Gene expression profiling for molecular staging of cutaneous melanoma in patients undergoing sentinel lymph node biopsy

Sentinel lymph node risk prognostication in primary cutaneous melanoma through tissue-based profiling, potentially redefining the need for sentinel lymph node biopsy

PURPOSE OF REVIEW

The role of Sentinel Lymph Node Biopsy (SLNB) is pivotal in the contemporary staging of cutaneous melanoma. In this review, we examine advanced molecular testing platforms like gene expression profiling (GEP) and immunohistochemistry (IHC) as tools for predicting the prognosis of sentinel lymph nodes. We compare these innovative approaches with traditional staging assessments. Additionally, we delve into the shared genetic and protein markers between GEP and IHC tests and their relevance to melanoma biology, exploring their prognostic and predictive characteristics. Finally, we assess alternative methods to potentially obviate the need for SLNB altogether.

RECENT FINDINGS

Progress in adjuvant melanoma therapy has diminished the necessity of Sentinel Lymph Node Biopsy (SLNB) while underscoring the importance of accurately identifying high-risk stage I and II melanoma patients who may benefit from additional anti-tumor interventions. The clinical application of testing through gene expression profiling (GEP) or immunohistochemistry (IHC) is gaining traction, with platforms such as DecisionDx, Merlin Assay (CP-GEP), MelaGenix GEP, and Immunoprint coming into play. Currently, extensive validation studies are in progress to incorporate routine molecular testing into clinical practice. However, due to significant methodological limitations, widespread clinical adoption of tissue-based molecular testing remains elusive at present.

SUMMARY

While various tissue-based molecular testing platforms have the potential to stratify the risk of sentinel lymph node positivity (SLNP), most suffer from significant methodological deficiencies, including limited sample size, lack of prospective validation, and limited correlation with established clinicopathological variables. Furthermore, the genes and proteins identified by individual gene expression profiling (GEP) or immunohistochemistry (IHC) tests exhibit minimal overlap, even when considering the most well-established melanoma mutations. However, there is hope that the ongoing prospective trial for the Merlin Assay may safely reduce the necessity for SLNB procedures if successful. Additionally, the MelaGenix GEP and Immunoprint tests could prove valuable in identifying high-risk stage I-II melanoma patients and potentially guiding their selection for adjuvant therapy, thus potentially reducing the need for SLNB. Due to the diverse study designs employed, effective comparisons between GEP or IHC tests are challenging, and to date, there is no study directly comparing the clinical utility of these respective GEP or IHC tests.

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.

The 31-Gene Expression Profile Test Outperforms AJCC in Stratifying Risk of Recurrence in Patients with Stage I Cutaneous Melanoma

BACKGROUND

Patients with stage I cutaneous melanoma (CM) are considered at low risk for metastasis or melanoma specific death; however, because the majority of patients are diagnosed with stage I disease, they represent the largest number of melanoma deaths annually. The 31-gene expression profile (31-GEP) test has been prospectively validated to provide prognostic information independent of staging, classifying patients as low (Class 1A), intermediate (Class 1B/2A), or high (Class 2B) risk of poor outcomes.

METHODS

Patients enrolled in previous studies of the 31-GEP were combined and evaluated for recurrence-free (RFS) and melanoma-specific survival (MSS) (n = 1261, "combined"). A second large, unselected real-world cohort (n = 5651) comprising clinically tested patients diagnosed 2013-2018 who were linked to outcomes data from the NCI Surveillance, Epidemiology, and End Results (SEER) Program registries was evaluated for MSS.

RESULTS

Combined cohort Class 1A patients had significantly higher RFS than Class 1B/2A or Class 2B patients (97.3%, 88.6%, 77.3%, p < 0.001)-better risk stratification than AJCC8 stage IA (97.5%) versus IB (89.3%). The SEER cohort showed better MSS stratification by the 31-GEP (Class 1A = 98.0%, Class 1B/2A = 97.5%, Class 2B = 92.3%; p < 0.001) than by AJCC8 staging (stage IA = 97.6%, stage IB = 97.9%; p < 0.001).

CONCLUSIONS

The 31-GEP test significantly improved patient risk stratification, independent of AJCC8 staging in patients with stage I CM. The 31-GEP provided greater separation between high- (Class 2B) and low-risk (Class 1A) groups than seen between AJCC stage IA and IB. These data support integrating the 31-GEP into clinical decision making for more risk-aligned management plans.

Sentinel lymph node assessment in melanoma: current state and future directions

Assessment of sentinel lymph node status is an important step in the evaluation of patients with melanoma for both prognosis and therapeutic management. Pathologists have an important role in this evaluation. The methodologies have varied over time, from the evaluation of dimensions of metastatic burden to determination of the location of the tumour deposits within the lymph node to precise cell counting. However, no single method of sentinel lymph node tumour burden measurement can currently be used as a sole independent predictor of prognosis. The management approach to sentinel lymph node-positive patients has also evolved over time, with a more conservative approach recently recognised for selected cases. This review gives an overview of past and current status in the field with a glimpse into future directions based on prior experiences and clinical trials.

Routine imaging guided by a 31-gene expression profile assay results in earlier detection of melanoma with decreased metastatic tumor burden compared to patients without surveillance imaging studies

Patients with early-stage disease typically have a good prognosis, but still have a risk of recurrence, even with negative sentinel lymph node biopsy (SLNB). This study explores the utility of routine imaging to detect metastases in patients with negative SLNB but high-risk 31 gene expression profile (31-GEP) scores. We retrospectively identified melanoma patients with negative SLNBs. Patients with high-risk GEP results were placed in the experimental group and patients without GEP testing were placed in the control group. Among both cohorts, recurrent melanoma groups were identified. The tumor burden at the time of recurrence and the time to recurrence were compared between experimental group patients with routine imaging and control group patients without imaging schedules. We identified 327 control patients and 307 experimental patients, of which 14.1% versus 20.5% had melanoma recurrence, respectively. Of the patients with recurrent melanoma, those in the experimental group were older (65.75 versus 59.20), had higher Breslow depths (3.72 mm versus 3.31 mm), and had advanced tumor staging (89.5% versus 71.4% of patients presenting clinical stage ≥ II) compared to the control group at primary diagnosis. However, melanoma recurrence was detected earlier (25.50 months versus 35.35 months) in the experimental group at a lower overall tumor burden (73.10 mm versus 27.60 mm). A higher percentage of experimental patients started immunotherapy when offered (76.3% and 67.9%). Patients who received routine imaging after high-risk GEP test scores had an earlier recurrence diagnosis with lower tumor burden, leading to better clinical outcomes.

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.

