Gene Expression Profile Testing for Thin Melanoma: Evidence to Support Clinical Use Remains Thin

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.

Breslow thickness 2.0: Why gene expression profiling is a step toward better patient selection for sentinel lymph node biopsies

Risk-stratification of cutaneous melanoma is important. Patients want to know what to expect after diagnosis, and physicians need to decide on a treatment plan. Historically, melanoma that had spread beyond the skin and regional lymph nodes was largely incurable, and the only approach to preventing a bad outcome was surgery. Through the seminal work of Alexander Breslow and Donald Morton, a system was devised to carefully escalate surgery based on primary tumor thickness and sentinel lymph node status. Today, we know that prophylactic lymph node dissections do not improve survival, but we continue to appreciate the prognostic implications of a positive sentinel node and the benefits of removing nodal metastases, which facilitates locoregional disease control. However, the question arises whether we can better select patients for sentinel lymph node biopsies (SLNB) as, currently, 85% of these procedures are negative and non-therapeutic. Here, we argue that gene expression profiling (GEP) of the diagnostic biopsy is a valuable step toward better patient selection when combined with reliable clinicopathologic (CP) information such as patient age and Breslow thickness. Recently, a CP-GEP-based classifier of nodal metastasis risk, the Merlin Assay, has become commercially available. While CP-GEP is still being validated in prospective studies, preliminary data suggest that it is an independent predictor of nodal metastasis, outperforming clinicopathological variables. The hunt is on for Breslow thickness 2.0.

Bioinformatic and Machine Learning Applications in Melanoma Risk Assessment and Prognosis: A Literature Review

Over 100,000 people are diagnosed with cutaneous melanoma each year in the United States. Despite recent advancements in metastatic melanoma treatment, such as immunotherapy, there are still over 7000 melanoma-related deaths each year. Melanoma is a highly heterogenous disease, and many underlying genetic drivers have been identified since the introduction of next-generation sequencing. Despite clinical staging guidelines, the prognosis of metastatic melanoma is variable and difficult to predict. Bioinformatic and machine learning analyses relying on genetic, clinical, and histopathologic inputs have been increasingly used to risk stratify melanoma patients with high accuracy. This literature review summarizes the key genetic drivers of melanoma and recent applications of bioinformatic and machine learning models in the risk stratification of melanoma patients. A robustly validated risk stratification tool can potentially guide the physician management of melanoma patients and ultimately improve patient outcomes.

Tumour gene expression signature in primary melanoma predicts long-term outcomes

Adjuvant systemic therapies are now routinely used following resection of stage III melanoma, however accurate prognostic information is needed to better stratify patients. We use differential expression analyses of primary tumours from 204 RNA-sequenced melanomas within a large adjuvant trial, identifying a 121 metastasis-associated gene signature. This signature strongly associated with progression-free (HR = 1.63, p = 5.24 × 10-5) and overall survival (HR = 1.61, p = 1.67 × 10-4), was validated in 175 regional lymph nodes metastasis as well as two externally ascertained datasets. The machine learning classification models trained using the signature genes performed significantly better in predicting metastases than models trained with clinical covariates (pAUROC = 7.03 × 10-4), or published prognostic signatures (pAUROC < 0.05). The signature score negatively correlated with measures of immune cell infiltration (ρ = -0.75, p < 2.2 × 10-16), with a higher score representing reduced lymphocyte infiltration and a higher 5-year risk of death in stage II melanoma. Our expression signature identifies melanoma patients at higher risk of metastases and warrants further evaluation in adjuvant clinical trials.

Evaluation of a Gene Expression Profiling Assay in Primary Cutaneous Melanoma

BACKGROUND

A significant proportion of deaths from cutaneous melanoma occur among patients with an initial diagnosis of stage 1 or 2 disease. The Decision-Dx Melanoma (DDM) 31-gene assay attempts to stratify these patients by risk of recurrence. This study aimed to evaluate this assay in a large single-institution series.

METHODS

A retrospective chart review of all patients who underwent surgery for melanoma at a large academic cancer center with DDM results was performed. Patient demographics, tumor pathologic characteristics, sentinel node status, gene expression profile (GEP) class, and recurrence-free survival (RFS) were reviewed. The primary outcomes were recurrence of melanoma and distant metastatic recurrence.

RESULTS

Data from 361 patients were analyzed. The median follow-up period was 15 months. Sentinel node biopsy was performed for 75.9% (n = 274) of the patients, 53 (19.4%) of whom tested positive. Overall, 13.6% (n = 49) of the patients had recurrence, and 8% (n = 29) had distant metastatic recurrence. The 3- and 5-year RFS rates were respectively 85% and 75% for the class 1A group, 74% and 47% for the class 1B/class 2A group, and 54% and 45% for the class 2B group. Increased Breslow thickness, ulceration, mitoses, sentinel node biopsy positivity, and GEP class 2B status were significantly associated with RFS and distant metastasis-free survival (DMFS) in the univariate analysis (all p < 0.05). In the multivariate analysis, only Breslow thickness and ulceration were associated with RFS (p < 0.003), and only Breslow thickness was associated with DMFS (p < 0.001).

CONCLUSION

Genetic profiling of cutaneous melanoma can assist in predicting recurrence and help determine the need for close surveillance. However, traditional pathologic factors remain the strongest independent predictors of recurrence risk.