S1­Leitlinie Talgdrüsenkarzinom: S1­Guideline Sebaceous Carcinoma.

Sebaceous gland carcinomas are rare malignant cutaneous adnexal tumors with sebocytic differentiation. The typical predilection area is the head and neck region, where sebaceous gland carcinomas are the most common malignant adnexal tumors of the skin. According to their localization a distinction is made between periocular and extraocular sebaceous gland carcinomas. Muir-Torre syndrome (MTS) should always be ruled out if it is suspected. In terms of prognosis, sebaceous gland carcinomas are potentially aggressive tumors with a clear tendency to recur and metastasize. Only small extraocular sebaceous gland carcinomas that have been completely resected have a very good prognosis. Sebaceous gland carcinomas most frequently metastasize lymphogenously to regional or distant lymph nodes; organ metastasis occurs less frequently. Periocular sebaceous gland carcinomas have a higher metastasis rate (up to 15%) than extraocular sebaceous gland carcinomas (up to 2%). Complete micrographically controlled surgery (MCS) of the primary tumor is the therapy of first choice, regardless of periocular or extraocular localization. Adjuvant or therapeutic radiotherapy may be considered. There is currently no established standard therapy for advanced, inoperable or metastatic sebaceous gland carcinomas. Local procedures and system therapies such as chemotherapy or immunotherapy can be considered. The procedure should be determined individually in an interdisciplinary tumor board. Close follow-up care is recommended for these potentially aggressive carcinomas.

S1-Guideline Sebaceous Carcinoma.

Sebaceous gland carcinomas are rare malignant cutaneous adnexal tumors with sebocytic differentiation. The typical predilection area is the head and neck region, where sebaceous gland carcinomas are the most common malignant adnexal tumors of the skin. According to their localization a distinction is made between periocular and extraocular sebaceous gland carcinomas. Muir-Torre syndrome (MTS) should always be ruled out if it is suspected. In terms of prognosis, sebaceous gland carcinomas are potentially aggressive tumors with a clear tendency to recur and metastasize. Only small extraocular sebaceous gland carcinomas that have been completely resected have a very good prognosis. Sebaceous gland carcinomas most frequently metastasize lymphogenously to regional or distant lymph nodes; organ metastasis occurs less frequently. Periocular sebaceous gland carcinomas have a higher metastasis rate (up to 15%) than extraocular sebaceous gland carcinomas (up to 2%). Complete micrographically controlled surgery (MCS) of the primary tumor is the therapy of first choice, regardless of periocular or extraocular localization. Adjuvant or therapeutic radiotherapy may be considered. There is currently no established standard therapy for advanced, inoperable, or metastatic sebaceous gland carcinomas. Local procedures and systemic therapies such as chemotherapy or immunotherapy can be considered. The procedure should be determined individually by an interdisciplinary tumor board. Close follow-up care is recommended for these potentially aggressive carcinomas.

Deep learning based histological classification of adnex tumors.

BACKGROUND: Cutaneous adnexal tumors are a diverse group of tumors arising from structures of the hair appendages. Although often benign, malignant entities occur which can metastasize and lead to patients´ death. Correct diagnosis is critical to ensure optimal treatment and best possible patient outcome. Artificial intelligence (AI) in the form of deep neural networks has recently shown enormous potential in the field of medicine including pathology, where we and others have found common cutaneous tumors can be detected with high sensitivity and specificity. To become a widely applied tool, AI approaches will also need to reliably detect and distinguish less common tumor entities including the diverse group of cutaneous adnexal tumors. METHODS: To assess the potential of AI to recognize cutaneous adnexal tumors, we selected a diverse set of these entities from five German centers. The algorithm was trained with samples from four centers and then tested on slides from the fifth center. RESULTS: The neural network was able to differentiate 14 different cutaneous adnexal tumors and distinguish them from more common cutaneous tumors (i.e. basal cell carcinoma and seborrheic keratosis). The total accuracy on the test set for classifying 248 samples into these 16 diagnoses was 89.92 %. Our findings support AI can distinguish rare tumors, for morphologically distinct entities even with very limited case numbers (< 50) for training. CONCLUSION: This study further underlines the enormous potential of AI in pathology which could become a standard tool to aid pathologists in routine diagnostics in the foreseeable future. The final diagnostic responsibility will remain with the pathologist.