The 31-gene expression profile test informs sentinel lymph node biopsy decisions in patients with cutaneous melanoma: results of a prospective, multicenter study

BACKGROUND

The 31-gene expression profile test (Class 1A: low-risk; 1B/2A: intermediate-risk; 2B: high-risk) is validated to identify patients with cutaneous melanoma who can safely forego sentinel lymph node biopsy (SLNB). The objective of the current study is to quantify SLNB reduction by clinicians using 31-GEP.

METHODS

Patients with T1-T2 tumors eligible for SLNB were seen by surgical oncologists (89.1%), dermatologists (7.8%), and medical oncologists (3.1%). After receiving 31-GEP results but before SLNB, clinicians were asked which clinical and pathological features influenced SLNB decisions (n = 191). The Exact binomial test was used to compare SLNB procedure rates to a contemporary study (78% SLNB baseline rate). Logistic regression modeling (odds ratio [OR], 95% CI) was used to identify features associated with SLNB procedure rates.

RESULTS

One hundred clinical decisions (52.4%) were influenced by the 31-GEP to forego SLNB and 70% (70/100) were not performed. Of the 30 performed, 0% (0/30) were positive. The 31-GEP influenced sixty-three clinical decisions (33.0%) to perform SLNB, and 92.1% (58/63) were performed. There was a clinically meaningful 29.4% reduction of SLNBs performed in patients with a Class 1A result relative to the baseline rate of 78.0% (p < .01). In patients ≥55 or ≥65-year-old, SLNB reduction was 32.3% (p < .01), 28.3% (p < .01), respectively. Overall, 85.3% of decisions relating to SLNB were influenced by 31-GEP results.

CONCLUSION

In this prospective, multicenter study, clinicians demonstrated clinically meaningful use of the 31-GEP test to forego or pursue SLNB in patients with T1-T2 tumors resulting in a significant, risk appropriate decrease in SLNBs.

Identifying the Transcriptional Drivers of Metastasis Embedded within Localized Melanoma

In melanoma, predicting which tumors will ultimately metastasize guides treatment decisions. Transcriptional signatures of primary tumors have been utilized to predict metastasis, but which among these are driver or passenger events remains unclear. We used data from the adjuvant AVAST-M trial to identify a predictive gene signature in localized tumors that ultimately metastasized. Using a zebrafish model of primary melanoma, we interrogated the top genes from the AVAST-M signature in vivo. This identified GRAMD1B, a cholesterol transfer protein, as a bona fide metastasis suppressor, with a majority of knockout animals rapidly developing metastasis. Mechanistically, excess free cholesterol or its metabolite 27-hydroxycholesterol promotes invasiveness via activation of an AP-1 program, which is associated with increased metastasis in humans. Our data demonstrate that the transcriptional seeds of metastasis are embedded within localized tumors, suggesting that early targeting of these programs can be used to prevent metastatic relapse.

SIGNIFICANCE

We analyzed human melanoma transcriptomics data to identify a gene signature predictive of metastasis. To rapidly test clinical signatures, we built a genetic metastasis platform in adult zebrafish and identified GRAMD1B as a suppressor of melanoma metastasis. GRAMD1B-associated cholesterol overload activates an AP-1 program to promote melanoma invasion. This article is highlighted in the In This Issue feature, p. 1.

Tissue Biomarkers Predicting Lymph Node Status in Cutaneous Melanoma

Cutaneous melanoma is a severe neoplasm that shows early invasiveness of the lymph nodes draining the primary site, with increased risk of distant metastases and recurrence. The tissue biomarker identification could be a new frontier to predict the risk of early lymph node invasiveness, especially in cases considered by current guidelines to be at low risk of lymph node involvement and not requiring evaluation of the sentinel lymph node (SLN). For this reason, we present a narrative review of the literature, seeking to provide an overview of current tissue biomarkers, particularly vascular endothelium growth factors (VEGF), Tetraspanin CD9, lymphatic vessel endothelial hyaluronan receptor-1 (LYVE-1), D2-40, and gene expression profile test (31-GEP). Among these, 31-GEP seems to be able to provide a distinction between low or high risk for positive SLN classes. VEGF receptor-3 and CD9 expression may be independent predictors of positive SLN. Lastly, LYVE-1 and D2-40 allow an easier assessment of lymph vascular invasion, which can be considered a good predictor of SLN status. In conclusion, biomarkers to assess the lymph node status of cutaneous melanoma patients may play an important role in those cases where the clinician is in doubt whether or not to perform SLN biopsy.

Insights and Strategies of Melanoma Immunotherapy: Predictive Biomarkers of Response and Resistance and Strategies to Improve Response Rates

Despite the recent successes and durable responses with immune checkpoint inhibitors (ICI), many cancer patients, including those with melanoma, do not derive long-term benefits from ICI therapies. The lack of predictive biomarkers to stratify patients to targeted treatments has been the driver of primary treatment failure and represents an unmet medical need in melanoma and other cancers. Understanding genomic correlations with response and resistance to ICI will enhance cancer patients' benefits. Building on insights into interplay with the complex tumor microenvironment (TME), the ultimate goal should be assessing how the tumor 'instructs' the local immune system to create its privileged niche with a focus on genomic reprogramming within the TME. It is hypothesized that this genomic reprogramming determines the response to ICI. Furthermore, emerging genomic signatures of ICI response, including those related to neoantigens, antigen presentation, DNA repair, and oncogenic pathways, are gaining momentum. In addition, emerging data suggest a role for checkpoint regulators, T cell functionality, chromatin modifiers, and copy-number alterations in mediating the selective response to ICI. As such, efforts to contextualize genomic correlations with response into a more insightful understanding of tumor immune biology will help the development of novel biomarkers and therapeutic strategies to overcome ICI resistance.

Scalable transcriptomics analysis with Dask: applications in data science and machine learning

BACKGROUND

Gene expression studies are an important tool in biological and biomedical research. The signal carried in expression profiles helps derive signatures for the prediction, diagnosis and prognosis of different diseases. Data science and specifically machine learning have many applications in gene expression analysis. However, as the dimensionality of genomics datasets grows, scalable solutions become necessary.

METHODS

In this paper we review the main steps and bottlenecks in machine learning pipelines, as well as the main concepts behind scalable data science including those of concurrent and parallel programming. We discuss the benefits of the Dask framework and how it can be integrated with the Python scientific environment to perform data analysis in computational biology and bioinformatics.

RESULTS

This review illustrates the role of Dask for boosting data science applications in different case studies. Detailed documentation and code on these procedures is made available at https://github.com/martaccmoreno/gexp-ml-dask .

CONCLUSION

By showing when and how Dask can be used in transcriptomics analysis, this review will serve as an entry point to help genomic data scientists develop more scalable data analysis procedures.

