MicroRNA expression profiling of cutaneous squamous cell carcinomas and precursor lesions.

Background: Actinic keratoses (AK) are pre-malignant skin lesions caused by chronic sun exposure. Progression from an AK to intraepidermal carcinoma (IEC) and a cutaneous squamous cell carcinoma (SCC) is well known but the rate of transformation to an invasive SCC is highly variable. Since no definitive biomarkers are available, treatment decisions are made ad hoc. Objectives: To fully characterise our AK to SCC progression series, we performed microRNA (miRNA) microarray expression profiling of normal and photodamaged skin, as well as AKs, IEC, and invasive SCCs. Methods: The study recruited 27 patients who donated fresh biopsies of normal skin, photodamaged skin, AK, IEC, and SCC (n = 67 specimens). All miRbase (v.21) miRNAs were profiled to identify miRNAs related to SCC progression. miRNAs were validated using qRT-PCR and in vitro phenotypic assays. Results: There were 234 robustly expressed miRNAs across the tissue collection, which resulted in 20 miRNA that were differentially expressed ((cor)p ≤ 0.05 and ≥ 10 fold) between normal skin and SCC. Hierarchical clustering all samples illustrated that AKs, IEC, and SCCs were largely indistinguishable, which confirms the premalignant status of an AK. A panel of miRNAs showed significant dysregulation between normal and photodamaged skin and AK. Importantly, we found miR-34a-5p and miR-31-5p had significant differential expression between AKs and IEC and IEC and SCC respectively. Phenotypic assays determined that the miR-31 duplex had opposing effects on SCC cell lines which suggests that dysregulation of this duplex may be related to the dynamic control of progression of transformed keratinocytes. Conclusions: This study confirmed the continuum of AK with IEC and SCC highlighting that miRNA expression plays a role in keratinocyte transformation. Development of our putative miRNA biomarker candidates is warranted to aid in clinical management of patients experiencing high AK load to determine the most appropriate treatment.

Skin cancers are the most frequent cancers in fair-skinned populations, but we can prevent them.

Cancers of the skin are the most commonly occurring cancers in humans. In fair-skinned populations, up to 95% of keratinocyte skin cancers and 70-95% of cutaneous melanomas are caused by ultraviolet radiation and are thus theoretically preventable. Currently, however, there is no comprehensive global advice on practical steps to be taken to reduce the toll of skin cancer. To address this gap, an expert working group comprising clinicians and researchers from Africa, America, Asia, Australia, and Europe, together with learned societies (European Association of Dermato-Oncology, Euromelanoma, Euroskin, European Union of Medical Specialists, and the Melanoma World Society) reviewed the extant evidence and issued the following evidence-based recommendations for photoprotection as a strategy to prevent skin cancer. Fair skinned people, especially children, should minimise their exposure to ultraviolet radiation, and are advised to use protective measures when the UV index is forecast to reach 3 or higher. Protective measures include a combination of seeking shade, physical protection (e.g. clothing, hat, sunglasses), and applying broad-spectrum, SPF 30 + sunscreens to uncovered skin. Intentional exposure to solar ultraviolet radiation for the purpose of sunbathing and tanning is considered an unhealthy behaviour and should be avoided. Similarly, use of solaria and other artificial sources of ultraviolet radiation to encourage tanning should be strongly discouraged, through regulation if necessary. Primary prevention of skin cancer has a positive return on investment. We encourage policymakers to communicate these messages to the general public and promote their wider implementation.

POT1 and multiple primary melanomas: the dermatological phenotype.

POT1 is the second most frequently reported gene (after CDKN2A) in familial melanoma. Pathogenic variants are associated with earlier onset and/or multiple primary melanomas (MPMs). To date, POT1 phenotypical reports have been largely restricted to associated malignancies, and description of the dermatological landscape has been limited. We identified 10 variants in n=18 of 384 (4.7%) unrelated individuals (n=13 MPMs; n=5 single primary melanomas) of European ancestry. Five variants were rare (minor allele frequency <0.001) or novel (two loss-of-function (LOF), one splice acceptor and two missense) and were predicted to be functionally significant, in five unrelated probands with MPMs (≥3 melanomas). We performed three-dimensional total body photography on both individuals with confirmed pathogenic LOF variants to characterise the dermatological phenotype. Total body naevus counts (≥2 mm diameter) were significantly higher (p=7.72×10-12) in carriers compared with a control population. Majority of naevi were on the probands' back and lower limb regions, where only mild to moderate ultraviolet (UV) damage was observed. Conversely, the head/neck region, where both probands exhibited severe UV damage, had comparably fewer naevi. We hypothesise that carriage of functionally significant POT1 variants is associated with increased naevus counts generally, and naevi >5 mm in diameter specifically and the location of these are independent of UV damage.

