Severe autoimmune hemolytic anemia following immunotherapy with checkpoint inhibitors in two patients with metastatic melanoma: a case report.

Introduction: Over the past decade, immune checkpoint inhibitors such as antibodies against cytotoxicity T-lymphocyte-associated protein 4 (CTLA-4) and programmed cell death protein 1 (PD-1) have become an important armamentarium against a broad spectrum of malignancies. However, these specific inhibitors can cause adverse autoimmune reactions by impairing self-tolerance. Hematologic side effects of immune checkpoint inhibitors, including autoimmune hemolytic anemia (AIHA), are rare but can be life-threatening. Case report: Herein, we report two patients on immune checkpoint inhibitors for metastatic melanoma who developed AIHA with symptoms of dyspnea and fatigue. In the first patient, symptoms alleviated after discontinuation of combined anti CTLA-4 and anti-PD-1 therapy, initiation of corticosteroids and application of a single red blood cell transfusion. Due to subsequent progress of melanoma, combinational anti-PD-1 and tyrosine kinase inhibitor therapy was initiated based on multidisciplinary tumor board decision. After two months, she again developed the described hematological and clinical signs of AIHA leading to cessation of anti-PD-1 therapy and initiation of corticosteroids, which again resulted in an alleviation of her symptoms. Due to further progression, the patient received dacarbazine for several months before she decided to stop any therapy other than palliative supportive care. In the second patient, discontinuation of anti-PD-1 therapy and initiation of corticosteroids entailed a complete alleviation of his symptoms. After refusing chemotherapy due to subsequent melanoma progression, he received radiotherapy of bone metastases and is currently enrolled in a clinical trial. The patient did not develop AIHA ever since. Conclusion: Hematologic immune-related adverse events due to treatment with immune checkpoint inhibitors are rare but can have life-threatening consequences. If dyspnea and other clinical symptoms are present, AIHA should be considered as a potential cause and treated promptly in a multidisciplinary setting. An expanded comprehension of risk factors and pathogenesis of AIHA is needed to identify high-risk patients beforehand, leading to more effective predictive and reactive treatment approaches.

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.

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.

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.

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.

COVID-19 vaccination in advanced skin cancer patients receiving systemic anticancer treatment: A prospective singlecenter study investigating seroconversion rates.

Background: COVID-19 vaccination reduces risk of SARS-CoV-2 infection, COVID-19 severity and death. However, the rate of seroconversion after COVID-19 vaccination in cancer patients requiring systemic anticancer treatment is poorly investigated. The aim of the present study was to determine the rate of seroconversion after COVID-19 vaccination in advanced skin cancer patients under active systemic anticancer treatment. Methods: This prospective single-center study of a consecutive sample of advanced skin cancer patients was performed from May 2020 until October 2021. Inclusion criteria were systemic treatment for advanced skin cancer, known COVID-19 vaccination status, repetitive anti-SARS-CoV-2-S IgG serum quantification and first and second COVID-19 vaccination. Primary outcome was the rate of anti-SARS-CoV-2-S IgG seroconversion after complete COVID-19 vaccination. Results: Of 60 patients with advanced skin cancers, 52 patients (86.7%) received immune checkpoint inhibition (ICI), seven (11.7%) targeted agents (TT), one (1.7%) chemotherapy. Median follow-up time was 12.7 months. During study progress ten patients had died from skin cancer prior to vaccination completion, six patients were lost to follow-up and three patients had refused vaccination. 41 patients completed COVID-19 vaccination with two doses and known serological status. Of those, serum testing revealed n=3 patients (7.3%) as anti-SARS-CoV-2-S IgG positive prior to vaccination, n=32 patients (78.0%) showed a seroconversion, n=6 patients (14.6%) did not achieve a seroconversion. Patients failing serological response were immunocompromised due to concomitant hematological malignancy, previous chemotherapy or autoimmune disease requiring immunosuppressive comedications. Immunosuppressive comedication due to severe adverse events of ICI therapy did not impair seroconversion following COVID-19 vaccination. Of 41 completely vaccinated patients, 35 (85.4%) were under treatment with ICI, five (12.2%) with TT, and one (2.4%) with chemotherapy. 27 patients (65.9%) were treated non adjuvantly. Of these patients, 13 patients had achieved objective response (complete/partial response) as best tumor response (48.2%). Conclusion and relevance: Rate of anti-SARS-CoV-2-S IgG seroconversion in advanced skin cancer patients under systemic anticancer treatment after complete COVID-19 vaccination is comparable to other cancer entities. An impaired serological response was observed in patients who were immunocompromised due to concomitant diseases or previous chemotherapies. Immunosuppressive comedication due to severe adverse events of ICI did not impair the serological response to COVID-19 vaccination.

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.

