Desmoplastic melanoma is a rare subtype of melanoma characterized by dense fibrous stroma, resistance to chemotherapy and a lack of actionable driver mutations, and is highly associated with ultraviolet light-induced DNA damage1. We analysed sixty patients with advanced desmoplastic melanoma who had been treated with antibodies to block programmed cell death 1 (PD-1) or PD-1 ligand (PD-L1). Objective tumour responses were observed in forty-two of the sixty patients (70%; 95% confidence interval 57–81%), including nineteen patients (32%) with a complete response. Whole-exome sequencing revealed a high mutational load and frequent NF1 mutations (fourteen out of seventeen cases) in these tumours. Immunohistochemistry analysis from nineteen desmoplastic melanomas and thirteen non-desmoplastic melanomas revealed a higher percentage of PD-L1-positive cells in the tumour parenchyma in desmoplastic melanomas (P = 0.04); these cells were highly associated with increased CD8 density and PD-L1 expression in the tumour invasive margin. Therefore, patients with advanced desmoplastic melanoma derive substantial clinical benefit from PD-1 or PD-L1 immune checkpoint blockade therapy, even though desmoplastic melanoma is defined by its dense desmoplastic fibrous stroma. The benefit is likely to result from the high mutational burden and a frequent pre-existing adaptive immune response limited by PD-L1 expression.
Desmoplastic melanoma (DM) accounts for fewer than 4% of melanomas. It is characterized histologically by spindle-shaped melanoma cells within abundant collagenous stroma with scattered lymphoid aggregates, and typically has a high mutational burden from ultraviolet light radiation-induced damage1. Anti-PD-1 antibodies have been approved in many countries for the treatment of advanced melanoma, and have an overall response rate of 33–40%2. As recognition of neoantigens that result from somatic non-synonymous mutations is associated with improved clinical responses to anti-PD-1 and anti-PD-L1 therapy3,4,5,6, we hypothesized that patients with DM might respond well to anti-PD-1 or anti-PD-L1 therapies, owing to their high mutational load.
We conducted a retrospective review of the pathology reports from 1,058 patients with advanced melanoma treated with anti-PD-1 or anti-PD-L1 immunotherapies between 2011 and 2016 at ten international sites with high-volume melanoma clinical trials. We identified 60 patients with advanced, unresectable DM who received PD-1 or PD-L1 blockade therapy (Extended Data Tables 1, 2). Thirty-five patients (58%) had extra-pulmonary visceral metastases or elevated lactate dehydrogenase (M1c disease), which are recognized makers of poor prognosis7. Local pathologists reported histological sub-classification into pure (n = 25), mixed (n = 30) or indeterminate (n = 5) DM subtypes8. All cases had the distinctive diagnostic features of DM with abundant connective tissue surrounding the tumour cells, which can be highlighted by Masson’s trichrome stain (examples in Fig. 1a, with the collagenous stroma stained in blue). Central review of haematoxylin and eosin-stained tissue from 34 cases by two pathologists revealed that 65% of cases had a substantial fibrous stroma (graded 2–3), and that 63% of cases had lymphoid aggregates within the tumour and/or at the tumour stromal interface (graded 1–3) (Supplementary Table 1). Forty-two patients (70%) had progressed after prior systemic treatment, most frequently with the cytotoxic T lymphocyte antigen-4 (CTLA-4) blocking antibody ipilimumab (Extended Data Table 1 and Supplementary Table 1). The most frequently administered anti-PD-1 or anti-PD-L1 drug was pembrolizumab (in forty-five patients (75%)), while eight (13%) received nivolumab, three (5%) the anti-PD-L1 antibody BMS-936559, and an additional three (5%) received a combination of nivolumab or pembrolizumab with ipilimumab.
With a median follow up of 22 months, 42 out of the 60 patients (70%, 95% Clopper–Pearson confidence interval of 57–81%) had an objective response by RECIST 1.1 criteria (Fig. 1b, c). This included 19 (32%) complete responses and 23 (38%) partial responses; nine patients with a partial response eventually showed tumour progression but none of the patients with complete response did. When the four patients treated with a combination of anti-PD1 drugs and ipilimumab were excluded, responses were seen in 38 out of 56 (68%) patients. Three patients with isolated progression (including two who had a partial response) underwent surgery and subsequently had no evidence of melanoma with ongoing follow up for more than 1.8, 5.2, and 5.3 years. Median progression-free survival and overall survival have not been reached, with an estimated two-year overall survival of 74% (95% confidence interval 60–84%) (Extended Data Fig. 1a, b). For patients censored in the Kaplan–Meier curve, median follow-up was 27 months or more. There were no statistically significant differences in either objective response rate (65% versus 70%), or overall survival between patients with the two histological subtypes of DM (pure or mixed). There was also no difference in objective responses based on degree of fibrosis or presence of lymphoid aggregates (Supplementary Table 1).