Applying an artificial intelligence deep learning approach to routine dermatopathological diagnosis of basal cell carcinoma.

BACKGROUND: Institutes of dermatopathology are faced with considerable challenges including a continuously rising numbers of submitted specimens and a shortage of specialized health care practitioners. Basal cell carcinoma (BCC) is one of the most common tumors in the fair-skinned western population and represents a major part of samples submitted for histological evaluation. Digitalizing glass slides has enabled the application of artificial intelligence (AI)-based procedures. To date, these methods have found only limited application in routine diagnostics. The aim of this study was to establish an AI-based model for automated BCC detection. PATIENTS AND METHODS: In three dermatopathological centers, daily routine practice BCC cases were digitalized. The diagnosis was made both conventionally by analog microscope and digitally through an AI-supported algorithm based on a U-Net architecture neural network. RESULTS: In routine practice, the model achieved a sensitivity of 98.23% (center 1) and a specificity of 98.51%. The model generalized successfully without additional training to samples from the other centers, achieving similarly high accuracies in BCC detection (sensitivities of 97.67% and 98.57% and specificities of 96.77% and 98.73% in centers 2 and 3, respectively). In addition, automated AI-based basal cell carcinoma subtyping and tumor thickness measurement were established. CONCLUSIONS: AI-based methods can detect BCC with high accuracy in a routine clinical setting and significantly support dermatopathological work.

Correlation of tumor PD-L1 expression in different tissue types and outcome of PD-1-based immunotherapy in metastatic melanoma - analysis of the DeCOG prospective multicenter cohort study ADOREG/TRIM.

BACKGROUND: PD-1-based immune checkpoint inhibition (ICI) is the major backbone of current melanoma therapy. Tumor PD-L1 expression represents one of few biomarkers predicting ICI therapy outcome. The objective of the present study was to systematically investigate whether the type of tumor tissue examined for PD-L1 expression has an impact on the correlation with ICI therapy outcome. METHODS: Pre-treatment tumor tissue was collected within the prospective DeCOG cohort study ADOREG/TRIM (CA209-578; NCT05750511) between February 2014 and May 2020 from 448 consecutive patients who received PD-1-based ICI for non-resectable metastatic melanoma. The primary study endpoint was best overall response (BOR), secondary endpoints were progression-free (PFS) and overall survival (OS). All endpoints were correlated with tumor PD-L1 expression (quantified with clone 28-8; cutoff ≥5%) and stratified by tissue type. FINDINGS: Tumor PD-L1 was determined in 95 primary tumors (PT; 36.8% positivity), 153 skin/subcutaneous (34.0% positivity), 115 lymph node (LN; 50.4% positivity), and 85 organ (40.8% positivity) metastases. Tumor PD-L1 correlated with BOR if determined in LN (OR = 0.319; 95% CI = 0.138-0.762; P = 0.010), but not in skin/subcutaneous metastases (OR = 0.656; 95% CI = 0.311-1.341; P = 0.26). PD-L1 positivity determined on LN metastases was associated with favorable survival (PFS, HR = 0.490; 95% CI = 0.310-0.775; P = 0.002; OS, HR = 0.519; 95% CI = 0.307-0.880; P = 0.014). PD-L1 positivity determined in PT (PFS, HR = 0.757; 95% CI = 0.467-1.226; P = 0.27; OS; HR = 0.528; 95% CI = 0.305-0.913; P = 0.032) was correlated with survival to a lesser extent. No relevant survival differences were detected by PD-L1 determined in skin/subcutaneous metastases (PFS, HR = 0.825; 95% CI = 0.555-1.226; P = 0.35; OS, HR = 1.083; 95% CI = 0.698-1.681; P = 0.72). INTERPRETATION: For PD-1-based immunotherapy in melanoma, tumor PD-L1 determined in LN metastases was stronger correlated with therapy outcome than that assessed in PT or organ metastases. PD-L1 determined in skin/subcutaneous metastases showed no outcome correlation and therefore should be used with caution for clinical decision making. FUNDING: Bristol-Myers Squibb (ADOREG/TRIM, NCT05750511); German Research Foundation (DFG; Clinician Scientist Program UMEA); Else Kröner-Fresenius-Stiftung (EKFS; Medical Scientist Academy UMESciA).