A deep learning model based on whole slide images to predict disease-free survival in cutaneous melanoma patients

The application of deep learning on whole-slide histological images (WSIs) can reveal insights for clinical and basic tumor science investigations. Finding quantitative imaging biomarkers from WSIs directly for the prediction of disease-free survival (DFS) in stage I-III melanoma patients is crucial to optimize patient management. In this study, we designed a deep learning-based model with the aim of learning prognostic biomarkers from WSIs to predict 1-year DFS in cutaneous melanoma patients. First, WSIs referred to a cohort of 43 patients (31 DF cases, 12 non-DF cases) from the Clinical Proteomic Tumor Analysis Consortium Cutaneous Melanoma (CPTAC-CM) public database were firstly annotated by our expert pathologists and then automatically split into crops, which were later employed to train and validate the proposed model using a fivefold cross-validation scheme for 5 rounds. Then, the model was further validated on WSIs related to an independent test, i.e. a validation cohort of 11 melanoma patients (8 DF cases, 3 non-DF cases), whose data were collected from Istituto Tumori 'Giovanni Paolo II' in Bari, Italy. The quantitative imaging biomarkers extracted by the proposed model showed prognostic power, achieving a median AUC value of 69.5% and a median accuracy of 72.7% on the public cohort of patients. These results remained comparable on the validation cohort of patients with an AUC value of 66.7% and an accuracy value of 72.7%, respectively. This work is contributing to the recently undertaken investigation on how treat features extracted from raw WSIs to fulfil prognostic tasks involving melanoma patients. The promising results make this study as a valuable basis for future research investigation on wider cohorts of patients referred to our Institute.

Discovery of pathway-independent protein signatures associated with clinical outcome in human cancer cohorts

Proteomic data provide a direct readout of protein function, thus constituting an information-rich resource for prognostic and predictive modeling. However, protein array data may not fully capture pathway activity due to the limited number of molecules and incomplete pathway coverage compared to other high-throughput technologies. For the present study, our aim was to improve clinical outcome prediction compared to published pathway-dependent prognostic signatures for The Cancer Genome Atlas (TCGA) cohorts using the least absolute shrinkage and selection operator (LASSO). RPPA data is particularly well-suited to the LASSO due to the relatively low number of predictors compared to larger genomic data matrices. Our approach selected predictors regardless of their pathway membership and optimally combined their RPPA measurements into a weighted risk score. Performance was assessed and compared to that of the published signatures using two unbiased approaches: 1) 10 iterations of threefold cross-validation for unbiased estimation of hazard ratio and difference in 5-year survival (by Kaplan-Meier method) between predictor-defined high and low risk groups; and 2) a permutation test to evaluate the statistical significance of the cross-validated log-rank statistic. Here, we demonstrate strong stratification of 445 renal clear cell carcinoma tumors from The Cancer Genome Atlas (TCGA) into high and low risk groups using LASSO regression on RPPA data. Median cross-validated difference in 5-year overall survival was 32.8%, compared to 25.2% using a published receptor tyrosine kinase (RTK) prognostic signature (median hazard ratios of 3.3 and 2.4, respectively). Applicability and performance of our approach was demonstrated in three additional TCGA cohorts: ovarian serous cystadenocarcinoma (OVCA), sarcoma (SARC), and cutaneous melanoma (SKCM). The data-driven LASSO-based approach is versatile and well-suited for discovery of new protein/disease associations.

Protein expression of prognostic genes in primary melanoma and benign nevi

PURPOSE

To evaluate the protein expression characteristics of genes employed in a recently introduced prognostic gene expression assay for patients with cutaneous melanoma (CM).

METHODS

We studied 37 patients with CM and 10 with benign (melanocytic) nevi (BN). Immunohistochemistry of primary tumor tissue was performed for eight proteins: COL6A6, DCD, GBP4, KLHL41, KRT9, PIP, SCGB1D2, SCGB2A2.

RESULTS

The protein expression of most markers investigated was relatively low (e.g., DCD, KRT9, SCGB1D2) and predominantly cytoplasmatic in melanocytes and keratinocytes. COL6A6, GBP4, and KLHL41 expression was significantly enhanced in CM when compared to BN. DCD protein expression was significantly correlated with COL6A6, GBP4, and KLHL41. GBP4 was positively correlated with KLHL41 and inversely correlated with SCGB2B2. The latter was also inversely correlated with serum S100B levels at time of initial diagnosis. The presence of SCGB1D2 expression was significantly associated with ulceration of the primary tumor. KRT9 protein expression was significantly more likely found in acral lentiginous melanoma. The presence of DCD expression was less likely associated with superficial spreading melanoma subtype but significantly associated with non-progressive disease. The absence of SCGB2A2 expression was significantly more often observed in patients who did not progress to stage III or IV.

CONCLUSIONS

The expression levels observed were relatively low but differed in part with those found in BN. Even though we detected some significant correlations between the protein expression levels and clinical parameters (e.g., CM subtype, course of disease), there was no major concordance with the protective or risk-associated functions of the corresponding genes included in a recently introduced prognostic gene expression assay.

Two subtypes of cutaneous melanoma with distinct mutational signatures and clinico-genomic characteristics

Background: To decipher mutational signatures and their associations with biological implications in cutaneous melanomas (CMs), including those with a low ultraviolet (UV) signature.

Materials and Methods: We applied non-negative matrix factorization (NMF) and unsupervised clustering to the 96-class mutational context of The Cancer Genome Atlas (TCGA) cohort (N = 466) as well as other publicly available datasets (N = 527). To explore the feasibility of mutational signature-based classification using panel sequencing data, independent panel sequencing data were analyzed.

Results: NMF decomposition of the TCGA cohort and other publicly available datasets consistently found two mutational signatures: UV (SBS7a/7b dominant) and non-UV (SBS1/5 dominant) signatures. Based on mutational signatures, TCGA CMs were classified into two clusters: UV-high and UV-low. CMs belonging to the UV-low cluster showed significantly worse overall survival and landmark survival at 1-year than those in the UV-high cluster; low or high UV signature remained the most significant prognostic factor in multivariate analysis. The UV-low cluster showed distinct genomic and functional characteristic patterns: low mutation counts, increased proportion of triple wild-type and KIT mutations, high burden of copy number alteration, expression of genes related to keratinocyte differentiation, and low activation of tumor immunity. We verified that UV-high and UV-low clusters can be distinguished by panel sequencing.

Conclusion: Our study revealed two mutational signatures of CMs that divide CMs into two clusters with distinct clinico-genomic characteristics. Our results will be helpful for the clinical application of mutational signature-based classification of CMs.

