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Refereed/Peer-reviewed Skin cancer is a significant and increasing global health burden. Although the current diagnostic workflow is robust and able to provide clinically actionable results, it is subject to notable limitations. The training and expertise required for accurate diagnoses using conventional skin cancer diagnostics are significant, and patient access to this workflow can be limited by geographic location or unforeseen events, such as coronavirus disease 2019 (COVID-19). Molecular biomarkers have transformed diagnostics and treatment delivery in oncology. With rapid advancements in molecular biology techniques, understanding of the underlying molecular mechanism of cancer pathologies has deepened, yielding biomarkers that can be used to monitor the course of malignant diseases. Herein, commercially available, clinically validated, and emerging skin cancer molecular biomarkers are reviewed. The qualities of an ideal molecular biomarker are defined. The potential benefits and limitations of applying molecular biomarker testing over the course of skin cancer from susceptibility to treatment are explored, with a view to outlining a future model of molecular biomarker skin cancer diagnostics.

Key Points Question What is the reliability of a more clinically focused, modified diagnostic classification system for diagnosing all lesions (ie, melanocytic and nonmelanocytic) excised to exclude melanoma and how is it associated with pathologists’ diagnostic confidence? Findings In this cohort study that included 197 patients with suspected melanoma, the interrater agreement overall of Management of Lesion to Exclude Melanoma (MOLEM) diagnosis vs majority diagnosis and by mean confidence level of pathologists was excellent. The overall accuracy of pathologist compared with the majority diagnosis based on concordance rates per MOLEM class was 88.6% (class I), 50.8% (class II), 76.2% (class III), 77.2% (class IV), and 74.5% (class V). Meaning These results suggest that combining melanocytic and nonmelanocytic lesions excised to exclude melanoma with similar prognostic outcomes into standardized classification categories may be more clinically relevant than the previous schema., This cohort study examines pathologist assessments of skin lesions with a high risk for melanoma presented at an Australian primary care skin clinic using a novel classification schema., Importance The proposed MOLEM (Management of Lesion to Exclude Melanoma) schema is more clinically relevant than Melanocytic Pathology Assessment Tool and Hierarchy for Diagnosis (MATH-Dx) for the management classification of melanocytic and nonmelanocytic lesions excised to exclude melanoma. A more standardized way of establishing diagnostic criteria will be crucial in the training of artificial intelligence (AI) algorithms. Objective To examine pathologists’ variability, reliability, and confidence in reporting melanocytic and nonmelanocytic lesions excised to exclude melanoma using the MOLEM schema in a population of higher-risk patients. Design, Setting, and Participants This cohort study enrolled higher-risk patients referred to a primary care skin clinic in New South Wales, Australia, between April 2019 and December 2019. Baseline demographic characteristics including age, sex, and related clinical details (eg, history of melanoma) were collected. Patients with lesions suspicious for melanoma assessed by a primary care physician underwent clinical evaluation, dermoscopy imaging, and subsequent excision biopsy of the suspected lesion(s). A total of 217 lesions removed and prepared by conventional histologic method and stained with hematoxylin-eosin were reviewed by up to 9 independent pathologists for diagnosis using the MOLEM reporting schema. Pathologists evaluating for MOLEM schema were masked to the original histopathologic diagnosis. Main Outcomes and Measures Characteristics of the lesions were described and the concordance of cases per MOLEM class was assessed. Interrater agreement and the agreement between pathologists’ ratings and the majority MOLEM diagnosis were calculated by Gwet AC1 with quadratic weighting applied. The diagnostic confidence of pathologists was then assessed. Results A total of 197 patients were included in the study (102 [51.8%] male; 95 [48.2%] female); mean (SD) age was 64.2 (15.8) years (range, 24-93 years). Overall, 217 index lesions were assessed with a total of 1516 histological diagnoses. Of 1516 diagnoses, 677 (44.7%) were classified as MOLEM class I; 120 (7.9%) as MOLEM class II; 564 (37.2%) as MOLEM class III; 114 (7.5%) as MOLEM class IV; and 55 (3.6%) as MOLEM class V. Concordance rates per MOLEM class were 88.6% (class I), 50.8% (class II), 76.2% (class III), 77.2% (class IV), and 74.2% (class V). The quadratic weighted interrater agreement was 91.3%, with a Gwet AC1 coefficient of 0.76 (95% CI, 0.72-0.81). The quadratic weighted agreement between pathologists’ ratings and majority MOLEM was 94.7%, with a Gwet AC1 coefficient of 0.86 (95% CI, 0.84-0.88). The confidence in diagnosis data showed a relatively high level of confidence (between 1.0 and 1.5) when diagnosing classes I (mean [SD], 1.3 [0.3]), IV (1.3 [0.3]) and V (1.1 [0.1]); while classes II (1.8 [0.2]) and III (1.5 [0.4]) were diagnosed with a lower level of pathologist confidence (≥1.5). The quadratic weighted interrater confidence rating agreement was 95.2%, with a Gwet AC1 coefficient of 0.92 (95% CI, 0.90-0.94) for the 1314 confidence ratings collected. The confidence agreement for each MOLEM class was 95.0% (class I), 93.5% (class II), 95.3% (class III), 96.5% (class IV), and 97.5% (class V). Conclusions and Relevance The proposed MOLEM schema better reflects clinical practice than the MPATH-Dx schema in lesions excised to exclude melanoma by combining diagnoses with similar prognostic outcomes for melanocytic and nonmelanocytic lesions into standardized classification categories. Pathologists’ level of confidence appeared to follow the MOLEM schema diagnostic concordance trend, ie, atypical naevi and melanoma in situ diagnoses were the least agreed upon and the most challenging for pathologists to confidently diagnose.

