Glycolysis, commonly used by malignant tumors for energy production, results in acidification of the tumor microenvironment (TME) through the secretion and accumulation of lactic acid. Acidosis is a potent inhibitor of immune cell function and may therefore affect T-cell infiltration and the efficacy of immunotherapy. This study aimed to characterize the metabolic tumor microenvironment and its association with lymphocyte distribution in patients with advanced melanoma treated with immune checkpoint blockade (ICB) therapies. Pre-treatment formalin-fixed, paraffin-embedded metastatic melanoma specimens from 45 patients treated with anti-PD-1 ± anti-CTLA-4 ICB were included in this study. Patients with progression-free survival (PFS) ≥ 6mo were categorized as responders (n = 23), while non-responders had a PFS < 6mo (n = 22). Two custom multiplex immunofluorescence panels were developed to evaluate the expression and distribution of markers of a hypoxic microenvironment (CA9 and HIF1α), glycolysis (GLUT1 and GLUT3) and vessels (CD31) in relation to melanocytes (SOX10) and T lymphocytes (CD3). GLUT1 + melanoma regions contained significantly lower proportions of CD3+ T-cells than GLUT1- regions (p < 0.0001). Responders displayed significantly higher proportions of intratumoral T-cells expressing GLUT1 (p = 0.049) and GLUT3 (p = 0.043) compared to non-responders. CD3+ T-cells co-expressing hypoxia-associated markers were present in higher proportions significantly closer to GLUT1+ melanoma cells in responders compared to non-responders (p < 0.05). Patients with higher proportions of CD3+ T-cells and CD3+CA9+ T-cells within the 20 µm distance to GLUT1+ melanoma cells had significantly longer progression-free survival (p = 0.0133 and p = 0.0378, respectively). Together, these findings support the hypothesis that the presence of glycolysis in melanoma (as inferred by increased GLUT expression) may affect the ability of T-cells to infiltrate tumors and function effectively. The results also suggest that the overall proportion and spatial distribution of GLUT+ T-cells, including those displaying evidence of adaptation to a hypoxic/acidic TME, may be relevant for responses to ICB therapy.

BAP1 inactivated melanocytic tumors (BIMTs) are recognized for their potential for significant morphologic atypia including nuclear atypia, expansile growth, and mitotic activity, making it difficult to form firm morphologic criteria for malignancy. Next generation sequencing (NGS) is becoming increasingly utilized in melanocytic pathology. We conducted a two-phase survey with 26 dermatopathologists from the International Melanoma Pathology Study Group to assess the impact of NGS on diagnostic accuracy and interobserver agreement in 31 BIMTs. After NGS results, interobserver agreement improved from fair on Survey 1 (κ = 0.348) to moderate on Survey 2 (κ = 0.441). Respondents were 1.7 times more likely to be correct on Survey 2 after NGS results (OR = 1.67, 95% CI [1.16-2.44], p = 0.005). When accounting for case variability and difficulty, respondents were 8.7 times more likely to provide a correct diagnosis for a given case (CMH OR = 8.69, 95% CI [4.89-15.44], p < 0.001). Among the 8 BAP1 inactivated melanomas in this study, 3 transitioned from a majority of votes for benign/intermediate grade BIMT to a majority of votes for melanoma after seeing NGS data. Genomic aberrations exclusive to malignant cases included pathogenic variants in TERT-p, CDKN2A, PTEN, and amplification of MYC. Our study suggests NGS has the potential to improve diagnostic accuracy and interobserver agreement for BAP1 inactivated melanocytic tumors. With the advent of increasingly effective therapies for melanoma, there is value in forming a definitive diagnosis of melanoma when appropriate. Additional studies with greater case numbers and follow-up are needed to further validate these findings.