Deep learning detection of melanoma metastases in lymph nodes.

BACKGROUND: In melanoma patients, surgical excision of the first draining lymph node, the sentinel lymph node (SLN), is a routine procedure to evaluate lymphogenic metastases. Metastasis detection by histopathological analysis assesses multiple tissue levels with hematoxylin and eosin and immunohistochemically stained glass slides. Considering the amount of tissue to analyze, the detection of metastasis can be highly time-consuming for pathologists. The application of artificial intelligence in the clinical routine has constantly increased over the past few years. METHODS: In this multi-center study, a deep learning method was established on histological tissue sections of sentinel lymph nodes collected from the clinical routine. The algorithm was trained to highlight potential melanoma metastases for further review by pathologists, without relying on supplementary immunohistochemical stainings (e.g. anti-S100, anti-MelanA). RESULTS: The established method was able to detect the existence of metastasis on individual tissue cuts with an area under the curve of 0.9630 and 0.9856 respectively on two test cohorts from different laboratories. The method was able to accurately identify tumour deposits>0.1 mm and, by automatic tumour diameter measurement, classify these into 0.1 mm to -1.0 mm and>1.0 mm groups, thus identifying and classifying metastasis currently relevant for assessing prognosis and stratifying treatment. CONCLUSIONS: Our results demonstrate that AI-based SLN melanoma metastasis detection has great potential and could become a routinely applied aid for pathologists. Our current study focused on assessing established parameters; however, larger future AI-based studies could identify novel biomarkers potentially further improving SLN-based prognostic and therapeutic predictions for affected patients.

Immune checkpoint inhibition in patients with NRAS mutated and NRAS wild type melanoma: a multicenter Dermatologic Cooperative Oncology Group study on 637 patients from the prospective skin cancer registry ADOREG.

BACKGROUND: Melanomas frequently harbour somatic mutations in BRAF (40%) or NRAS (20%). Impact of NRAS mutations on the therapeutic outcome of immune checkpoint inhibitors (ICI) remains controversial. Potential correlation of the NRAS mutational status and programmed cell death ligand-1 (PD-L1) expression in melanoma is unknown. PATIENTS AND METHODS: Advanced, non-resectable melanoma patients with known NRAS mutation status treated with first-line ICI between 06/2014 and 05/2020 in the prospective multicenter skin cancer registry ADOREG were included. Overall response rate (ORR), progression-free survival (PFS), and overall survival (OS) according to NRAS status were analysed. A multivariate Cox model was used to analyse factors associated with PFS and OS; survival was analysed using the Kaplan-Meier approach. RESULTS: Among 637 BRAF wild-type patients, 310 (49%) had an NRAS mutation with Q61R (41%) and Q61K (32%). NRAS-mutated (NRASmut) melanomas were significantly more often located on the lower extremities and trunk (p = 0.001); nodular melanoma was the most common subtype (p < 0.0001). No significant differences were found for PFS and OS for anti-PD1 monotherapy (2-year PFS 39%, [95% confidence interval (CI), 33-47] in NRASmut patients and 41% [95% CI, 35-48] in NRAS-wild type (NRASwt) patients; 2-year OS was 54% [95% CI, 48-61] in NRASmut patients and 57% [95% CI, 50-64] in NRASwt patients) and anti-PD1 plus anti-CTLA4 therapy between both cohorts (2-year PFS was 54% [95% CI, 44-66] in NRASmut patients and 53% [95% CI, 41-67] in NRASwt patients; 2-year OS was 58% [95% CI, 49-70] in NRASmut patients and 62% [95% CI, 51-75] in NRASwt patients). The ORR to anti-PD1 was 35% for NRASwt patients and 26% for NRASmut patients and 34% compared to 32% for combinational therapy. Data on PD-L1 expression was available in 82 patients (13%). PD-L1 expression (>5%) was not correlated to NRAS mutational status. In multivariate analysis, elevated lactate dehydrogenase, Eastern Cooperative Oncology Group performance status ≥ 1, and brain metastases were significantly associated with a higher risk of death in all patients. CONCLUSIONS: The PFS and OS were not affected by NRAS mutational status in patients treated with anti-PD1-based ICI. Similar ORR was seen in NRASwt and NRASmut patients. Tumour PD-L1 expression did not correlate with NRAS mutational status.