Whole-exome sequencing from 17 cases in our DM cohort revealed more than 82% C>T transitions as part of a strong signature of ultraviolet light-induced DNA damage that is common to cutaneous melanoma1,9 (Extended Data Fig. 2a, b). There was no difference in mutational load between locally advanced and metastatic lesions (Extended Data Fig. 3a). Mutations in NF1 in the absence of BRAF or RAS family hotspot mutations were the most common driving genetic event (82.4%, 14 of 17 samples), along with enrichment for loss-of-function mutations in TP53 and ARID2 (Fig. 2a, Extended Data Fig. 3b), similar to previously published series of DM1,10. These features are also characteristic of NF1 subtype melanoma, which comprises 8–12% of cutaneous melanoma cases in large cohorts and has more than double the mutational load of the NRAS, BRAF or triple wild-type subtypes11,12,13. Our DM series had similar mutational load to NF1 subtype cases (regardless of histological classification) in a combined series from two reports of patients with anti-PD-1-treated advanced melanoma14,15 and in data from the Cancer Genome Atlas (TCGA)13. In all three series, NF1 mutated cases had a substantially greater mutational load than the non-NF1 subtypes, but there was no difference in response to PD-1 blockade (Fig. 2b). Patients with DM that did not respond (n = 5) showed no difference in mutational load compared with patients that did respond (rank sum P = 0.87, Fig. 2b). This is consistent with the findings of two previous anti-PD-1-treated cohorts14,15 but not with data from patients with melanoma treated with anti-CTLA416 (Extended Data Fig. 3c) or patients with lung and bladder cancer treated with anti-PD-1 or anti-PD-L1 therapy3,6. We did not find any genes that were mutated more frequently in patients with DM with or without response to therapy (Extended Data Fig. 4a), including when performing specific analyses for potential detrimental mutations in the interferon receptor pathway or B2M that may result in innate or acquired resistance to anti-PD-1 therapy14,17 (Extended Data Fig. 4b).
We evaluated whether the presence of CD8+ T cells and PD-L1 in DM was associated with response to anti-PD1 or anti-PD-L1 therapy18,19 using 19 available pre-treatment DM tumour biopsies compared to 13 non-DM samples (seven with a complete or partial response, six with progressive disease) using digital quantitative immunohistochemistry (IHC). We used S100 expression to define the invasive tumour margin (stromal-tumour edge) and inside tumour parenchyma (tumour centre) (examples in Extended Data Fig. 5f.). Overall, biopsies from patients with DM had a notably higher percentage of PD-L1 positive cells in the tumour parenchyma than non-DM cases (P = 0.04, Fig. 3a), confirming the same finding from primary DM lesions20. There were no significant differences in the density of CD8+ cells in the tumour parenchyma, or of CD8+ and PD-L1+ cells in the invasive margin (P = 0.12, P = 0.41 and P = 0.16, respectively; Fig. 3b–d). Consistent with previous observations18, the strongest correlation with clinical benefit (defined as having a complete or partial response, or prolonged stable disease for more than 12 months) was baseline density of CD8+ T cells in the invasive margin in non-DM melanoma (P = 0.002, Extended Data Fig. 6a–d).
In DM samples, PD-L1 expression in the tumour parenchyma was significantly associated with CD8 density (P = 0.007) and PD-L1 expression in the invasive margin (P = 0.0003), but not with CD8 density inside the tumour parenchyma (P = 0.15, Extended Data Fig. 7). Similarly, PD-L1 expression in the invasive margin was significantly associated with CD8 density in the invasive margin (P = 0.0003), CD8 density in the tumour parenchyma (P = 0.04), and PD-L1 expression in the tumour parenchyma (P = 0.0003). Among DM cases for which we had exome sequencing, we did not detect many of the genetic mechanisms reported to cause constitutive PD-L1 expression, including amplification of the PD-L1–PD-L2–JAK2 (PDJ) locus, mutations or amplification of MYC or EGFR, or disruption of CDK521,22,23,24. The 3′ UTR of CD274 (encoding PD-L1) was not well captured in our exome sequencing, and disruption could not be assessed25. Therefore, the higher PD-L1 expression in DM is likely to result from a reactive response to CD8+ T cell infiltrates that reflect adaptive immune resistance26.
