Expert Consensus on the Use of Prognostic Gene Expression Profiling Tests for the Management of Cutaneous Melanoma: Consensus from the Skin Cancer Prevention Working Group

Improving systemic therapy selection for inflammatory skin diseases: A clinical need survey

Background

Empirical decisions to select therapies for psoriasis (PSO) and atopic dermatitis (AD) can lead to delays in disease control and increased health care costs. However, routine molecular testing for AD and PSO are lacking.

Objective

To examine (1) how clinicians choose systemic therapies for patients with PSO and AD without molecular testing and (2) to determine how often the current approach leads to patients switching medications.

Methods

A 20-question survey designed to assess clinician strategies for systemic treatment of AD and PSO was made available to attendees of a national dermatology conference in 2022.

Results

Clinicians participating in the survey (265/414, 64% response rate) ranked "reported efficacy" as the most important factor governing treatment choice (P < .001). However, 62% (165/265) of clinicians estimated that 2 or more systemic medications were typically required to achieve efficacy. Over 90% (239/265) of respondents would or would likely find a molecular test to guide therapeutic selection useful.

Limitations

To facilitate ease of recall, questions focused on systemic therapies as a whole and not individual therapies.

Conclusion

Clinicians want a molecular test to help determine the most efficacious drug for individual patients.

Impact of an Online Risk Calculator for Sentinel Node Positivity on Management of Patients with T1 and T2 Melanomas

BACKGROUND

Predicting which patients with American Joint Committee on Cancer (AJCC) T1-T2 melanomas will have a positive sentinel lymph node (SLN) is challenging. Melanoma Institute Australia (MIA) developed an internationally validated SLN metastatic risk calculator. This study evaluated the nomogram's impact on T1-T2 melanoma patient management at MIA.

METHODS

SLN biopsy (SLNB) rates were compared for the pre- and post-nomogram periods of 1 July 2018-30 June 2019 and 1 August 2020-31 July 2021, respectively.

RESULTS

Overall, 850 patients were identified (pre-nomogram, 383; post-nomogram, 467). SLNB was performed in 29.0% of patients in the pre-nomogram group and 34.5% in the post-nomogram group (p = 0.091). The overall positivity rate was 16.2% in the pre-nomogram group and 14.9% in the post-nomogram group (p = 0.223). SLNB was performed less frequently in T1a melanoma patients in the pre-nomogram group (1.1%, n = 2/177) than in the post-nomogram group (8.6%, n = 17/198) [p ≤ 0.001]. This increase was particularly for melanomas with a risk score ≥ 5%, with an SLN positivity rate of 11.8% in the post-nomogram group (p = 0.004) compared with zero. For T1b melanomas with a risk score of > 10%, the SLNB rate was 40.0% (8/20) pre-nomogram and 75.0% (12/16) post-nomogram (p = 0.049).

CONCLUSIONS

In this specialized center, the SLN risk calculator appears to influence practice for melanomas previously considered low risk for metastasis, with increased use of SLNB for T1a and higher-risk T1b melanomas. Further evaluation is required across broader practice settings. Melanoma management guidelines could be updated to incorporate the availability of nomograms to better select patients for SLNB than previous criteria.

Validation of epidermal AMBRA1 and loricrin (AMBLor) as a prognostic biomarker for nonulcerated American Joint Committee on Cancer stage I/II cutaneous melanoma

BACKGROUND

Combined expression of the autophagy-regulatory protein AMBRA1 (activating molecule in Beclin1-regulated autophagy) and the terminal differentiation marker loricrin in the peritumoral epidermis of stage I melanomas can identify tumour subsets at low risk of -metastasis.

OBJECTIVES

To validate the combined expression of peritumoral AMBRA1 and loricrin (AMBLor) as a prognostic biomarker able to identify both stage I and II melanomas at low risk of tumour recurrence.

METHODS

Automated immunohistochemistry was used to analyse peritumoral AMBRA1 and loricrin expression in geographically distinct discovery (n = 540) and validation (n = 300) cohorts of nonulcerated American Joint Committee on Cancer (AJCC) stage I and II melanomas. AMBLor status was correlated with clinical outcomes in the discovery and validation cohorts separately and combined.