TERT promoter mutations are associated with longer progression-free and overall survival in patients with BRAF-mutant melanoma receiving BRAF and MEK inhibitor therapy.

BACKGROUND: Around 50% of cutaneous melanomas harbour therapeutically targetable BRAF V600 mutations. Reliable clinical biomarkers predicting duration of response to BRAF-targeted therapies are still lacking. Recent in vitro studies demonstrated that BRAF-MEK inhibitor therapy response is associated with tumour TERT promoter mutation status. We assessed this potential association in a clinical setting. METHODS: The study cohort comprised 232 patients with metastatic or unresectable BRAF V600-mutated melanoma receiving combined BRAF/MEK inhibitor treatment, including a single-centre retrospective discovery cohort (N = 120) and a prospectively collected multicenter validation cohort (N = 112). Patients were excluded if they received BRAF or MEK inhibitors in an adjuvant setting, as monotherapy, or in combination with immunotherapy. Kaplan-Meier and univariate/multivariate Cox regression analyses were performed as appropriate. RESULTS: median age at first diagnosis was 54 years (range 16-84 years). The majority of patients were men 147/232 (63.4%). Most tumours harboured TERT promoter mutations (72%, N = 167). A survival advantage was observed in both progression-free survival (PFS) and overall survival (OS) for patients with TERT promoter-mutant versus wild-type tumours in both the discovery cohort (mPFS of 9.6 months [N = 87] vs 5.0 months [N = 33]; hazard ratio [HR] = 0.56 [95% confidence interval {CI} 0.33-0.96] and mOS of 33.6 months vs 15.0 months; HR = 0.47 [95%CI 0.32-0.70]) as well as the validation cohort (mPFS of 7.3 months [N = 80] vs 5.8 months [N = 32]; HR = 0.67 [95%CI 0.41-1.10] and mOS of 51.1 months vs 15.0 months; HR = 0.33 [95%CI 0.18-0.63]). In the pooled cohort of TERT promoter-mutant (N = 167) versus wild-type (N = 65) tumours, respectively, PFS was 8.9 versus 5.5 months, (HR = 0.62; 95%CI 0.45-0.87; P = 0.004), and OS was 33.6 versus 17.0 months, (HR = 0.51; 95%CI 0.35-0.75, P = 0.0001). CONCLUSIONS: In patients with melanoma receiving BRAF/MEK-targeted therapies, TERT promoter mutations are associated with longer survival. If validated in larger studies, TERT promoter mutation status should be included as a predictive biomarker in treatment algorithms for advanced melanoma.

High-resolution three-dimensional imaging for precise staging in melanoma.

INTRODUCTION: Many cancer guidelines include sentinel lymph node (SLN) staging to identify microscopic metastatic disease. Current SLN analysis of melanoma patients is effective but has the substantial drawback that only a small representative portion of the node is sampled, whereas most of the tissue is discarded. This might explain the high clinical false-negative rate of current SLN diagnosis in melanoma. Furthermore, the quantitative assessment of metastatic load and microanatomical localisation might yield prognosis with higher precision. Thus, methods to analyse entire SLNs with cellular resolution apart from tedious sequential physical sectioning are required. PATIENTS AND METHODS: Eleven melanoma patients eligible to undergo SLN biopsy were included in this prospective study. SLNs were fixed, optically cleared, whole-mount stained and imaged using light sheet fluorescence microscopy (LSFM). Subsequently, compatible and unbiased gold standard histopathological assessment allowed regular patient staging. This enabled intrasample comparison of LSFM and histological findings. In addition, the development of an algorithm, RAYhance, enabled easy-to-handle display of LSFM data in a browsable histologic slide-like fashion. RESULTS: We comprehensively quantify total tumour volume while simultaneously visualising cellular and anatomical hallmarks of the associated SLN architecture. In a first-in-human study of 21 SLN of melanoma patients, LSFM not only confirmed all metastases identified by routine histopathological assessment but also additionally revealed metastases not detected by routine histology alone. This already led to additional therapeutic options for one patient. CONCLUSION: Our three-dimensional digital pathology approach can increase sensitivity and accuracy of SLN metastasis detection and potentially alleviate the need for conventional histopathological assessment in the future. GERMAN CLINICAL TRIALS REGISTER: (DRKS00015737).

NF1-mutated melanomas reveal distinct clinical characteristics depending on tumour origin and respond favourably to immune checkpoint inhibitors.