We noted five distinct patterns of CD8+ cell infiltration and PD-L1 expression in the invasive margin and tumour parenchyma; most patients who responded to therapy had one of the three patterns characterized by high CD8+ T cells (twelve out of fourteen with DM and six out of seven with non-DM; Extended Data Fig. 5a–e). Patients without a tumour response tended to have low CD8+ cells regardless of the status of PD-L1 (Extended Data Fig. 5g), although a small number of patients (two out of nine) whose tumours had low baseline CD8+ infiltrates responded to therapy. We integrated the data regarding CD8 and PD-L1 expression in biopsies with response and mutational load, allowing cases of DM and non-DM to self-organize on the basis of these data (Extended Data Fig. 8a and b). CD8 and PD-L1 levels did not differ between cases with pure or mixed DM histology (Extended Data Fig. 8b). Biopsies in which the invasive margin showed higher CD8+ density clustered together, usually with higher PD-L1 expression both in the tumour and in the invasive margin, and were enriched in patients with an objective tumour response. Mutational load, which was relatively high in all these cases, did not cluster with any particular pattern of CD8 or PD-L1 expression, or with response to therapy.
Dense collagenous stroma as found in DM has been thought to be an important limitation for immune infiltration, as has been described for pancreatic cancer27. However, our data challenge this notion, as there are pre-existing T cell infiltrates in the invasive edge of DM lesions, and DMs show a much higher response rate to anti-PD1 therapy than any other subtype of melanoma. The response rate of 70% in DM, together with relapsed Hodgkin’s disease and Merkel cell carcinomas21,28, is among the highest responses to single agent PD-1 blockade therapy in any pathologically defined cancer. Our data suggest that DM, and probably the non-DM NF1 subtype arising from sun-exposed areas, have a high response rate to PD-1 blockade therapy because they have a more dynamic pre-existing adaptive immune response.
Analysis of clinical data
To conduct this retrospective analysis, records of 1,058 patients with advanced melanoma treated with anti-PD-1 or anti-PD-L1 therapy were reviewed across ten institutions to identify those with a diagnosis of DM. Each institution conducted its own search to find patients who fit these criteria. The study was conducted under Institutional Review Board approval at each centre and complied with all relevant ethical regulations. All patients had signed a local written informed consent form for research analyses. Consent to obtain photographs was obtained. No statistical methods were used to predetermine sample size. The experiments were not randomized.
Immunohistochemistry (IHC) analyses
Patients were selected for IHC analysis if they had adequate pre-treatment tumour samples and had signed a local written informed consent form for research analyses. Tumour samples were obtained from eight institutions. Slides cut from frozen or FFPE tissue samples were stained with haematoxylin and eosin, Masson’s trichrome stain, or anti-S100, anti-CD8, and anti-PD-L1 at the UCLA Anatomic Pathology Immunohistochemistry and Histology Laboratory (CLIA-certified). Antibodies used included rabbit polyclonal S100 (DAKO, 1:1,000 dilution, low pH retrieval), CD8 clone C8/144B (Dako, 1:100, low pH retrieval), and PD-L1 (Spring Biosciences, Sp142, 1:200, high pH retrieval). IHC was performed on Leica Bond III autostainer using Bond ancillary reagents and a Refine Polymer Detection system. Slides were examined for the presence of CD8 and PD-L1 within the tumour parenchyma and the connective tissue surrounding the tumour (invasive margin). We defined the invasive margin (or leading edge) as the interfaces between individual tumour bundles and the fibrotic regions, as opposed to the intra-tumour staining, which is within the capsule of individual tumours. All slides were scanned at an absolute magnification of ×200 (resolution of 0.5 μm per pixel). An algorithm was designed based on pattern recognition that quantified immune cells within S100-positive areas (tumour) and S100-negative areas (invasive margin). The algorithm calculated the percentage cellularity (% positive cells/all nucleated cells) using the Halo platform (Indica Labs). This analysis system was not able to differentiate between tumour cell or infiltrating immune cell PD-L1 staining30. Immunohistochemical variables were compared between biopsies of patients who responded or progressed on treatement using the Wilcoxon Mann–Whitney test.