RESULTS

Analysis of AMBLor in the discovery cohort revealed a recurrence-free survival (RFS) rate of 95.5% in the AMBLor low-risk group vs. 81.7% in the AMBLor at-risk group (multivariate log-rank, P < 0.001) and a negative predictive value (NPV) of 96.0%. In the validation cohort, AMBLor analysis revealed a RFS rate of 97.6% in the AMBLor low-risk group vs. 78.3% in the at-risk group (multivariate log-rank, P < 0.001) and a NPV of 97.6%. In a multivariate model considering AMBLor, Breslow thickness, age and sex, analysis of the combined discovery and validation cohorts showed that the estimated effect of AMBLor was statistically significant, with a hazard ratio of 3.469 (95% confidence interval 1.403-8.580, P = 0.007) and an overall NPV of 96.5%.

CONCLUSIONS

These data provide further evidence validating AMBLor as a prognostic biomarker to identify nonulcerated AJCC stage I and II melanoma tumours at low risk of disease recurrence.

Prognostic and predictive biomarkers in melanoma

Biomarkers help to inform the clinical management of patients with melanoma. For patients with clinically localised primary melanoma, biomarkers can help to predict post-surgical outcome (including via the use of risk prediction tools), better select patients for sentinel lymph node biopsy, and tailor catch-all follow-up protocols to the individual. Systemic drug treatments, including immune checkpoint inhibitor (ICI) therapies and BRAF-targeted therapies, have radically improved the prognosis of metastatic (stage III and IV) cutaneous melanoma patients, and also shown benefit in the earlier setting of stage IIB/C primary melanoma. Unfortunately, a response is far from guaranteed. Here, we review clinically relevant, established, and emerging, prognostic, and predictive pathological biomarkers that refine clinical decision-making in primary and metastatic melanoma patients. Gene expression profile assays and nomograms are emerging tools for prognostication and sentinel lymph node risk prediction in primary melanoma patients. Biomarkers incorporated into clinical practice guidelines include BRAF V600 mutations for the use of targeted therapies in metastatic cutaneous melanoma, and the HLA-A∗02:01 allele for the use of a bispecific fusion protein in metastatic uveal melanoma. Several predictive biomarkers have been proposed for ICI therapies but have not been incorporated into Australian clinical practice guidelines. Further research, validation, and assessment of clinical utility is required before more prognostic and predictive biomarkers are fluidly integrated into routine care.

The Use of Gene Expression Profiling and Biomarkers in Melanoma Diagnosis and Predicting Recurrence: Implications for Surveillance and Treatment

Cutaneous melanoma is becoming more prevalent in the United States and has the highest mortality among cutaneous malignancies. The majority of melanomas are diagnosed at an early stage and, as such, survival is generally favorable. However, there remains prognostic uncertainty among subsets of early- and intermediate-stage melanoma patients, some of whom go on to develop advanced disease while others remain disease-free. Melanoma gene expression profiling (GEP) has evolved with the notion to help bridge this gap and identify higher- or lower-risk patients to better tailor treatment and surveillance protocols. These tests seek to prognosticate melanomas independently of established AJCC 8 cancer staging and clinicopathologic features (sex, age, primary tumor location, thickness, ulceration, mitotic rate, lymphovascular invasion, microsatellites, and/or SLNB status). While there is a significant opportunity to improve the accuracy of melanoma prognostication and diagnosis, it is equally important to understand the current landscape of molecular profiling for melanoma treatment. Society guidelines currently do not recommend molecular testing outside of clinical trials for melanoma clinical decision making, citing insufficient high-quality evidence guiding indications for the testing and interpretation of results. The goal of this chapter is to review the available literature for GEP testing for melanoma diagnosis and prognostication and understand their place in current treatment paradigms.