HLA Class II Loss and JAK1/2 Deficiency Coevolve in Melanoma Leading to CD4 T-cell and IFNγ Cross-Resistance.

PURPOSE: Recent studies have demonstrated HLA class II (HLA-II)-dependent killing of melanoma cells by cytotoxic CD4 T cells. We investigated evolution of HLA-II-loss tumors that escape cytotoxic CD4 T-cell activity and contribute to immunotherapy resistance. EXPERIMENTAL DESIGN: Melanoma cells from longitudinal metastases were studied for constitutive and IFN-inducible HLA-II expression, sensitivity towards autologous CD4 T cells, and immune evasion by HLA-II loss. Clinical significance of HLA-II-low tumors was determined by analysis of transcriptomic data sets from patients with immune checkpoint blockade (ICB). RESULTS: Analysis of longitudinal samples revealed strong intermetastatic heterogeneity in melanoma cell-intrinsic HLA-II expression and subclonal HLA-II loss. Tumor cells from early lesions either constitutively expressed HLA-II, sensitizing to cytotoxic CD4 T cells, or induced HLA-II and gained CD4 T-cell sensitivity in the presence of IFNγ. In contrast, late outgrowing subclones displayed a stable CD4 T-cell-resistant HLA-II-loss phenotype. These cells lacked not only constitutive but also IFNγ-inducible HLA-II due to JAK1/2-STAT1 pathway inactivation. Coevolution of JAK1/2 deficiency and HLA-II loss established melanoma cross-resistance to IFNγ and CD4 T cells, as detected in distinct stage IV metastases. In line with their immune-evasive phenotype, HLA-II-low melanomas showed reduced CD4 T-cell infiltrates and correlated with disease progression under ICB. CONCLUSIONS: Our study links melanoma resistance to CD4 T cells, IFNγ, and ICB at the level of HLA-II, highlighting the significance of tumor cell-intrinsic HLA-II antigen presentation in disease control and calling for strategies to overcome its downregulation for improvement of patient outcome.

Clinical, histopathological and molecular features of dedifferentiated melanomas: An EORTC Melanoma Group Retrospective Analysis.

PURPOSE: Dedifferentiated melanoma (DedM) poses significant diagnostic challenges. We aimed to investigate the clinical, histopathological and molecular features of DedM. Methylation signature (MS) and copy number profiling (CNP) were carried out in a subgroup of cases. PATIENTS AND METHODS: A retrospective series of 78 DedM tissue samples from 61 patients retrieved from EORTC (European Organisation for Research and Treatment of Cancer) Melanoma Group centres were centrally reviewed. Clinical and histopathological features were retrieved. In a subgroup of patients, genotyping through Infinium Methylation microarray and CNP analysis was carried out. RESULTS: Most patients (60/61) had a metastatic DedM showing most frequently an unclassified pleomorphic, spindle cell, or small round cell morphology akin to undifferentiated soft tissue sarcoma, rarely associated with heterologous elements. Overall, among 20 successfully analysed tissue samples from 16 patients, we found retained melanoma-like MS in only 7 tissue samples while a non-melanoma-like MS was observed in 13 tissue samples. In two patients from whom multiple specimens were analysed, some of the samples had a preserved cutaneous melanoma MS while other specimens exhibited an epigenetic shift towards a mesenchymal/sarcoma-like profile, matching the histological features. In these two patients, CNP was largely identical across all analysed specimens, in line with their common clonal origin, despite significant modification of their epigenome. CONCLUSIONS: Our study further highlights that DedM represents a real diagnostic challenge. While MS and genomic CNP may help pathologists to diagnose DedM, we provide proof-of-concept that dedifferentiation in melanoma is frequently associated with epigenetic modifications.

Characterisation and outcome of RAC1 mutated melanoma.