Dermatologic Disease-Directed Targeted Therapy (D3T2): The Application of Biomarker-Based Precision Medicine for the Personalized Treatment of Skin Conditions-Precision Dermatology

Precision dermatology uses individualized dermatologic disease-directed targeted therapy (D³T²) for the management of dermatoses and for the evaluation and therapy of cutaneous malignancies. Personalized/precision strategies are based on biomarkers that are most frequently derived from tissue transcriptomic expression or genomic sequencing or from circulating cytokines. For instance, the pathologic diagnosis of a pigmented lesion and determining the prognosis of a malignant melanocytic neoplasm can be enhanced by genomic/transcriptomic analysis. In addition to biopsy, innovative techniques have been developed for obtaining transcriptomes in skin conditions; as an example, patches can be applied to a psoriasis plaque for a few minutes to capture the epidermis/upper dermis transcriptome. Atopic dermatitis and prurigo nodularis may also be candidate conditions for precision dermatology. Precision dermatology has a role in managing melanoma and nonmelanoma skin cancers and rare cutaneous tumors-such as perivascular epithelioid cell tumor (PEComa)-that can originate in or metastasize to the skin. For instance, advanced/metastatic basal cell carcinomas can be treated with Hedgehog inhibitors (vismodegib and sonidegib) targeting the smoothened (SMO) or patched 1 (PTCH1) gene alterations that are a hallmark of these cancers and activate the Hedgehog pathway. Advanced/metastatic basal and cutaneous squamous cell cancers often have a high tumor mutational burden (which predicts immunotherapy response); immune checkpoint blockade with cemiplimab, a programmed cell death protein 1 (PD1) inhibitor, is now approved for these malignancies. Gene expression profiling of primary cutaneous squamous cell carcinoma can identify those individuals at high risk for subsequent metastases. In the realm of rare neoplasms, PEComas-which can originate in the skin, albeit uncommonly-have tuberous sclerosis complex 1 (TSC1)/tuberous sclerosis complex 2 (TSC2) gene alterations, which activate mammalian target of rapamycin (mTOR) signaling, and can be suppressed by nab-sirolimus, now approved for this condition. In summary, precision dermatologic techniques/strategies are an important emerging approach for evaluation and management of skin disorders and cutaneous neoplasms, and may serve as a paradigm for the application of precision medicine beyond dermatology.

Attitudes of patients with cutaneous melanoma toward prognostic testing using the 31-gene expression profile test

OBJECTIVE

Although most patients diagnosed with early-stage cutaneous melanoma (CM) have excellent outcomes, because of the large number diagnosed each year, many will experience recurrence or death. Prognostic testing for CM using the 31-gene expression profile (31-GEP) test can benefit patients by helping guide risk-appropriate treatment and surveillance plans. We sought to evaluate patients' attitudes toward prognostic testing with the 31-GEP and assess whether patients experience decision regret about having 31-GEP testing.

METHODS

A 43-question survey was distributed by the Melanoma Research Foundation in June-August 2021 to CM patients enrolled in their database. Patients were asked questions regarding their decision to undergo 31-GEP testing and the extent to which they experienced decision regret using a validated set of Decision Regret Scale questions.

RESULTS

We analyzed responses from patients diagnosed in 2014 or later (n  = 120). Of these, 28 had received 31-GEP testing. Most respondents (n  = 108, 90%) desired prognostic information when diagnosed. Of those who received 31-GEP testing, most felt the results were useful (n  = 22 out of 24) and had regret scores significantly less than neutral regret, regardless of their test results (Class 1: p  < 0.001; Class 2: p  = 0.036). Further, decision regret scores were not significantly different between patients who received a Class 1 31-GEP result and those who received a Class 2 result (mean Class 1 = 1.39 and mean Class 2 = 1.90, p  = 0.058).

CONCLUSIONS

Most newly diagnosed CM patients desired prognostic information about their tumors. Patients who received 31-GEP testing felt it was useful and did not regret their decision to undergo 31-GEP testing.

Expanded evidence that the 31-gene expression profile test provides clinical utility for melanoma management in a multicenter study

OBJECTIVE

National Comprehensive Cancer Network (NCCN) guidelines for cutaneous melanoma (CM) recommend physicians consider increased surveillance for patients who typically have lower melanoma survival rates (stages IIB-IV as determined by the American Joint Committee on Cancer (AJCC), 8th edition). However, up to 15% of patients identified as having a low recurrence risk (stages I-IIA) experience disease recurrence, and some patients identified as having a high recurrence risk will not experience any recurrence. The 31-gene expression profile test (31-GEP) stratifies patient recurrence risk into low (Class 1) and high (Class 2) and has demonstrated risk-appropriate impact on disease management and clinical decisions.

METHODS

Five-year plans for lab work, frequency of clinical visits, and imaging pre- and post-31-GEP test results were assessed for a cohort of 509 stage I-III patients following an interim subset analysis of 247 patients.

RESULTS

After receiving 31-GEP results, 50.6% of patients had a change in management plans in at least one of the following categories-clinical visits, lab work, or surveillance imaging. The changes aligned with the risk predicted by the 31-GEP for 76.1% of patients with a Class 1 result and 78.7% of patients with a Class 2 result. A Class 1 31-GEP result was associated with changes toward low-intensity management recommendations, while a Class 2 result was associated with changes toward high-intensity management recommendations.

CONCLUSION

The 31-GEP can stratify patient recurrence risk in patients with CM, and clinicians understand and apply the prognostic ability of the 31-GEP test to alter patient management in risk-appropriate directions.

High-Plex Spatial RNA Profiling Reveals Cell Type‒Specific Biomarker Expression during Melanoma Development

Early diagnosis of melanoma is critical for improved survival. However, the biomarkers of early melanoma evolution and their origin within the tumor and its microenvironment, including the keratinocytes, are poorly defined. To address this, we used spatial transcript profiling that maintains the morphological tumor context to measure the expression of >1,000 RNAs in situ in patient-derived formalin-fixed, paraffin-embedded tissue sections in primary melanoma and melanocytic nevi. We profiled 134 regions of interest (each 200 μm in diameter) enriched in melanocytes, neighboring keratinocytes, or immune cells. This approach captured distinct expression patterns across cell types and tumor types during melanoma development. Unexpectedly, we discovered that S100A8 is expressed by keratinocytes within the tumor microenvironment during melanoma growth. Immunohistochemistry of 252 tumors showed prominent keratinocyte-derived S100A8 expression in melanoma but not in benign tumors and confirmed the same pattern for S100A8's binding partner S100A9, suggesting that injury to the epidermis may be an early and readily detectable indicator of melanoma development. Together, our results establish a framework for high-plex, spatial, and cell type‒specific resolution of gene expression in archival tissue applicable to the development of biomarkers and characterization of tumor microenvironment interactions in tumor evolution.

Development and validation of a nomogram incorporating gene expression profiling and clinical factors for accurate prediction of metastasis in patients with cutaneous melanoma following Mohs micrographic surgery

BACKGROUND

There is a need to improve prognostic accuracy for patients with cutaneous melanoma. A 31-gene expression profile (31-GEP) test uses the molecular biology of primary tumors to identify individual patient metastatic risk.