BACKGROUND: NF1-mutated tumours represent a small subset (10-15%) of melanomas, not sufficiently analysed in large clinical cohorts. This study investigated the largest multicentre collection of NF1-mutated melanomas to date. METHODS: This study analysed a multicentre tumour tissue sample cohort from 266 patients with NF1-mutated melanoma. Targeted next-generation sequencing of the TERT promoter and 29 relevant melanoma genes was performed. Survival was compared with NF1 wild-type cohorts from the Tissue Registry in Melanoma project (n = 432). RESULTS: Most NF1-mutated melanoma arose in the head-and-neck region of patients >60 years. NF1 alterations were frequently inactivating, primarily non-sense, less frequently truncating mutations. Non-inactivating NF1 mutations more frequently co-occurred with activating BRAF and RAS mutations. NF1-mutated tumours had higher numbers of gene mutations and UV signature C>T and CC>TT transitions than BRAF, RAS and triple wild-type melanomas. NF1-mutated acral and mucosal melanomas harboured a different mutation signature and were frequent in women (69% and 83%, respectively), differing from non-acral cutaneous NF1-mutated melanomas (men 73%, women 27%). Overall survival in stage IV disease was comparable for patients with NF1-mutated or wild-type melanoma. However, in patients receiving first-line immune checkpoint inhibitor treatment, better median overall survival (mOS) was observed for NF1-mutated than wild-type tumours (mOS = not reached vs mOS = 25.82, p = 0.0154, n = 80 and 432, respectively). CONCLUSIONS: Cutaneous, acral and mucosal NF1-mutated melanomas vary in clinical and genetic characteristics and demonstrate a favourable outcome on immune checkpoint inhibition therapy.

GNA14, GNA11, and GNAQ Mutations Are Frequent in Benign but Not Malignant Cutaneous Vascular Tumors.

Cutaneous vascular tumors consist of a heterogeneous group of benign proliferations, including a range of hemangiomas and vascular malformations, as well as heterogeneous groups of both borderline and malignant neoplasms such as Kaposi's sarcoma and angiosarcomas. The genetics of these tumors have been assessed independently in smaller individual cohorts making comparisons difficult. In our study, we analyzed a representative cohort of benign vascular proliferations observed in a clinical routine setting as well as a selection of malignant vascular proliferations. Our cohort of 104 vascular proliferations including hemangiomas, malformations, angiosarcomas and Kaposi's sarcoma were screened by targeted next-generation sequencing for activating genetic mutations known or assumed to be potentially relevant in vascular proliferations. An association analysis was performed for mutation status and clinico-pathological parameters. Frequent activating hotspot mutations in GNA genes, including GNA14 Q205, GNA11 and GNAQ Q209 were identified in 16 of 64 benign vascular tumors (25%). GNA gene mutations were particularly frequent (52%) in cherry (senile) hemangiomas (13 of 25). In angiosarcomas, activating RAS mutations (HRAS and NRAS) were identified in three samples (16%). No activating GNA or RAS gene mutations were identified in Kaposi's sarcomas. Our study identifies GNA14 Q205, GNA11 and GNAQ Q209 mutations as being the most common and mutually exclusive mutations in benign hemangiomas. These mutations were not identified in malignant vascular tumors, which could be of potential diagnostic value in distinguishing these entities.

Molecular pathology as a diagnostic aid in difficult-to-classify melanocytic tumours with spitzoid morphology.

Accurate classification of melanocytic proliferations has important implications for prognostic prediction, treatment and follow-up. Although most melanocytic proliferations can be accurately classified using clinical and pathological criteria, classification (specifically distinction between nevus and melanoma) can be challenging in a subset of cases, including those with spitzoid morphology. Genetic studies have shown that mutation profiles differ between primary melanoma subtypes and Spitz nevi. These differences may aid in distinguishing benign from malignant in some melanocytic tumours. Here, we present a selection of melanocytic proliferations with equivocal histopathological criteria, wherein genetic analysis was requested to help guide classification. In two of four cases, the genetic results offered valuable insights, allowing a definitive diagnosis, indicating the diagnostic value of mutation profiling in a real-world routine clinical setting. Although histopathological assessment remains decisive in melanocytic proliferation classification, we recommend including genetic profiling in cases of borderline or atypical lesion to support accurate classification.

Calculating the number of undetected active SARS-CoV-2 infections from results of population-wide antigen tests.

Current European research estimates the number of undetected active SARS-CoV-2 infections (dark figure) to be two- to 130-fold the number of detected cases. We revisited the population-wide antigen tests in Slovakia and South Tyrol and calculated the dark figure of active cases in the vulnerable populations and the number of undetected active cases per detected active case at the time of the population-wide tests. Our analysis follows three steps: using the sensitivities and specificities of the used antigen tests, we first calculated the number of test-positive individuals and the proportion of actual positives in those who participated in the antigen tests. We then calculated the dark figure in the total population of Slovakia and South Tyrol, respectively. Finally, we calculated the ratio of the dark figure in the vulnerable population to the number of newly detected infections through PCR tests. Per one positive PCR result, another 0.15 to 0.71 cases must be added in South Tyrol and 0.01 to 1.25 cases in Slovakia. The dark figure was in both countries lower than assumed by earlier studies.