An ensemble-based deep learning model for detection of mutation causing cutaneous melanoma

When the mutation affects the melanocytes of the body, a condition called melanoma results which is one of the deadliest skin cancers. Early detection of cutaneous melanoma is vital for raising the chances of survival. Melanoma can be due to inherited defective genes or due to environmental factors such as excessive sun exposure. The accuracy of the state-of-the-art computer-aided diagnosis systems is unsatisfactory. Moreover, the major drawback of medical imaging is the shortage of labeled data. Generalized classifiers are required to diagnose melanoma to avoid overfitting the dataset. To address these issues, blending ensemble-based deep learning (BEDLM-CMS) model is proposed to detect mutation of cutaneous melanoma by integrating long short-term memory (LSTM), Bi-directional LSTM (BLSTM) and gated recurrent unit (GRU) architectures. The dataset used in the proposed study contains 2608 human samples and 6778 mutations in total along with 75 types of genes. The most prominent genes that function as biomarkers for early diagnosis and prognosis are utilized. Multiple extraction techniques are used in this study to extract the most-prominent features. Afterwards, we applied different DL models optimized through grid search technique to diagnose melanoma. The validity of the results is confirmed using several techniques, including tenfold cross validation (10-FCVT), independent set (IST), and self-consistency (SCT). For validation of the results multiple metrics are used which include accuracy, specificity, sensitivity, and Matthews's correlation coefficient. BEDLM gives the highest accuracy of 97% in the independent set test whereas in self-consistency test and tenfold cross validation test it gives 94% and 93% accuracy, respectively. Accuracy of in self-consistency test, independent set test, and tenfold cross validation test is LSTM (96%, 94%, 92%), GRU (93%, 94%, 91%), and BLSTM (99%, 98%, 93%), respectively. The findings demonstrate that the proposed BEDLM-CMS can be used effectively applied for early diagnosis and treatment efficacy evaluation of cutaneous melanoma.

Advances in melanoma: epidemiology, diagnosis, and prognosis

Unraveling the multidimensional complexities of melanoma has required concerted efforts by dedicated community of researchers and clinicians battling against this deadly form of skin cancer. Remarkable advances have been made in the realm of epidemiology, classification, diagnosis, and therapy of melanoma. The treatment of advanced melanomas has entered the golden era as targeted personalized therapies have emerged that have significantly altered the mortality rate. A paradigm shift in the approach to melanoma classification, diagnosis, prognosis, and staging is underway, fueled by discoveries of genetic alterations in melanocytic neoplasms. A morphologic clinicopathologic classification of melanoma is expected to be replaced by a more precise molecular based one. As validated, convenient, and cost-effective molecular-based tests emerge, molecular diagnostics will play a greater role in the clinical and histologic diagnosis of melanoma. Artificial intelligence augmented clinical and histologic diagnosis of melanoma is expected to make the process more streamlined and efficient. A more accurate model of prognosis and staging of melanoma is emerging based on molecular understanding melanoma. This contribution summarizes the recent advances in melanoma epidemiology, classification, diagnosis, and prognosis.

Detection of micro-plasma-induced exosomes secretion in a fibroblast-melanoma co-culture model

BACKGROUND

Melanoma is a highly aggressive tumor and a significant cause of skin cancer-related death. Timely diagnosis and treatment require identification of specific biomarkers in exosomes secreted by melanoma cells. In this study, label-free surface-enhanced Raman spectroscopy (SERS) method with size-matched selectivity was used to detect membrane proteins in exosomes released from a stimulated environment of fibroblasts (L929) co-cultured with melanoma cells (B16-F10). To promote normal secretion of exosomes, micro-plasma treatment was used to gently induce the co-cultured cells and slightly increase the stress level around the cells for subsequent detection using the SERS method.

RESULTS AND DISCUSSION

Firstly, changes in reactive oxygen species/reactive nitrogen species (ROS/RNS) concentrations in the cellular microenvironment and the viability and proliferation of healthy cells are assessed. Results showed that micro-plasma treatment increased extracellular ROS/RNS levels while modestly reducing cell proliferation without significantly affecting cell survival. Secondly, the particle size of secreted exosomes isolated from the culture medium of L929, B16-F10, and co-cultured cells with different micro-plasma treatment time did not increase significantly under single-cell conditions at short treatment time but might be changed under co-culture condition or longer treatment time. Third, for SERS signals related to membrane protein biomarkers, exosome markers CD9, CD63, and CD81 can be assigned to significant Raman shifts in the range of 943-1030 and 1304-1561 cm-1, while the characteristics SERS peaks of L929 and B16-F10 cells are most likely located at 1394/1404, 1271 and 1592 cm-1 respectively.