OBJECTIVE

Develop a nomogram incorporating 31-GEP with relevant clinical factors to improve prognostic accuracy.

METHODS

In an IRB-approved study, 1124 patients from 9 Mohs micrographic surgery centers were prospectively enrolled, treated with Mohs micrographic surgery, and underwent 31-GEP testing. Data from 684 of those patients with at least 1-year follow-up or a metastatic event were included in nomogram development to predict metastatic risk.

RESULTS

Logistic regression modeling of 31-GEP results and T stage provided the simplest nomogram with the lowest Bayesian information criteria score. Validation in an archival cohort (n = 901) demonstrated a significant linear correlation between observed and nomogram-predicted risk of metastasis. The resulting nomogram more accurately predicts the risk for cutaneous melanoma metastasis than T stage or 31-GEP alone.

LIMITATIONS

The patient population is representative of Mohs micrographic surgery centers. Sentinel lymph node biopsy was not performed for most patients and could not be used in the nomogram.

CONCLUSIONS

Integration of 31-GEP and T stage can gain clinically useful prognostic information from data obtained noninvasively.

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

BACKGROUND

Prognostic assessment of cutaneous melanoma relies on historical, clinicopathological, and phenotypic risk factors according to American Joint Committee on Cancer(AJCC) and National Comprehensive Cancer Network (NCCN) guidelines but may not account for a patient's individual additional genetic risk factors.

OBJECTIVE

To review the available literature regarding commercially available gene expression profile (GEP) tests and their use in the management of cutaneous melanoma.

METHODS

A literature search was conducted for original, English-language studies or meta-analyses published between 2010 and 2021 on commercially available GEP tests in cutaneous melanoma prognosis, clinical decision-making regarding sentinel lymph node biopsy, and real-world efficacy. After the literature review, the Skin Cancer Prevention Working Group, an expert panel of dermatologists with specialized training in melanoma and non-melanoma skin cancer diagnosis and management, utilized a modified Delphi technique to develop consensus statements regarding prognostic gene expression profile tests. Statements were only adopted with a supermajority vote of > 80%.

RESULTS

The initial search identified 1064 studies/meta-analyses that met the search criteria. Of these, we included 21 original articles and meta-analyses that studied the 31-GEP test (DecisionDx-Melanoma; Castle Biosciences, Inc.), five original articles that studied the 11-GEP test (Melagenix; NeraCare GmbH), and four original articles that studied the 8-GEP test with clinicopathological factors (Merlin; 8-GEP + CP; SkylineDx B.V.) in this review. Six statements received supermajority approval and were adopted by the panel.

CONCLUSION

GEP tests provide additional, reproducible information for dermatologists to consider within the larger framework of the eighth edition of the AJCC and NCCN cutaneous melanoma guidelines when counseling regarding prognosis and when considering a sentinel lymph node biopsy.

"The Benefit Of Positron Emission Tomography/Computed Tomography In Stage I And Stage II Melanomas With High-Risk Decisiondx-Melanoma Scores"

INTRODUCTION

Early detection of melanoma is instrumental as the 5-year survival decreases from 93.3% to <50% when metastases are present.^1-3 Distinguishing which patients require closer follow-up can be difficult for melanoma patients. Developments by Castle Biosciences' (Friendswood, TX) DecisionDx-Melanoma (DDx-M) use 31 melanoma associated genes to stratify melanomas into 4 classes with 1A having lowest risk of morbidity and mortality and 2B the highest.⁵ We assessed the benefit of providing additional ¹⁸FDG-PET-CT and brain MRI to genetically high-risk patients who may have otherwise been overlooked.

METHODS

297 patients at our institution had biopsies sent for DDx-M between 2014 and 2021. Patients found to have Class 2 melanomas received additional screening with yearly ¹⁸FDG-PET-CT scans and brain MRIs. Patients with Class 2 DDx-M scores and negative SLNB were included in the study. 66 met inclusion criteria and received imaging.

RESULTS

Within 3 years of follow-up, 8/66 (12.1%) patients had metastases detected by ¹⁸FDG-PET-CT scans. No patients with stage IA or IB went on to develop metastases.

DISCUSSION

¹⁸FDG-PET-CT scans detect metastases in < 3% of the time when all stage I and II patients are scanned; however, by using DDx-M in our screening protocols, we achieved a detection rate of 12.1%.^6,7 These patients went on to receive treatment and would have otherwise progressed undetected, leading to higher morbidity and mortality.

CONCLUSION

We suggest all patients with initial stage II or above melanomas receive a DDx-M score and those with class 2 receive yearly ¹⁸FDG-PET-CT/brain MRI imaging.

Machine learning directed sentinel lymph node biopsy in cutaneous head and neck melanoma

BACKGROUND

The specificity of sentinel lymph node biopsy (SLNB) for detecting lymph node metastasis in head and neck melanoma (HNM) is low under current National Comprehensive Cancer Network (NCCN) treatment guidelines.

METHODS

Multiple machine learning (ML) algorithms were developed to identify HNM patients at very low risk of occult nodal metastasis using National Cancer Database (NCDB) data from 8466 clinically node negative HNM patients who underwent SLNB. SLNB performance under NCCN guidelines and ML algorithm recommendations was compared on independent test data from the NCDB (n = 2117) and an academic medical center (n = 96).

RESULTS

The top-performing ML algorithm (AUC = 0.734) recommendations obtained significantly higher specificity compared to the NCCN guidelines in both internal (25.8% vs. 11.3%, p < 0.001) and external test populations (30.1% vs. 7.1%, p < 0.001), while achieving sensitivity >97%.

CONCLUSION

Machine learning can identify clinically node negative HNM patients at very low risk of nodal metastasis, who may not benefit from SLNB.

Sentinel Lymph Node Biopsy Positivity in Patients With Acral Lentiginous and Other Subtypes of Cutaneous Melanoma

IMPORTANCE

Acral lentiginous melanoma (ALM) is a rare subtype of malignant melanoma typically occurring on the palmar and plantar surfaces. Although it has distinctive genetic, prognostic, and behavioral characteristics relative to cutaneous melanomas overall, owing to its rarity, treatment is largely guided by data extrapolated from more common subtypes. Although sentinel lymph node (SLN) status has been shown to be a significant prognostic factor for ALM, the independent effect of ALM-subtype disease on the likelihood of SLN positivity and the stage-specific positivity rates for ALM are not well characterized.

OBJECTIVE

To evaluate the association of ALM with SLN status as well as to characterize the clinical stage-specific rates of SLN positivity for ALM based on the AJCC Cancer Staging Manual, 8th edition (AJCC-8).