Lymphocytic infiltrate and fibrosis analysis
We analysed available pathological samples from 34 cases to define their lymphoid inflammation and degree of fibrosis. There is no quantitative measure for these readouts, so we used a semiquantitative pathological assessment. Examples of each grade were circulated to pathology reviewers to ensure reproducibility. The investigators were not blinded to allocation during experiments and outcome assessment. When available, metastatic lesions were graded by the same schema as primary samples, as not all patients had primary tumour samples available for quantification. The hallmark of lymphoid infiltration in DM is the presence of lymphoid nodules within and occasionally surrounding the tumour. Therefore, we developed the grading schema below to describe the location of these nodules within the tumours:
0: no lymphoid aggregates
1: lymphoid aggregates within tumour
2: lymphoid aggregates at tumour–stroma interface
3: lymphoid aggregates within tumour and at tumour–stroma interface
A grading schema was also developed to describe the degree of fibrosis in tumours:
0: no significant stroma separates tumour cells
1: mild increase in fibroblasts and/or myxoid stroma separates tumour cells
2: moderate increase in fibroblasts and/or myxoid stroma separates tumour cells
3: tumour cells separated by abundant fibromyxoid stroma
In brief, whole-exome sequencing was performed at the UCLA Clinical Microarray Core using the Roche Nimblegen SeqCap EZ Human Exome Library v3.0 targeting 65 Mb of genome. Mutation calling was performed as previously described14,17. Out of 22 biopsies of DM sequenced, 17 cases (3 complete responses, 8 partial responses, 1 stable disease, 5 progressive disease) could be analysed by meeting quality control criteria for minimum coverage (50× tumour, 30× normal), tumour content (10%), and effective depth (coverage multiplied by tumour content >12×, representing >80% probability to detect heterozygous mutations with at least four reads). These were compared with exome sequencing from the TCGA13, a prior DM cohort1, and two anti-PD-1 monotherapy-treated cohorts, one from our group14 with 23 cases which included a mix of responders and non-responders, and the second a subset of 30 patients after non-response to CTLA-415. From that cohort to include one sample per patient, we excluded on-treatment samples in the setting of response; then we selected the biopsy with the highest tumour purity, regardless of time point, since most patients with more than one biopsy had <10% variance in their mutational loads. Response was defined as CR, PR, or SD for >12 months by RECIST1.1 in both cohorts. Mutation calling methods between cohorts all used MuTect at their core, and only non-synonymous mutations (Nonsense, Missense, Splice_Site, Frameshift indels, In-frame indels, Start_Codon indels or SNPs, and Stoploss/Nonstop variants) were assessed to minimize differences between exon-capture kits. An additional filter was applied to all data sets to exclude mutations at sites of known germline variation with an allele frequency >0.0005 in the Exome Aggregation Consortium (ExAC) database v0.3.1. Tumour purity was estimated by Sequenza, or as 2 × median variant allele frequency if less than 30%. Loss-of-function burden was determined using the LOF SIgRank algorithm1, with the simulation run for 1,000 iterations and synonymous mutations for background mutation rate defined as silent, 3′UTR, 5′UTR, or exon-flanking intronic mutations. Single nucleotide variants and their flanking contexts were analysed for mutation signatures for the DM and UCLA non-DM14 cohorts together using a published tool9.
The Kaplan–Meier method and Greenwood’s formula were used to estimate survival probabilities (survival rates and overall survival) and the corresponding 95% confidence intervals (CIs). Progression-free survival was defined from start of treatment to disease progression or death from any cause. Overall survival was defined from start of treatment to death from any cause. The objective response rate was reported as proportion along with Clopper–Pearson exact CIs. The chi-square and Fisher’s exact tests were used to test for differences between groups for categorical variables. The Wilcoxon Mann–Whitney rank sum test was used to compare mutation rates between groups. Statistical analyses of the pathological data were performed using GraphPad Prism and mutation data using R v3.2.5. All tests were two-sided; P values <0.05 were considered statistically significant.
Whole-exome sequencing data has been deposited in the National Center for Biotechnology Information (NCBI) dbGaP (https://www.ncbi.nlm.nih.gov/gap) with accession number phs001469. All other data are available from the authors on reasonable request.
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This study was funded in part by the Grimaldi Family Fund, the Parker Institute for Cancer Immunotherapy, National Institutes of Health (NIH) grants R35 CA197633 and P01 CA168585, the Ressler Family Fund, the Samuels Family Fund and the Garcia-Corsini Family Fund (to A.R.). Z.E. was supported in part by the Moffitt Cancer Center NCI Skin SPORE (5P50CA168536) and Moffitt’s Total Cancer Care Initiative and Collaborative Data Services (P30-CA076292) for this work. J.M.Z. is part of the UCLA Medical Scientist Training Program supported by NIH training grant GM08042. S.H.-L. was supported by a Young Investigator Award and a Career Development Award from the American Society of Clinical Oncology (ASCO), a Tower Cancer Research Foundation Grant, and a Dr. Charles Coltman Fellowship Award from the Hope Foundation. We acknowledge the Translational Pathology Core Laboratory (TPCL) and R. Guo, W. Li, J. Pang and M. H. Macabali from UCLA for blood and biopsy processing, and X. Li, L. Dong, J. Yoshizawa, and J. Zhou from the UCLA Clinical Microarray Core for sequencing expertise. G.V.L. is supported by an NHMRC Fellowship and The University of Sydney Medical Foundation. R.A.S. is supported by an NHMRC Fellowship.