Examining labelling guidelines for AI-based software as a medical device: A review and analysis of dermatology mobile applications in Australia.

In recent years, there has been a surge in the development of AI-based Software as a Medical Device (SaMD), particularly in visual specialties such as dermatology. In Australia, the Therapeutic Goods Administration (TGA) regulates AI-based SaMD to ensure its safe use. Proper labelling of these devices is crucial to ensure that healthcare professionals and the general public understand how to use them and interpret results accurately. However, guidelines for labelling AI-based SaMD in dermatology are lacking, which may result in products failing to provide essential information about algorithm development and performance metrics. This review examines existing labelling guidelines for AI-based SaMD across visual medical specialties, with a specific focus on dermatology. Common recommendations for labelling are identified and applied to currently available dermatology AI-based SaMD mobile applications to determine usage of these labels. Of the 21 AI-based SaMD mobile applications identified, none fully comply with common labelling recommendations. Results highlight the need for standardized labelling guidelines. Ensuring transparency and accessibility of information is essential for the safe integration of AI into health care and preventing potential risks associated with inaccurate clinical decisions.

A protocol for annotation of total body photography for machine learning to analyze skin phenotype and lesion classification.

Introduction: Artificial Intelligence (AI) has proven effective in classifying skin cancers using dermoscopy images. In experimental settings, algorithms have outperformed expert dermatologists in classifying melanoma and keratinocyte cancers. However, clinical application is limited when algorithms are presented with 'untrained' or out-of-distribution lesion categories, often misclassifying benign lesions as malignant, or misclassifying malignant lesions as benign. Another limitation often raised is the lack of clinical context (e.g., medical history) used as input for the AI decision process. The increasing use of Total Body Photography (TBP) in clinical examinations presents new opportunities for AI to perform holistic analysis of the whole patient, rather than a single lesion. Currently there is a lack of existing literature or standards for image annotation of TBP, or on preserving patient privacy during the machine learning process. Methods: This protocol describes the methods for the acquisition of patient data, including TBP, medical history, and genetic risk factors, to create a comprehensive dataset for machine learning. 500 patients of various risk profiles will be recruited from two clinical sites (Australia and Spain), to undergo temporal total body imaging, complete surveys on sun behaviors and medical history, and provide a DNA sample. This patient-level metadata is applied to image datasets using DICOM labels. Anonymization and masking methods are applied to preserve patient privacy. A two-step annotation process is followed to label skin images for lesion detection and classification using deep learning models. Skin phenotype characteristics are extracted from images, including innate and facultative skin color, nevi distribution, and UV damage. Several algorithms will be developed relating to skin lesion detection, segmentation and classification, 3D mapping, change detection, and risk profiling. Simultaneously, explainable AI (XAI) methods will be incorporated to foster clinician and patient trust. Additionally, a publicly released dataset of anonymized annotated TBP images will be released for an international challenge to advance the development of new algorithms using this type of data. Conclusion: The anticipated results from this protocol are validated AI-based tools to provide holistic risk assessment for individual lesions, and risk stratification of patients to assist clinicians in monitoring for skin cancer.

Terahertz in vivo imaging of human skin: Toward detection of abnormal skin pathologies.

Terahertz (THz) imaging has long held promise for skin cancer detection but has been hampered by the lack of practical technological implementation. In this article, we introduce a technique for discriminating several skin pathologies using a coherent THz confocal system based on a THz quantum cascade laser. High resolution in vivo THz images (with diffraction limited to the order of 100 µm) of several different lesion types were acquired and compared against one another using the amplitude and phase values. Our system successfully separated pathologies using a combination of phase and amplitude information and their respective surface textures. The large scan field (50 × 40 mm) of the system allows macroscopic visualization of several skin lesions in a single frame. Utilizing THz imaging for dermatological assessment of skin lesions offers substantial additional diagnostic value for clinicians. THz images contain information complementary to the information contained in the conventional digital images.