Importance:The MEL-SELF randomized clinical trial (RCT) evaluated patient-led surveillance as an alternative model of follow-up. The baseline characteristics of participants provide insights into current unmet clinical needs of this population. Objective:To describe the baseline characteristics of people screened for and randomized to the MEL-SELF RCT, and those potentially eligible but not randomized. Design, Setting, and Participants:Baseline data from the RCT's recruitment processes, from December 2021 to June 2024, were analyzed. Data were collected from dermatologist- and general practitioner-led skin cancer clinics in Australia, and included adults previously treated for early-stage melanoma (by American Joint Committee on Cancer Staging Manual [AJCC, 0-II]) attending routinely scheduled clinics, with a skin self-examination (SSE) partner, and a smartphone. Analysis took place between August 2025 and December 2025. Interventions:Participants were invited to participate in the MEL-SELF trial with randomization (1:1) to patient-led surveillance (usual care plus reminders to perform SSE, mobile dermatoscope, teledermatologist assessment, fast-tracked unscheduled clinic visits) or clinician-led surveillance (usual care) for 12 months. Main Outcomes and Measures:The main outcomes were enrollment; active run-in and allocation results, sociodemographic and clinical characteristics; SSE knowledge, attitudes, and practice (frequency and thoroughness); and psychological measures including fear of cancer recurrence (FCR) at baseline. Results:Of 1226 patients screened and potentially eligible, 504 were randomized to patient-led (n = 251) or clinician-led (n = 253) surveillance. Overall, 295 were female individuals (59%) and 209 were male individuals (41%), most were aged 50 years and older (mean [SD] age, 56.0 [11.6] years) and had a highest substage of melanoma in situ (245 [49%]) or IA (217 [43%]). SSE practice varied substantially, ranging from no SSE in the previous 12 months (103 [20%]) to weekly or monthly SSE (160 [32%]). A high proportion (232 [46%]) reported clinically significant levels of FCR, which was associated with being female, younger age, and higher depression, anxiety, and stress scores. FCR was associated with a higher perceived lifetime risk of melanoma, but not with participants' actual calculated risk of a subsequent new primary melanoma (OR, 1.00; 95% CI, 0.99-1.01). Characteristics were similar between the trial population and potentially eligible patients who completed the baseline questionnaire but were not randomized (n = 225). Conclusions:This secondary analysis of baseline characteristics in the MEL-SELF trial indicates suboptimal SSE practice and clinically significant levels of FCR. Future reports will evaluate comparative effects of patient-led surveillance on health, psychological and health resource use outcomes. Trial Registration:anzctr.org.au Identifier: ACTRN12621000176864.

Half of the participants in this randomized online experiment of 1655 Australian adults would prefer to not have a wide local excision (WLE) after a completely excised low-risk melanocytic lesion, if offered this option. Using the term ‘low-risk melanocytic neoplasm’ instead of ‘melanoma in situ’ reduced anxiety and the proportion choosing a WLE. Across all label groups, most participants preferred to attend routine 6-monthly clinic visits for follow-up, most likely reflecting the high prevalence of this in the Australian context.

Overdiagnosis in melanoma is a nuanced and evolutionary issue. It may appear to be an epidemic from an epidemiological point of view; however, it is important to appreciate all perspectives. We consider overdiagnosis a necessary challenge-which drives clinicians to improve diagnostic accuracy and reduce overtreatment-pathologists to recalibrate histopathological thresholds and reduce overcalling-and researchers to develop markers of biological activity for melanoma. In this article, we present our clinical perspective on the current burden of melanoma overdiagnosis and its future value for progress in the field of melanoma.