DESIGN, SETTING, AND PARTICIPANTS

The National Cancer Database (NCDB) includes all reportable cases from Commission on Cancer accredited facilities and represents approximately 50% of all newly diagnosed melanoma cases in the US. This retrospective cohort study included cases of AJCC-8 clinical stage I to II melanomas from the NCDB diagnosed from 2012 to 2015. The analysis took place between April 2021 and September 2021.

EXPOSURES

Melanoma histopathologic subtype.

MAIN OUTCOMES AND MEASURES

Sentinel lymph node status.

RESULTS

We identified 60 148 patients with malignant melanomas, 959 of whom had ALM-subtype disease. Among patients in the cohort, 25 550 (42.5%) were women and the mean (SD) age was 64 (16) years. Multivariable logistic regression controlling for demographic and histopathologic characteristics revealed that ALM was independently associated with the highest risk for SLN positivity among included subtypes (vs superficial spreading melanoma: odds ratio, 1.91; 95% CI, 1.59-2.28). Subgroup analysis by AJCC clinical stage demonstrated that ALM was independently associated with the highest risk for SLN positivity for both stage IB and II disease. The rate of SLN positivity for patients with stage IB and II ALM was 18.39% (95% CI, 13.82%-24.03%) and 39.53% (34.98%-44.26%), respectively.

CONCLUSIONS AND RELEVANCE

In this cohort study ALM was independently associated with SLN positivity and had relatively high positivity rates at clinical stage IB and II. This suggests that SLNB should be encouraged for all patients with clinical stage IB and II ALM, and such patients should receive appropriate counseling about the higher regional metastatic risk of their cancers. Future work with a larger cohort is required to elucidate the risk of SLN positivity for stage IA ALM.

Impact of Next-generation Sequencing on Interobserver Agreement and Diagnosis of Spitzoid Neoplasms

Atypical Spitzoid melanocytic tumors are diagnostically challenging. Many studies have suggested various genomic markers to improve classification and prognostication. We aimed to assess whether next-generation sequencing studies using the Tempus xO assay assessing mutations in 1711 cancer-related genes and performing whole transcriptome mRNA sequencing for structural alterations could improve diagnostic agreement and accuracy in assessing neoplasms with Spitzoid histologic features. Twenty expert pathologists were asked to review 70 consultation level cases with Spitzoid features, once with limited clinical information and again with additional genomic information. There was an improvement in overall agreement with additional genomic information. Most significantly, there was increase in agreement of the diagnosis of conventional melanoma from moderate (κ=0.470, SE=0.0105) to substantial (κ=0.645, SE=0.0143) as measured by an average Cohen κ. Clinical follow-up was available in all 70 cases which substantiated that the improved agreement was clinically significant. Among 3 patients with distant metastatic disease, there was a highly significant increase in diagnostic recognition of the cases as conventional melanoma with genomics (P<0.005). In one case, none of 20 pathologists recognized a tumor with BRAF and TERT promoter mutations associated with fatal outcome as a conventional melanoma when only limited clinical information was provided, whereas 60% of pathologists correctly diagnosed this case when genomic information was also available. There was also a significant improvement in agreement of which lesions should be classified in the Spitz category/WHO Pathway from an average Cohen κ of 0.360 (SE=0.00921) to 0.607 (SE=0.0232) with genomics.

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.

Integrating 31-Gene Expression Profiling With Clinicopathologic Features to Optimize Cutaneous Melanoma Sentinel Lymph Node Metastasis Prediction

National guidelines recommend sentinel lymph node biopsy (SLNB) be offered to patients with > 10% likelihood of sentinel lymph node (SLN) positivity. On the other hand, guidelines do not recommend SLNB for patients with T1a tumors without high-risk features who have < 5% likelihood of a positive SLN. However, the decision to perform SLNB is less certain for patients with higher-risk T1 melanomas in which a positive node is expected 5%-10% of the time. We hypothesized that integrating clinicopathologic features with the 31-gene expression profile (31-GEP) score using advanced artificial intelligence techniques would provide more precise SLN risk prediction.

Impact of Circulating and Tissue Biomarkers in Adjuvant and Neoadjuvant Therapy for High-Risk Melanoma: Ready for Prime Time?

The prognosis of patients with metastatic melanoma has substantially improved over the last years with the advent of novel treatment strategies, mainly immune checkpoint inhibitors and BRAF and MEK inhibitors. Given the survival benefit provided in the metastatic setting and the evidence from prospective clinical trials in the early stages, these drugs have been introduced as adjuvant therapies for high-risk resected stage III disease. Several studies have also investigated immune checkpoint inhibitors, as well as BRAF and MEK inhibitors, for neoadjuvant treatment of high-risk stage III melanoma, with preliminary evidence suggesting this could be a very promising approach in this setting. However, even with new strategies, the risk of disease recurrence varies widely among stage III patients, and no available biomarkers for predicting disease recurrence have been established to date. Improved risk stratification is particularly relevant in this setting to avoid unnecessary treatment for patients who have minimum risk of disease recurrence and to reduce toxicities and costs. Research for predictive and prognostic biomarkers in this setting is ongoing to potentially shed light on the complex interplay between the tumor and the host immune system, and to further personalize treatment. This review provides an insight into available data on circulating and tissue biomarkers, including the tumor microenvironment and associated gene signatures, and their predictive and prognostic role during neoadjuvant and adjuvant treatment for cutaneous high-risk melanoma patients.

Recognition, Staging, and Management of Melanoma

Melanoma accounts for approximately 1% of all skin cancers but contributes to almost all skin cancer deaths. The developing picture suggests that melanoma phenotypes are driven by epigenetic mechanisms that reflect a complex interplay between genotype and environment. Furthermore, the growing consensus is that current classification standards, notwithstanding pertinent clinical history and appropriate biopsy, fall short of capturing the vast complexity of the disease. This article summarizes the current understanding of the clinical picture of melanoma, with a focus on the tremendous breakthroughs in molecular classification and therapeutics.

Sentinel lymph node biopsy in melanoma: beyond histologic factors

Sentinel lymph node (SLN) biopsy should be performed with the technical expertise required to correctly identify the sentinel node, in the context of understanding both the likelihood of positivity in a given patient and the prognostic significance of a positive or negative result. National Comprehensive Cancer Network guidelines recommend SLN biopsy for all cutaneous melanoma patients with primary tumor thickness greater than 1 mm and in select patients with thickness between 0.8 and 1 mm, yet admit a lack of consistent clarity in its utility for prognosis and therapeutic value in tumors < 1 mm and leave the decision for undergoing the procedure up to the patient and treating physician. Recent studies have evaluated specific patient populations, tumor histopathologic characteristics, and gene expression profiling and their use in predicting SLN positivity. These data have given insight into improving the physician's ability to potentially predict SLN positivity, shedding light on if and when omission of SLN biopsy in specific patients based on clinicopathological characteristics might be appropriate. This review provides discussion and insight into these recent advancements.