BACKGROUND: Activating hot spot R29S mutations in RAC1, a small GTPase influencing several cellular processes including cell proliferation and cytoskeleton rearrangement, have been reported in up to 9% of sun-exposed melanomas. Clinical characteristics and treatment implications of RAC1 mutations in melanoma remain unclear. METHODS: We investigated the largest set (n = 64) of RAC1 mutated melanoma patients reported to date, including a retrospective single institution cohort (n = 34) from the University Hospital Essen and a prospective multicentre cohort (n = 30) from the translational study Tissue Registry in Melanoma (TRIM; CA209-578), for patient and tumour characteristics as well as therapy outcomes. RESULTS: From 3037 sequenced melanoma samples screened RAC1 mutations occurred in ∼2% of samples (64/3037). The most common RAC1 mutation was P29S (95%, 61/64). The majority of tumours had co-occuring MAP kinase mutations (88%, 56/64); mostly activating NRAS (47%, 30/64) mutations, followed by activating BRAF (28%, 18/64) and NF1 (25%, 16/64) mutations. RAC1 mutated melanomas were almost exclusively of cutaneous origin (84%, 54/64) or of unknown primary (MUP, 14%, 9/64). C > T alterations were the most frequent mutation type identified demonstrating a UV-signature for RAC1 mutated melanoma. Most patients with unresectable disease (39) received immune checkpoint inhibitors (ICI) (77%, 30/39). Objective response rate of first-line treatment in patients with stage III/IV disease was 21%; median overall survival was 47.8 months. CONCLUSIONS: RAC1 mutated melanomas are rare, mostly of cutaneous origin and frequently harbour concomitant MAP kinase mutations, particularly in NRAS. Patients with advanced disease benefit from systemic treatment with ICI.

Human Papillomavirus 42 Drives Digital Papillary Adenocarcinoma and Elicits a Germ Cell-like Program Conserved in HPV-Positive Cancers.

The skin is exposed to viral pathogens, but whether they contribute to the oncogenesis of skin cancers has not been systematically explored. Here we investigated 19 skin tumor types by analyzing off-target reads from commonly available next-generation sequencing data for viral pathogens. We identified human papillomavirus 42 (HPV42) in 96% (n = 45/47) of digital papillary adenocarcinoma (DPA), an aggressive cancer occurring on the fingers and toes. We show that HPV42, so far considered a nononcogenic, "low-risk" HPV, recapitulates the molecular hallmarks of oncogenic, "high-risk" HPVs. Using machine learning, we find that HPV-driven transformation elicits a germ cell-like transcriptional program conserved throughout all HPV-driven cancers (DPA, cervical carcinoma, and head and neck cancer). We further show that this germ cell-like transcriptional program, even when reduced to the top two genes (CDKN2A and SYCP2), serves as a fingerprint of oncogenic HPVs with implications for early detection, diagnosis, and therapy of all HPV-driven cancers. SIGNIFICANCE: We identify HPV42 as a uniform driver of DPA and add a new member to the short list of tumorigenic viruses in humans. We discover that all oncogenic HPVs evoke a germ cell-like transcriptional program with important implications for detecting, diagnosing, and treating all HPV-driven cancers. See related commentary by Starrett et al., p. 17. This article is highlighted in the In This Issue feature, p. 1.

Circulating cell-free messenger RNA enables non-invasive pan-tumour monitoring of melanoma therapy independent of the mutational genotype.