Diagnosing and treating skin diseases require advanced visual skills across domains and the ability to synthesize information from multiple imaging modalities. While current deep learning models excel at specific tasks such as skin cancer diagnosis from dermoscopic images, they struggle to meet the complex, multimodal requirements of clinical practice. Here we introduce PanDerm, a multimodal dermatology foundation model pretrained through self-supervised learning on over 2 million real-world skin disease images from 11 clinical institutions across 4 imaging modalities. We evaluated PanDerm on 28 diverse benchmarks, including skin cancer screening, risk stratification, differential diagnosis of common and rare skin conditions, lesion segmentation, longitudinal monitoring, and metastasis prediction and prognosis. PanDerm achieved state-of-the-art performance across all evaluated tasks, often outperforming existing models when using only 10% of labeled data. We conducted three reader studies to assess PanDerm's potential clinical utility. PanDerm outperformed clinicians by 10.2% in early-stage melanoma detection through longitudinal analysis, improved clinicians' skin cancer diagnostic accuracy by 11% on dermoscopy images and enhanced nondermatologist healthcare providers' differential diagnosis by 16.5% across 128 skin conditions on clinical photographs. These results show PanDerm's potential to improve patient care across diverse clinical scenarios and serve as a model for developing multimodal foundation models in other medical specialties, potentially accelerating the integration of artificial intelligence support in healthcare.

To-date the genomic landscape of cutaneous pericytic tumours (PTs), which represent a morphological continuum, have not been comprehensively explored. In order to identify the driver events of PTs from across their histological spectrum, and potentially aid the current classification system, we sequenced DNA (whole-exome) and RNA (pulldown transcriptome) from tumour-normal pairs classified by two different dermatopathologists of angioleiomyoma (n=37), glomus tumour (n=30) and myopericytoma (n=11); with all sequencing data deposited in the European Genome and Phenome Archive. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was funded by the Medical Research Council (MR/V000292/1) and Wellcome Trust (220540/Z/20/A). NR is supported by the Newcastle NIHR Biomedical Research Centre. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethical approval for the use of all patient samples in this project was obtained by a committee at the institution of origin and from Research Governance at the Wellcome Sanger Institute. This study is part of the DERMATLAS Project that has been approved by the NHS Health Research Authority; Research Ethics Committee (REC) reference: 21/PR/1024, IRAS project ID: 304621. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All sequencing data deposited in the European Genome and Phenome Archive.

BACKGROUND:Next-generation sequencing (NGS) is becoming more commonly used for diagnosis in dermatopathology. It's critical to appraise its efficacy and limitations. Distinguishing benign deep penetrating nevi (DPN) from deep penetrating like-melanoma (DPN-M) is a challenging diagnostic scenario even for experienced dermatopathologists. METHODS:We sent a two-phase survey (pre-and postgenomics) to 32 experienced dermatopathologists to evaluate 39 diagnostically challenging cases from the DPN/WNT-activated family of melanocytic neoplasms. RESULTS:With NGS data, interobserver agreement improved from 0.41 to 0.51 (p < 0.0001) in distinguishing DPN-M from nonmelanoma cases. Overall diagnostic accuracy improved, mostly driven by a 16% increase in accurate diagnosis of DPN-M. However, in two cases, the inclusion of genomics shifted the majority vote from a correct to an incorrect diagnosis. A total of 218 diagnostic changes occurred between Survey 1 and 2. Among the changes, 132 votes moved toward the correct diagnosis while 86 moved toward an incorrect diagnosis. The shift in voting which resulted in improved diagnostic accuracy was statistically significant (p = 0.0001). CONCLUSIONS:NGS has the potential to improve interobserver agreement and diagnostic accuracy. We provide guidance on the utilization of bioinformatic data to maximize its benefits and improve diagnostic accuracy and interobserver agreement.