The authors declare no competing financial interests.
Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
a, Progression-free survival (PFS), n = 60, median not reached. b, Overall survival (OS), n = 60, median not reached.
a, Analysis of mutational load in samples obtained from primary locally advanced cases and metastatic lesions. Two-sided Wilcoxon Mann–Whitney rank sum test, P = 0.16 (95% CI, −171 to 1,175). b, Scores from the loss-of-function (LOF) SigRank algorithm1 show enrichment for LOF mutations (nonsense, frameshift, splice-site or damaging missense) compared to the expected number based on the rate of LOF mutations in the cohort. Solid line corresponds to observed/expected ratio of 1.0. c, Mutational load in the vanAllen16 anti-CTLA4 treated cohort separated by driver subtype and coloured by response. In the box plots, line is median, box is 25th to 75th percentile, whiskers show highest and lowest values within 1.5 × interquartile range.
Extended Data Figure 4 Mutations in antigen-presenting machinery or enriched by response in the DM cohort.
a, Mutations in genes enriched in responders (n = 12) (blue) or non-responders (n = 5) (red). Shown are genes with P < 0.05 by unadjusted two-sided Fisher’s exact test of samples with or without a non-synonymous mutation between responders and non-responders. None were significant after false-discovery rate adjustment. b, Mutations in antigen-presenting machinery genes. Tiling plot shows mutations in a given gene (rows) per sample (columns). Colour indicates mutation type, with truncating mutations (frameshift, nonsense, splice-site) in red, missense in green. Darker colour intensity indicates potentially homozygous mutations, with variant allele frequency more than 1.5 times the sample median.
Extended Data Figure 5 Patterns of CD8 infiltration and PD-L1 expression in biopsies from patients with DM and non-DM tumours.
a–e, Using cut off of >10% for high CD8 density in either parenchyma or invasive margins and >15% for high PD-L1 expression, five different patterns were identified. a, High CD8 density, high PD-L1 in tumour parenchyma higher than in invasive margins. b, High CD8 density, high PD-L1 in invasive margins higher than in tumour parenchyma. c, High CD8 density, high PD-L1 in the invasive margins only. d, Low CD8 density, high PD-L1. e, Low CD8 density, low PD-L1 expression. f, Yellow lines delineate the edges of tumour regions determined by positive S100 staining. Green or red lines mark the invasive margins around the tumour edges. All analysis was done with HALO software (Indica Labs). g, Heat map summary of patterns of CD8 and PD-L1 expression in biopsies from patients with DM and CM, based on their response to anti-PD-1 or anti-PD-L1 treatment. Intensity of colour coding indicates number of cases in each category. All calculations were based on scanned whole tumour images.
Extended Data Figure 6 CD8 density and PD-L1 expression in the tumour parenchyma and invasive margins in biopsies of patients with DM and non-DM tumours.
a, CD8 staining in the invasive margin. b, PD-L1 staining in the invasive margin. c, CD8 staining in the tumour centre. d, PD-L1 staining in the tumour centre. The percentage of positively stained cells in all nucleated cells is shown. CB, clinical benefit; PD, progressive disease. All calculations used two-sided Mann–Whitney rank sum test. See Supplementary Table for all statistical analyses. Asterisk indicates statistical significance. Tumour, tumour centre.
Extended Data Figure 7 Correlation between CD8 and PD-L1 in the invasive margin or tumour parenchyma in DM.
Black squares represent a sample from a patient who had a good response in the lesion biopsied (analysed) but was found to have brain metastasis shortly after treatment started. See Supplementary Table for further statistical analyses. IM, invasive margin.
Extended Data Figure 8 Hierarchical clustering of cases of DM and non-DM based on CD8 and PD-L1 expression in the invasive margin and tumour parenchyma.
a, Non-desmoplastic cutaneous melanomas (n = 13), with the y axis colour coded for response and mutational load. b, Desmoplastic melanomas (n = 19), with the additional information of differentiation between pure (red) and mixed (blue) histology on the y axis. For mutational load, darker squares correspond to higher mutational load. Gray squares are missing data points.
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Eroglu, Z., Zaretsky, J., Hu-Lieskovan, S. et al. High response rate to PD-1 blockade in desmoplastic melanomas. Nature 553, 347–350 (2018). https://doi.org/10.1038/nature25187
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