BACKGROUND: Plasma-derived tumour-specific cell-free nucleic acids are increasingly utilized as a minimally invasive, real-time biomarker approach in many solid tumours. Circulating tumour DNA of melanoma-specific mutations is currently the best studied liquid biopsy biomarker for melanoma. However, the combination of hotspot genetic alterations covers only around 80% of all melanoma patients. Therefore, alternative approaches are needed to enable the follow-up of all genotypes, including wild-type. METHODS: We identified KPNA2, DTL, BACE2 and DTYMK messenger RNA (mRNA) upregulated in melanoma versus nevi tissues by unsupervised data mining (N = 175 melanoma, N = 20 normal skin, N = 6 benign nevi) and experimentally confirmed differential mRNA expression in vitro (N = 18 melanoma, N = 8 benign nevi). Circulating cell-free RNA (cfRNA) was analysed in 361 plasma samples (collected before and during therapy) from 100 melanoma patients and 18 healthy donors. Absolute cfRNA copies were quantified on droplet digital PCR. RESULTS: KPNA2, DTL, BACE2 and DTYMK cfRNA demonstrated high diagnostic accuracy between melanoma patients' and healthy donors' plasma (AUC > 86%, p < .0001). cfRNA copies increased proportionally with increasing tumour burden independently of demographic variables and even remained elevated in individuals with radiological absence of disease. Re-analysis of single-cell transcriptomes revealed a pan-tumour origin of cfRNA, including endothelial, cancer-associated fibroblasts, macrophages and B cells beyond melanoma cells as cellular sources. Low baseline cfRNA levels were associated with significantly longer progression-free survival (PFS) (KPNA2 HR = .54, p = .0362; DTL HR = .60, p = .0349) and overall survival (KPNA2 HR = .52, p = .0237; BACE2 HR = .55, p = .0419; DTYMK HR = .43, p = .0393). Lastly, we found that cfRNA copies significantly increased during therapy in non-responders compared to responders regardless of therapy and mutational subtypes and that the increase of KPNA2 (HR = 1.73, p = .0441) and DTYMK (HR = 1.82, p = .018) cfRNA during therapy was predictive of shorter PFS. CONCLUSIONS: In sum, we identified a new panel of cfRNAs for a pan-tumour liquid biopsy approach and demonstrated its utility as a prognostic, therapy-monitoring tool independent of the melanoma mutational genotype.

Genetic and Methylation Analysis of CTNNB1 in Benign and Malignant Melanocytic Lesions.

Melanocytic neoplasms have been genetically characterized in detail during the last decade. Recurrent CTNNB1 exon 3 mutations have been recognized in the distinct group of melanocytic tumors showing deep penetrating nevus-like morphology. In addition, they have been identified in 1-2% of advanced melanoma. Performing a detailed genetic analysis of difficult-to-classify nevi and melanomas with CTNNB1 mutations, we found that benign tumors (nevi) show characteristic morphological, genetic and epigenetic traits, which distinguish them from other nevi and melanoma. Malignant CTNNB1-mutant tumors (melanomas) demonstrated a different genetic profile, instead grouping clearly with other non-CTNNB1 melanomas in methylation assays. To further evaluate the role of CTNNB1 mutations in melanoma, we assessed a large cohort of clinically sequenced melanomas, identifying 38 tumors with CTNNB1 exon 3 mutations, including recurrent S45 (n = 13, 34%), G34 (n = 5, 13%), and S27 (n = 5, 13%) mutations. Locations and histological subtype of CTNNB1-mutated melanoma varied; none were reported as showing deep penetrating nevus-like morphology. The most frequent concurrent activating mutations were BRAF V600 (n = 21, 55%) and NRAS Q61 (n = 13, 34%). In our cohort, four of seven (58%) and one of nine (11%) patients treated with targeted therapy (BRAF and MEK Inhibitors) or immune-checkpoint therapy, respectively, showed disease control (partial response or stable disease). In summary, CTNNB1 mutations are associated with a unique melanocytic tumor type in benign tumors (nevi), which can be applied in a diagnostic setting. In advanced disease, no clear characteristics distinguishing CTNNB1-mutant from other melanomas were observed; however, studies of larger, optimally prospective, cohorts are warranted.

Deep Learning Assisted Diagnosis of Onychomycosis on Whole-Slide Images.

BACKGROUND: Onychomycosis numbers among the most common fungal infections in humans affecting finger- or toenails. Histology remains a frequently applied screening technique to diagnose onychomycosis. Screening slides for fungal elements can be time-consuming for pathologists, and sensitivity in cases with low amounts of fungi remains a concern. Convolutional neural networks (CNNs) have revolutionized image classification in recent years. The goal of our project was to evaluate if a U-NET-based segmentation approach as a subcategory of CNNs can be applied to detect fungal elements on digitized histologic sections of human nail specimens and to compare it with the performance of 11 board-certified dermatopathologists. METHODS: In total, 664 corresponding H&E- and PAS-stained histologic whole-slide images (WSIs) of human nail plates from four different laboratories were digitized. Histologic structures were manually annotated. A U-NET image segmentation model was trained for binary segmentation on the dataset generated by annotated slides. RESULTS: The U-NET algorithm detected 90.5% of WSIs with fungi, demonstrating a comparable sensitivity with that of the 11 board-certified dermatopathologists (sensitivity of 89.2%). CONCLUSIONS: Our results demonstrate that machine-learning-based algorithms applied to real-world clinical cases can produce comparable sensitivities to human pathologists. Our established U-NET may be used as a supportive diagnostic tool to preselect possible slides with fungal elements. Slides where fungal elements are indicated by our U-NET should be reevaluated by the pathologist to confirm or refute the diagnosis of onychomycosis.