CONTEXT & AIM:Sentinel lymph node biopsy (SLNB) is an invasive procedure that detects microscopic nodal metastasis, crucial for accurate staging and optimal management. In melanoma, most patients who undergo the procedure have no sentinel lymph node (SLN) metastasis detected. The CP-GEP model (Merlin Assay) was developed to identify patients who do not have SLN metastases and who may therefore safely forgo SLNB, based upon clinicopathologic and gene expression features of the primary tumour. While the Merlin Assay has been validated by independent cohorts with relatively moderate sample sizes, this meta-analysis aims to assess the overall predictive performance of the model and examine potential heterogeneity between the external validation cohorts. METHOD:We conducted a literature search in MEDLINE and Embase for studies that externally validated the CP-GEP model and were published between January 2019 and June 2024. Studies that reported the model's sensitivity and specificity were included. Quality assessment was conducted by two reviewers using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. The outcomes investigated were sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and SLNB reduction rate, defined as the proportion of patients that can forgo SLNB based on the Merlin Assay outcome. Individual estimates were pooled using random effects meta-analysis model. Subgroup analysis was performed by T category. FINDINGS (IMPACT):Four external validation studies (three retrospective and one prospective) were identified and included, involving 1099 participants. The pooled median age was 60 years and median Breslow thickness was 1.8 mm. Across all primary tumour classification groups (pT1-pT4), the pooled sensitivity was 93 % (95 % CI: 88 %-96 %), specificity was 32 % (95 % CI: 23 %-41 %), PPV was 24 % (95 % CI: 18 %-31 %), NPV was 95 %, (95 % CI: 92 %-97 %) and SLNB reduction rate was 27 % (95 % CI: 20 %-35). The subgroup analysis revealed that the model performed best in the pT2 group; pooled sensitivity was 91 % (95 % CI: 82 %-96 %), specificity was 35 % (95 % CI: 30 %-39 %), PPV was 20 % (95 % CI: 14 %-27 %), NPV was 96 %, (95 % CI: 91 %-98 %) and SLNB reduction rate was 31 % (95 % CI: 27 %-35 %). CONCLUSION:This meta-analysis suggests that the CP-GEP model can reduce the number of unnecessary SLNBs performed, especially in pT2 patients. It can improve clinical decision making and assist in patients' informed consent. Broader implications such as potential reductions in healthcare costs and risks of surgical complications should be explored further. PROSPERO REGISTRATION:CRD42024547893.

Supplementary Table S1. Representativeness of study participants Supplementary Table S2. Analysis of the intracranial response duration in the COMBI-MB trial. Supplementary Table S3. Analysis of overall survival in the COMBI-MB trial. Supplementary Table S4. Baseline clinical features of the COMBI-BRV trial. Supplementary Table S5. Gene expression score (SingScore) and multiplex immunohistochemical results. Supplementary Table S6. Summary statistics of the linear mixed models utilized to assess the association between protein/pathway expression/score and biopsy sites. Supplementary Table S7. Mutation annotated file of exome sequencing from melanoma biopsies.

Gene expression of major signalling pathways and multiplex immunohistochemical comparison of markers between timepoints and tumor sites. A) Gene expression of related oncogenic signalling pathways between treatment timepoints and site, B) Estimated mean difference of proteins and pathways between biopsy categories (95% confidence interval for the estimated mean difference is depicted as a segment), C) Multiplex immunohistochemical staining for key intracellular signalling proteins, D) Changes in immune intratumoral immune cell densities between treatment timepoints and site, E and F) Multiplex immunohistochemical staining depicted marked decrease in immune cell densities EDT in PT4 (steroid treated). PRE, collected prior to treatment; EDT, collected early during treatment (i.e., after 10-14 days of dabrafenib). MBM, melanoma brain metastasis; ECM, extracranial metastasis. PT, patient.

To comprehensively explore the mutational landscape of cutaneous leiomyoma (cLM) and identify candidate driver events, we performed a retrospective, multi-institutional, whole-exome sequencing and RNA sequencing study. We confirmed that a large proportion of patients with cLM have germline FH variants and additionally showed that somatic alteration of FH also drives cLM, with biallelic inactivation of FH being a frequent event. Treatment of Fh1-proficient and -deficient cell lines with the purine antagonist and chemotherapeutic agent, mercaptopurine, significantly decreased growth/colony formation; however, the addition of nucleosides was able to rescue only the Fh1-proficient cells, suggesting that purine metabolism is a targetable vulnerability for FH-deficient cLMs.