The genetic and epigenetic basis of distinct melanoma types

Melanoma represents the deadliest skin cancer. Recent therapeutic developments, including targeted and immune therapies have revolutionized clinical management and improved patient outcome. This progress was achieved by rigorous molecular and functional studies followed by robust clinical trials. The identification of key genomic alterations and gene expression profiles have propelled the understanding of distinct characteristics within melanoma subtypes. The aim of this review is to summarize and highlight the main genetic and epigenetic findings of melanomas and highlight their pathological and therapeutic importance.

Genetic markers for characterization and prediction of prognosis of melanoma subtypes: a 2021 update

In this article we examined the most important genetic markers involved in melanoma susceptibility, initiation and progression, and their impact on the prognosis of the disease. Current knowledge in melanoma genetics identifies distinct pathways to the development of different melanoma subtypes characterized by specific clinico-pathological features and partially known genetic markers, modulated by high, low or absence of cumulative sun damage. The most prevalent somatic mutations are related to the activation of the MAPK pathway, which are classified into four major subtypes: BRAF mutant, NRAS mutant, NF1 mutant and triple wild type. Moreover, germinal mutations are also involved in the characterization and predictions of prognosis in melanoma. Currently, CDKN2A is seen as the main high-risk gene involved in melanoma susceptibility being mutated in around 20% of melanoma-prone families. Other high-risk susceptibility genes described include CDK4, POT1, BAP1, TERT promoter, ACD, and TERF2IP. Melanoma is one of the most genetically predisposed among all cancers in humans, and ultraviolet light from the sun is the main environmental factor. This genetic predisposition is starting to be understood, impacting not only on the risk of developing melanoma but also on the risk of developing other types of cancer, as well as on the prognosis of the disease.

Prognostic and Predictive Biomarkers in Stage III Melanoma: Current Insights and Clinical Implications

Melanoma is one of the most aggressive skin cancers. The 5-year survival rate of stage III melanoma patients ranges from 93% (IIIA) to 32% (IIID) with a high risk of recurrence after complete surgery. The introduction of target and immune therapies has dramatically improved the overall survival, but the identification of patients with a high risk of relapse who will benefit from adjuvant therapy and the determination of the best treatment choice remain crucial. Currently, patient prognosis is based on clinico-pathological features, highlighting the urgent need of predictive and prognostic markers to improve patient management. In recent years, many groups have focused their attention on identifying molecular biomarkers with prognostic and predictive potential. In this review, we examined the main candidate biomarkers reported in the literature.

Picomets: Assessing single and few cell metastases in melanoma sentinel lymph node biopsies

BACKGROUND

Lymph node involvement is a significant prognostic factor for melanoma. Both number of positive nodes and disease burden within a lymph node affects survival. However, the significance of few tumor cells within a single node and subsequent optimal management remains without consensus. We investigated the implications of minimal nodal disease on clinical outcomes.

METHODS

We reviewed 752 patients who underwent lymph node sampling at time of primary melanoma resection at our institution over 15 years. We deemed patients who had 1 node with 1 to 4 atypical cells staining positive for either Melan-A or Sox-10 as having "picomets." We examined the initial clinicopathological features, subsequent management, and outcomes.

RESULTS

Thirty-three patients (4%) met criteria for having picomets. The most common number of positively staining atypical cells was 1 (n = 13). Nodal staging at initial pathology review varied, and overall stage ranged from IA to IIIC. Four patients underwent further therapy, none of whom had recurrent disease. Of the 29 patients undergoing observation/surveillance only, 5 had disease recurrence (17%).

CONCLUSION

Although patients with picomets had better outcomes than historical stage matched cohorts, a small subset had recurrent disease. Staging patients with picomets as "N0" may not reflect the true negative prognostic significance of picomets. A larger population of patients meeting picomets criteria is needed to draw further conclusions.

Risk factors and patterns of recurrence after sentinel lymph node biopsy for thin melanoma

While having a thin melanoma (defined as AJCC 8 T1 stage tumor ≤ 1.0 mm) with negative sentinel lymph node biopsy (SLNB) provides an excellent prognosis, some patients still develop recurrence and die. To determine risk factors for any recurrence (local/in-transit, nodal, distant) in thin melanoma patients with negative SLNB and assess survival outcomes. Retrospective review of thin melanomas with negative SLNB from 1999 to 2018 was performed. Two hundred and nine patients were identified. Clinicopathologic characteristics of the primary melanoma were collected. Patterns of recurrence for local/in-transit, nodal or distant recurrence and survival outcomes were analyzed. Eighteen patients (8.6%) developed recurrence: 3 (1.9%) local/in-transit, 4 (2.9%) regional/nodal, and 11 (5.3%) distant recurrence during a median follow-up time of 62 months. A multivariate Cox regression model showed that head and neck site (HR 3.52), ulceration (HR 10.8), and mitotic rate (HR 1.39) were significant risk factors for recurrence. Median time to first recurrence was 49 months. Patients with recurrence had a significantly worse 5 year overall survival than those without recurrence (82.2 vs 99.2%). A retrospective single center study and limited sample size. Did not factor in possible false negative SLNBs when calculating hazard ratios. For thin melanoma patients with negative SLNB, heightened surveillance is warranted for those with ulceration, primary tumor location on the head or neck, and elevated mitotic rate.

Molecular Biomarkers for Melanoma Screening, Diagnosis and Prognosis: Current State and Future Prospects

Despite significant progress in the development of treatment options, melanoma remains a leading cause of death due to skin cancer. Advances in our understanding of the genetic, transcriptomic, and morphologic spectrum of benign and malignant melanocytic neoplasia have enabled the field to propose biomarkers with potential diagnostic, prognostic, and predictive value. While these proposed biomarkers have the potential to improve clinical decision making at multiple critical intervention points, most remain unvalidated. Clinical validation of even the most commonly assessed biomarkers will require substantial resources, including limited clinical specimens. It is therefore important to consider the properties that constitute a relevant and clinically-useful biomarker-based test prior to engaging in large validation studies. In this review article we adapt an established framework for determining minimally-useful biomarker test characteristics, and apply this framework to a discussion of currently used and proposed biomarkers designed to aid melanoma detection, staging, prognosis, and choice of treatment.

Long-Term Outcomes in a Multicenter, Prospective Cohort Evaluating the Prognostic 31-Gene Expression Profile for Cutaneous Melanoma

PURPOSE

Current guidelines for postoperative management of patients with stage I-IIA cutaneous melanoma (CM) do not recommend routine cross-sectional imaging, yet many of these patients develop metastases. Methods that complement American Joint Committee on Cancer (AJCC) staging are needed to improve identification and treatment of these patients. A 31-gene expression profile (31-GEP) test predicts metastatic risk as low (class 1) or high (class 2). Prospective analysis of CM outcomes was performed to test the hypotheses that the 31-GEP provides prognostic value for patients with stage I-III CM, and that patients with stage I-IIA melanoma and class 2 31-GEP results have metastatic risk similar to patients for whom surveillance is recommended.