Clinical and pathological characteristics of familial melanoma with germline TERT promoter variants.

Around 10% of melanoma occurs in patients with a suspected familial predisposition. TERT promoter mutations are the most common somatic hotspot mutations in human cancers. However, only two families with germline mutations have been identified to date. We present detailed histological, clinical, and molecular pathologic analyses of affected patients and details of newly identified individuals in one of these previously reported families. TERT (NM_198253.3) Chr.5:1,295,161T>C (c.-57 T>C) promoter variants were detected in all melanoma-affected (n = 18) and one non-diseased family member. The median age at diagnosis was 30 years (n = 18, range 16-46 years, 2 unknown). While most primary melanomas arose on the upper extremities (n = 7, 21%) and were superficial spreading melanoma (SSM, n = 8, 24%), many primary melanomas also originated from non-UV-exposed mucosal (n = 2, 6%) and acral (n = 4, 12%) locations. One SSM sample harbored a Chr.5:1,295,228C>T TERT promoter mutation in addition to the germline Chr.5:1,295,161T>C variant, arguing additional pathway activation can support tumor pathogenesis. Patients treated with BRAF inhibitor and/or immune checkpoint inhibition (ICI) showed responses, although of limited duration. One mucosal melanoma harbored both a KIT copy number gain and an activating c.1727 p.Leu576Pro mutation. Following the modest response to ICI, subsequent KIT inhibitor (imatinib) therapy demonstrated an ongoing complete pathological response (currently 7 months).

Evaluation of a Deep Learning Approach to Differentiate Bowen's Disease and Seborrheic Keratosis.

Background: Some of the most common cutaneous neoplasms are Bowen's disease and seborrheic keratosis, a malignant and a benign proliferation, respectively. These entities represent a significant fraction of a dermatopathologists' workload, and in some cases, histological differentiation may be challenging. The potential of deep learning networks to distinguish these diseases is assessed. Methods: In total, 1935 whole-slide images from three institutions were scanned on two different slide scanners. A U-Net-based segmentation deep learning algorithm was trained on data from one of the centers to differentiate Bowen's disease, seborrheic keratosis, and normal tissue, learning from annotations performed by dermatopathologists. Optimal thresholds for the class distinction of diagnoses were extracted and assessed on a test set with data from all three institutions. Results: We aimed to diagnose Bowen's diseases with the highest sensitivity. A good performance was observed across all three centers, underlining the model's robustness. In one of the centers, the distinction between Bowen's disease and all other diagnoses was achieved with an AUC of 0.9858 and a sensitivity of 0.9511. Seborrheic keratosis was detected with an AUC of 0.9764 and a sensitivity of 0.9394. Nevertheless, distinguishing irritated seborrheic keratosis from Bowen's disease remained challenging. Conclusions: Bowen's disease and seborrheic keratosis could be correctly identified by the evaluated deep learning model on test sets from three different centers, two of which were not involved in training, and AUC scores > 0.97 were obtained. The method proved robust to changes in the staining solution and scanner model. We believe this demonstrates that deep learning algorithms can aid in clinical routine; however, the results should be confirmed by qualified histopathologists.

Genetic and methylation profiles distinguish benign, malignant and spitzoid melanocytic tumors.