SIGNIFICANCE AND NOVELTY

Therefore, this micro-plasma-induced co-culture model provides a promising preclinical approach to understand the diagnostic potential of exosomes secreted by cutaneous melanoma/fibroblasts. Furthermore, the label-free SERS method with size-matched selectivity provides a novel approach to screen biomarkers in exosomes secreted by melanoma cells, aiming to reduce the use of labeling reagents and the processing time traditionally required.

The Use of Single-Cell RNA-Sequencing and Spatial Transcriptomics in Understanding the Pathogenesis and Treatment of Skin Diseases

The development of multiomic profiling tools has rapidly expanded in recent years, along with their use in profiling skin tissues in various contexts, including dermatologic diseases. Among these tools, single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics (ST) have emerged as widely adopted and powerful assays for elucidating key cellular components and their spatial arrangement within skin disease. In this paper, we review the recent biological insights gained from the use of scRNA-seq and ST and the advantages of combining both for profiling skin diseases, including aberrant wound healing, inflammatory skin diseases, and cancer. We discuss the role of scRNA-seq and ST in improving skin disease treatments and moving toward the goal of achieving precision medicine in dermatology, whereby patients can be optimally matched to treatments that maximize therapeutic response.

Molecular testing in melanoma for the surgical pathologist

The diagnostic work-up of melanocytic tumours has undergone significant changes in the last years following the exponential growth of molecular assays. For the practising pathologist it is often difficult to sort through the multitude of different tests that are currently available for clinical use. The molecular tests used in melanocytic pathology can be broadly divided into four categories: (1) tests that predict response to systemic therapy in melanoma; (2) tests that predict prognosis in melanoma; (3) tests useful in determining the type or class of melanocytic tumour; and (4) tests useful in the differential diagnosis of naevus versus melanoma (primarily used as an aid in the diagnosis of histologically ambiguous melanocytic lesions). This review will present an updated synopsis of major molecular ancillary tests used in clinical practice.

Expression profile and prognostic values of LSM family in skin cutaneous melanoma

Background

The like-Smith (LSM) family is a group of RNA-binding proteins involved in RNA metabolism. However, their involvement in tumors, particularly skin cutaneous melanoma (SKCM), is not fully understood. In this study, we focused on the expression profiles and prognostic values of the LSM family in SKCM.

Methods

Raw data were downloaded from The Cancer Genome Atlas. The expression profile and prognostic value of LSM genes in SKCM were explored using the GEPIA, cBioPortal, and HPA databases. Protein–protein and gene–gene interaction analyses were performed using STRING and GeneMANIA. Enrichment and Cox regression analysis were conducted using R software. The TISIDB database was used to explore the relationship between LSMs and immunomodulators. Receiver operating characteristic curves and nomogram models were constructed to validate prognostic values.

Results

mRNA and protein expression levels of LSM2, LSM4, and LSM12 were significantly elevated in SKCM. The upregulated mRNA expression of LSM2 (p = 0.0013) and LSM4 (p = 0.0043) was significantly correlated with poor overall survival in patients with SKCM, whereas only LSM2 (p = 0.049) overexpression was markedly associated with worse disease-free survival. LSM2 overexpression was an independent risk factor (p = 0.013) and was confirmed to have a high prognostic value in SKCM using the receiver operating characteristic curve (AUC = 0.942) and nomogram models. All LSM genes were identified as genomic mutations, whereas alteration of LSM2 (p = 0.0153) significantly affected the overall survival in patients with SKCM. Significant correlations were observed between LSM family expression, immune cell infiltration, and immunomodulator. Furthermore, function and pathway enrichment analysis showed that the LSM family was mainly RNA binding proteins and involved in RNA splicing and degradation.

Conclusion

Expression profiles and prognostic values of LSM in SKCM were inconsistent. Among the LSM family, only LSM2 may serve as a potential poor prognosticator and immunotherapeutic target of SKCM.