Minimum labelling requirements for dermatology artificial intelligence-based Software as Medical Device (SaMD): A consensus statement.

BACKGROUND/OBJECTIVES: Artificial intelligence (AI) holds remarkable potential to improve care delivery in dermatology. End users (health professionals and general public) of AI-based Software as Medical Devices (SaMD) require relevant labelling information to ensure that these devices can be used appropriately. Currently, there are no clear minimum labelling requirements for dermatology AI-based SaMDs. METHODS: Common labelling recommendations for AI-based SaMD identified in a recent literature review were evaluated by an Australian expert panel in digital health and dermatology via a modified Delphi consensus process. A nine-point Likert scale was used to indicate importance of 10 items, and voting was conducted to determine the specific characteristics to include for some items. Consensus was achieved when more than 75% of the experts agreed that inclusion of information was necessary. RESULTS: There was robust consensus supporting inclusion of all proposed items as minimum labelling requirements; indication for use, intended user, training and test data sets, algorithm design, image processing techniques, clinical validation, performance metrics, limitations, updates and adverse events. Nearly all suggested characteristics of the labelling items received endorsement, except for some characteristics related to performance metrics. Moreover, there was consensus that uniform labelling criteria should apply across all AI categories and risk classes set out by the Therapeutic Goods Administration. CONCLUSIONS: This study provides critical evidence for setting labelling standards by the Therapeutic Goods Administration to safeguard patients, health professionals, consumers, industry, and regulatory bodies from AI-based dermatology SaMDs that do not currently provide adequate information about how they were developed and tested.

Genetic testing for familial melanoma.

While the average lifetime risk of melanoma worldwide is approximately 3%, those with inherited high-penetrance mutations face an increased lifetime risk of 52-84%. In countries of low melanoma incidence, such as in Southern Europe, familial melanoma genetic testing may be warranted when there are two first degree relatives with a melanoma diagnosis. Testing criteria for high incidence countries such as USA, or with very-high incidence, such as Australia and New Zealand, would require a threshold of 3 to 4 affected family members. A mutation in the most common gene associated with familial melanoma, CDKN2A, is identified in approximately 10-40% of those meeting testing criteria. However, the use of multi-gene panels covering additional less common risk genes can significantly increase the diagnostic yield. Currently, genetic testing for familial melanoma is typically conducted by qualified genetic counsellors, however with increasing demand on testing services and high incidence rate in certain countries, a mainstream model should be considered. With appropriate training, dermatologists are well placed to identify high risk individuals and offer melanoma genetic test in dermatology clinics. Genetic testing should be given in conjunction with pre- and post-test consultation. Informed patient consent should cover possible results, the limitations and implications of testing including inconclusive results, and potential for genetic discrimination. Previous studies reporting on participant outcomes of genetic testing for familial melanoma have found significant improvements in both sun protective behavior and screening frequency in mutation carriers.

A Narrative Review: Opportunities and Challenges in Artificial Intelligence Skin Image Analyses Using Total Body Photography.

Artificial intelligence (AI) algorithms for skin lesion classification have reported accuracy at par with and even outperformance of expert dermatologists in experimental settings. However, the majority of algorithms do not represent real-world clinical approach where skin phenotype and clinical background information are considered. We review the current state of AI for skin lesion classification and present opportunities and challenges when applied to total body photography (TBP). AI in TBP analysis presents opportunities for intrapatient assessment of skin phenotype and holistic risk assessment by incorporating patient-level metadata, although challenges exist for protecting patient privacy in algorithm development and improving explainable AI methods.

MITF E318K: A rare homozygous case with multiple primary melanoma.