Actinic Keratosis (AK), Intraepidermal Carcinoma (IEC), and Squamous Cell Carcinoma (SCC) are generally considered to be advancing stages of the same disease spectrum. However, while AK often regress spontaneously, and IEC often regress in response to immune-activating treatments, SCC typically do not regress. Therefore, it is vital to define whether fundamental immunological changes occur during progression to SCC. Here we show that proinflammatory cytokine expression, chemokine expression, and immune cell infiltration density change during progression to SCC. Our findings suggest a switch from predominantly proinflammatory cytokine production to chemokine production is a key feature of progression from precancer to cancer. Together, these observations propose a model that can underpin current research and open new avenues of exploration into the clinical significance of these profiles with respect to immunotherapeutic or other treatment outcomes.

BLZ-100 (tozuleristide) is an intraoperative fluorescent imaging agent that selectively detects malignant tissue and can be used in real time to guide tumor resection. The purpose of this study was to assess the safety, tolerability, and pharmacokinetics of BLZ-100 and to explore the pharmacodynamics of fluorescence imaging of skin tumors. In this first-in-human study, BLZ-100 was administered intravenously to 21 adult patients 2 days before excising known or suspected skin cancers. Doses were 1, 3, 6, 12, and 18 mg, with 3–6 patients/cohort. Fluorescence imaging was conducted before and up to 48 h after dosing. BLZ-100 was well tolerated. There were no serious adverse events, deaths, or discontinuations due to adverse events, and no maximum tolerated dose (MTD) was identified. Headache (n = 2) and nausea (n = 2) were the only BLZ-100 treatment-related adverse events reported for >1 patient. Median time to maximal serum concentration was

This review analyses physical drug delivery enhancement technologies with a focus on improving UV damaged skin, actinic keratoses and non-melanoma skin cancer treatment. In recent years, physical drug delivery enhancement has been shown to enhance cosmeceutical and skin cancer treatment efficacy, but there are pros and cons to each approach which we discuss in detail. Mechanisms of action, clinical efficacy, experimental design, outcomes in academic publications, clinical trial reports and patents are explored to evaluate each technology with a critical, translation focused lens. We conclude that the commercial success of cosmeceutical applications,e.g. microneedles, will drive further innovation in this arena that will impact how actinic keratoses and non-melanoma skin cancers are clinically managed. Refereed/Peer-reviewed

WOS: 000381303000009, PubMed ID: 27166757, Here we have carried out a multiparameter analysis using a panel of 28 immunohistochemical markers to identify markers of transformation from benign and dysplastic naevus to primary melanoma in three separate cohorts totalling 279 lesions. We have identified a set of eight markers that distinguish naevi from melanoma. None of markers or parameters assessed differentiated benign from dysplastic naevi. Indeed, the naevi clustered tightly in terms of their immunostaining patterns whereas primary melanomas showed more diverse staining patterns. A small subset of histopathologically benign lesions had elevated levels of multiple markers associated with melanoma, suggesting that these represent naevi with an increased potential for transformation to melanoma., Australia Postgraduate Award; Australian Cancer Research Foundation (ACRF) for the Diamantina Individualised Oncology Care Centre at The University of Queensland Diamantina Institute; National Health and Medical Research Council (NHMRC)National Health and Medical Research Council of Australia [APP1087415]; National Health and Medical Research Council of AustraliaNational Health and Medical Research Council of Australia; Cancer Council QueenslandCancer Council Queensland, The authors acknowledge the assistance of Dr. Xin-Yi Chua from QFAB Bioinformatics for her assistance with the biostatistics. CF was supported by an Australia Postgraduate Award. KALC was supported in part by the Australian Cancer Research Foundation (ACRF) for the Diamantina Individualised Oncology Care Centre at The University of Queensland Diamantina Institute and the National Health and Medical Research Council (NHMRC) Career Development fellowship (APP1087415). BG is an NHMRC Senior Research Fellow. This work was supported by funding from the National Health and Medical Research Council of Australia and the Cancer Council Queensland.