Accurate classification of melanocytic tumors is important for prognostic evaluation, treatment and follow-up protocols of patients. The majority of melanocytic proliferations can be classified solely based on clinical and pathological criteria, however in select cases a definitive diagnostic assessment remains challenging and additional diagnostic biomarkers would be advantageous. We analyzed melanomas, nevi, Spitz nevi and atypical spitzoid tumors using parallel sequencing (exons of 611 genes and 507 gene translocation analysis) and methylation arrays (850k Illumina EPIC). By combining detailed genetic and epigenetic analysis with reference-based and reference-free DNA methylome deconvolution we compared Spitz nevi to nevi and melanoma and assessed the potential for these methods in classifying challenging spitzoid tumors. Results were correlated with clinical and histologic features. Spitz nevi were found to cluster independently of nevi and melanoma and demonstrated a different mutation profile. Multiple copy number alterations and TERT promoter mutations were identified only in melanomas. Genome-wide methylation in Spitz nevi was comparable to benign nevi while the Leukocytes UnMethylation for Purity (LUMP) algorithm in Spitz nevi was comparable to melanoma. Histologically difficult to classify Spitz tumor cases were assessed which, based on methylation arrays, clustered between Spitz nevi and melanoma and in terms of genetic profile or copy number variations demonstrated worrisome features suggesting a malignant neoplasm. Comprehensive sequencing and methylation analysis verify Spitz nevi as an independent melanocytic entity distinct from both nevi and melanoma. Combined genetic and methylation assays can offer additional insights in diagnosing difficult to classify Spitzoid tumors.

Genetic and Clinical Characteristics of ARID1A Mutated Melanoma Reveal High Tumor Mutational Load without Implications on Patient Survival.

(1) Background: Melanoma has the highest mortality of all cutaneous tumors, despite recent treatment advances. Many relevant genetic events have been identified in the last decade, including recurrent ARID1A mutations, which in various tumors have been associated with improved outcomes to immunotherapy. (2) Methods: Retrospective analysis of 116 melanoma samples harboring ARID1A mutations. Assessment of clinical and genetic characteristics was performed as well as correlations with treatment outcome applying Kaplan-Meier (log-rank test), Fisher's exact and Chi-squared tests. (3) Results: The majority of ARID1A mutations were in cutaneous and occult melanoma. ARID1A mutated samples had a higher number of mutations than ARID1A wild-type samples and harbored UV-mutations. A male predominance was observed. Many samples also harbored NF1 mutations. No apparent differences were noted between samples harboring genetically inactivating (frame-shift or nonsense) mutations and samples with other mutations. No differences in survival or response to immunotherapy of patients with ARID1A mutant melanoma were observed. (4) Conclusions: ARID1A mutations primarily occur in cutaneous melanomas with a higher mutation burden. In contrast to findings in other tumors, our data does not support ARID1A mutations being a biomarker of favorable response to immunotherapies in melanoma. Larger prospective studies would still be warranted.

Genetic characterization of advanced conjunctival melanoma and response to systemic treatment.

BACKGROUND: Conjunctival melanoma is a rare type of ocular melanoma, which is prone to local recurrence and metastasis and can lead to patient death. Novel therapeutic strategies have revolutionized cutaneous melanoma management. The efficacy of these therapies in conjunctival melanoma, however, has not been evaluated in larger patient cohorts. METHODS: In this multi-center retrospective cohort study with additional screening of the ADOREG database, data were collected from 34 patients with metastatic conjunctival melanoma who received targeted therapy (TT) (BRAF ± MEK inhibitors) or immune checkpoint inhibitors (ICI) (anti-PD-1 ± anti-CTLA4). In 15 cases, tissue was available for targeted next-generation-sequencing (611 genes) and RNA sequencing. Driver mutations, tumor mutational burden, copy number variations and inflammatory/IFNγ gene expression signatures were determined. RESULTS: Genetic characterization identified frequent BRAF (46.7%, 7/15), NRAS (26.7%, 4/15), NF1 (20%, 3/15), and TERT promoter (46.7%, 7/15) mutations. UV associated C>T and CC>TT mutations were common. Median follow-up time after start of first TT or ICI therapy was 13.2 months. In 26 patients receiving first-line ICI, estimated one-year progression-free survival (PFS) rate was 42.0%, PFS and overall survival (OS) 6.2 and 18.0 months, respectively. First-line TT was given to 8 patients, estimated one-year PFS rate was 54.7%, median PFS and OS 12.6 and 29.1 months, respectively. CONCLUSIONS: Our findings support the role of UV irradiation in conjunctival melanoma and the genetic similarity with cutaneous melanoma. Conjunctival melanoma patients with advanced disease benefit from both targeted therapies (BRAF ± MEK inhibitors) and immune checkpoint inhibitors.