MITF E318K moderates melanoma risk. Only five MITF E318K homozygous cases have been reported to date, one in association with melanoma. This novel report uses 3D total-body-photography (TBP) to describe the dermatological phenotype of a homozygous MITF E318K individual. The case, a 32-year-old male, was diagnosed with his first of six primary melanomas at 26 years of age. Five melanomas were located on the back and one in the groin. Two were superficial spreading. Three arose from pre-existing naevi and one was a rare naevoid melanoma. 3D-TBP revealed a high naevus count (n = 162) with pigmentation varying from light to dark. Most naevi generally (n = 90), and large (>5 mm diameter) and clinically atypical naevi specifically were located on the back where sun damage was mild. In contrast, naevi count was low (n = 25 total) on the head/neck and lower limbs where sun damage was severe. Thus, melanoma location correlated with naevi density, rather than degree of sun damage. In addition to the MITF E318K homozygosity, there was heterozygosity for four other moderate-risk variants, which may contribute to melanoma risk. Further research is warranted to explore whether melanomas in E318K heterozygous and other homozygotes coincide with regions of high naevi density as opposed to sun damage. This could inform future melanoma screening/surveillance.

Expert Agreement on the Presence and Spatial Localization of Melanocytic Features in Dermoscopy.

Dermoscopy aids in melanoma detection; however, agreement on dermoscopic features, including those of high clinical relevance, remains poor. In this study, we attempted to evaluate agreement among experts on exemplar images not only for the presence of melanocytic-specific features but also for spatial localization. This was a cross-sectional, multicenter, observational study. Dermoscopy images exhibiting at least 1 of 31 melanocytic-specific features were submitted by 25 world experts as exemplars. Using a web-based platform that allows for image markup of specific contrast-defined regions (superpixels), 20 expert readers annotated 248 dermoscopic images in collections of 62 images. Each collection was reviewed by five independent readers. A total of 4,507 feature observations were performed. Good-to-excellent agreement was found for 14 of 31 features (45.2%), with eight achieving excellent agreement (Gwet's AC >0.75) and seven of them being melanoma-specific features. These features were peppering/granularity (0.91), shiny white streaks (0.89), typical pigment network (0.83), blotch irregular (0.82), negative network (0.81), irregular globules (0.78), dotted vessels (0.77), and blue-whitish veil (0.76). By utilizing an exemplar dataset, a good-to-excellent agreement was found for 14 features that have previously been shown useful in discriminating nevi from melanoma. All images are public (www.isic-archive.com) and can be used for education, scientific communication, and machine learning experiments.

The Skin Molecular Ecosystem Holds the Key to Nevogenesis and Melanomagenesis.

Early detection of melanoma is critical to good patient outcomes, but we still know little about the mechanisms of early melanoma development. Normal epidermis has many of the sequence variants and genetic architecture disruptions found in both benign nevi, melanomas, and other skin cancers, yet continues to behave more or less normally. One hypothesis is that many melanocytes in this context are "tumor competent" but are regulated by the microenvironment provided by the surrounding keratinocytes to inhibit progress to nevi or melanoma. There is evidence of accumulating disorder in several measures of the genomic and epigenomic landscape from normal skin through nevi to melanoma that may be key to promoting nevogenesis and melanomagenesis.

Assessing the genetic risk of nodular melanoma using a candidate gene approach.

BACKGROUND: Nodular melanoma (NM) is a challenge to diagnose early due to its rapid growth and more atypical clinical presentation, making it the largest contributor to melanoma mortality. OBJECTIVES: Our study aim was to perform a rare-variant allele (RVA) analysis of whole-exome sequencing of patients with NM and non-NM (minor allele frequency ≤ 1% non-Finnish European) for a set of 500 candidate genes potentially implicated in melanoma. METHODS: This study recruited 131 participants with NM and 194 with non-NM from South-east Queensland and patients with NM from Victoria to perform a comparative analysis of possible genetic differences or similarities between the two melanoma cohorts. RESULTS: Phenotypic analysis revealed that a majority of patients diagnosed with NM were older males with a higher frequency of fair skin and red hair than is seen in the general population. The distribution of common melanoma polygenic risk scores was similar in patients with NM and non-NM, with over 28% in the highest quantile of scores. There was also a similar frequency of carriage of familial/high-penetrant melanoma gene and loss-of-function variants. We identified 39 genes by filtering 500 candidate genes based on the greatest frequency in NM compared with non-NM cases. The genes with RVAs of greatest frequency in NM included PTCH1, ARID2 and GHR. Rare variants in the SMO gene, which interacts with PTCH1 as ligand and receptor, were also identified, providing evidence that the Hedgehog pathway may contribute to NM risk. There was a cumulative effect in carrying multiple rare variants in the NM-associated genes. A 14.8-fold increased ratio for NM compared with non-NM was seen when two RVAs of the 39 genes were carried by a patient. CONCLUSIONS: This study highlights the importance of considering frequency of RVA to identify those at risk of NM in addition to known high penetrance genes.

