship to M. Peifer), German Ministry for Education and Research (BMBF e:Med program: 01ZX1901 to M. Peifer and H.C. Reinhardt), and the Else Kröner-Fresenius Stiftung (2016_Kolleg.19 to S. Klein).

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