Position statement of the EADV Artificial Intelligence (AI) Task Force on AI-assisted smartphone apps and web-based services for skin disease.

BACKGROUND: As the use of smartphones continues to surge globally, mobile applications (apps) have become a powerful tool for healthcare engagement. Prominent among these are dermatology apps powered by Artificial Intelligence (AI), which provide immediate diagnostic guidance and educational resources for skin diseases, including skin cancer. OBJECTIVE: This article, authored by the EADV AI Task Force, seeks to offer insights and recommendations for the present and future deployment of AI-assisted smartphone applications (apps) and web-based services for skin diseases with emphasis on skin cancer detection. METHODS: An initial position statement was drafted on a comprehensive literature review, which was subsequently refined through two rounds of digital discussions and meticulous feedback by the EADV AI Task Force, ensuring its accuracy, clarity and relevance. RESULTS: Eight key considerations were identified, including risks associated with inaccuracy and improper user education, a decline in professional skills, the influence of non-medical commercial interests, data security, direct and indirect costs, regulatory approval and the necessity of multidisciplinary implementation. Following these considerations, three main recommendations were formulated: (1) to ensure user trust, app developers should prioritize transparency in data quality, accuracy, intended use, privacy and costs; (2) Apps and web-based services should ensure a uniform user experience for diverse groups of patients; (3) European authorities should adopt a rigorous and consistent regulatory framework for dermatology apps to ensure their safety and accuracy for users. CONCLUSIONS: The utilisation of AI-assisted smartphone apps and web-based services in diagnosing and treating skin diseases has the potential to greatly benefit patients in their dermatology journeys. By prioritising innovation, fostering collaboration and implementing effective regulations, we can ensure the successful integration of these apps into clinical practice.

Local blockade of tacrolimus promotes T-cell-mediated tumor regression in systemically immunosuppressed hosts.

BACKGROUND: Immunosuppressive drugs such as tacrolimus have revolutionized our ability to transplant organs between individuals. Tacrolimus acts systemically to suppress the activity of T-cells within and around transplanted organs. However, tacrolimus also suppresses T-cell function in the skin, contributing to a high incidence of skin cancer and associated mortality and morbidity in solid organ transplant recipients. Here, we aimed to identify a compound capable of re-establishing antitumor T-cell control in the skin despite the presence of tacrolimus. METHODS: In this study, we performed time-resolved fluorescence resonance energy transfer to identify molecules capable of antagonizing the interaction between tacrolimus and FKBP12. The capacity of these molecules to rescue mouse and human T-cell function in the presence of tacrolimus was determined in vitro, and the antitumor effect of the lead compound, Q-2361, was assessed in "regressor" models of skin cancer in immunosuppressed mice. Systemic CD8 T-cell depletion and analyses of intratumoral T-cell activation markers and effector molecule production were performed to determine the mechanism of tumor rejection. Pharmacokinetic studies of topically applied Q-2361 were performed to assess skin and systemic drug exposure. RESULTS: Q-2361 potently blocked the interaction between tacrolimus and FKBP12 and reversed the inhibition of the nuclear factor of activated T cells activation by tacrolimus following T-cell receptor engagement in human Jurkat cells. Q-2361 rescued T-cell function in the presence of tacrolimus, rapamycin, and everolimus. Intratumoral injection of Q-2361-induced tumor regression in mice systemically immune suppressed with tacrolimus. Mechanistically, Q-2361 treatment permitted T-cell activation, proliferation, and effector function within tumors. When CD8 T cells were depleted, Q-2361 could not induce tumor regression. A simple solution-based Q-2361 topical formulation achieved high and sustained residence in the skin with negligible drug in the blood. CONCLUSIONS: Our findings demonstrate that the local application of Q-2361 permits T-cells to become activated driving tumor rejection in the presence of tacrolimus. The data presented here suggests that topically applied Q-2361 has great potential for the reactivation of T-cells in the skin but not systemically, and therefore represents a promising strategy to prevent or treat skin malignancies in immunosuppressed organ transplant recipients.