MATERIALS AND METHODS

Two multicenter registry studies, INTEGRATE (ClinicalTrials.gov identifier:NCT02355574) and EXPAND (ClinicalTrials.gov identifier:NCT02355587), were initiated under institutional review board approval, and 323 patients with stage I-III CM and median follow-up time of 3.2 years met inclusion criteria. Primary end points were 3-year recurrence-free survival (RFS), distant metastasis-free survival (DMFS), and overall survival (OS).

RESULTS

The 31-GEP was significant for RFS, DMFS, and OS in a univariate analysis and was a significant, independent predictor of RFS, DMFS, and OS in a multivariable analysis. GEP class 2 results were significantly associated with lower 3-year RFS, DMFS, and OS in all patients and those with stage I-IIA disease. Patients with stage I-IIA CM and a class 2 result had recurrence, distant metastasis, and death rates similar to patients with stage IIB-III CM. Combining 31-GEP results and AJCC staging enhanced sensitivity over each approach alone.

CONCLUSION

These data provide a rationale for using the 31-GEP along with AJCC staging, and suggest that patients with stage I-IIA CM and a class 2 31-GEP signature may be candidates for more intense follow-up.

Utility of a 31-gene expression profile for predicting outcomes in patients with primary cutaneous melanoma referred for sentinel node biopsy

BACKGROUND

A 31-gene genetic expression profile (31-GEP; Class 1 = low risk, Class 2 = high risk) developed to predict outcome in cutaneous melanoma (CM) has been validated by retrospective, industry-sponsored, or small series.

METHODS

Tumor features, sentinel node biopsy (SNB) results, and outcomes were extracted from a prospective database of 383 C M patients who underwent SNB and had a 31-GEP run on their primary tumor. Groups were compared by uni- and multi-variable analysis. Relapse-free and distant metastasis-free survival (RFS, DMFS) were estimated by Kaplan-Meier method.

RESULTS

Breslow thickness, T stage, and SNB positivity were significantly higher in Class 2 patients. Recurrence rates were higher for Class 2 vs Class 1 patients and highest in patients who were Class 2 and SNB positive. GEP class was predictive of RFS and DMFS and independently predicted relapse in AJCC "low risk" (stages IA-IIA) patients.

CONCLUSIONS

31-GEP adds prognostic information in CM patents undergoing SNB.

Adjuvant immunotherapy for melanoma

Surgical resection is the treatment for early cutaneous melanoma and is often curative. Some patients, however, will subsequently relapse. High-risk features in the primary tumor and regional lymph node metastasis highlight patient subsets that are at increased risk for recurrent disease. Immunotherapy in the form of checkpoint inhibitors ipilimumab, nivolumab, and pembrolizumab have been shown to improve recurrence-free survival for node-positive melanoma in the adjuvant setting and will be the focus of this review.

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.

Re-evaluation of Sentinel Lymph Node Biopsy for Melanoma

OPINION STATEMENT

The vast majority of patients newly diagnosed with melanoma present with clinically localized disease, and sentinel lymph node biopsy (SLNB) is a standard of care in the management of these patients, particularly in intermediate thickness cases, in order to provide important prognostic data. However, SLNB also has an important role in the management of patients with other subtypes of melanoma such as thick melanomas, certain thin melanomas, and specific histologic variants of melanoma such as desmoplastic melanoma. Furthermore, there have been technical advances in the SLNB technique, such as the development of newer radiotracers and use of SPECT/CT, and there is some data to suggest performing a SLNB may be therapeutic. Finally, the management of patients with a positive sentinel lymph node (SLN) has undergone dramatic changes over the past several years based on the results of recent important clinical trials. Treatment options for patients with SLN metastases now include surveillance, completion lymph node dissection, and adjuvant therapy with checkpoint inhibitors and targeted therapy. SLNB continues to play a crucial role in the management of patients with melanoma, allowing for risk stratification, potential regional disease control, and further treatment options for patients with a positive SLN.

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.

Computational Drug Repositioning Identifies Statins as Modifiers of Prognostic Genetic Expression Signatures and Metastatic Behavior in Melanoma

Despite advances in melanoma treatment, more than 70% of patients with distant metastasis die within 5 years. Proactive treatment of early melanoma to prevent metastasis could save lives and reduce overall healthcare costs. Currently, there are no treatments specifically designed to prevent early melanoma from progressing to metastasis. We used the Connectivity Map to conduct an in silico drug screen and identified 3-hydroxy-3-methylglutaryl-coenzyme A reductase inhibitors (statins) as a drug class that might prevent melanoma metastasis. To confirm the in vitro effect of statins, RNA sequencing was completed on A375 cells after treatment with fluvastatin to describe changes in the melanoma transcriptome. Statins induced differential expression in genes associated with metastasis and are used in commercially available prognostic tests for melanoma metastasis. Finally, we completed a chart review of 475 patients with melanoma. Patients taking statins were less likely to have metastasis at the time of melanoma diagnosis in both univariate and multivariate analyses (24.7% taking statins vs. 37.6% not taking statins, absolute risk reduction = 12.9%, P = 0.038). These findings suggest that statins might be useful as a treatment to prevent melanoma metastasis. Prospective trials are required to verify our findings and to determine the mechanism of metastasis prevention.

Development and validation of a Brazilian nomogram to assess sentinel node biopsy positivity in melanoma

BACKGROUND

Although well-established, sentinel node biopsy (SNB) for melanoma is not free from controversies and sometimes it can be questionable if SNB should be considered even for patients who meet the criteria for the procedure. Mathematical tools such as nomograms can be helpful and give more precise answers for both clinicians and patients. We present a nomogram for SNB positivity that has been internally validated.

METHODS

Retrospective analysis of patients who underwent SNB from 2000 to 2015 in a single institution. Single logistic regressions were used to identify variables that were associated to SNB positivity. All variables with a p value < 0.05 were included in the final model. Overall performance, calibration, and discriminatory power of the final multiple logistic regression model were all assessed. Internal validation of the multiple logistic regression model was performed via bootstrap analysis based on 1000 replications.

RESULTS

Site of primary lesion, Breslow thickness, mitotic rate, histologic regression, lymphatic invasion, and Clark level were statistically related to SNB positivity. After internal validation, a good performance was observed as well as an adequate power of discrimination (area under the curve 0.751).

CONCLUSIONS

We have presented a nomogram that can be helpful and easily used in daily practice for assessing SNB positivity.

Identification of stage I/IIA melanoma patients at high risk for disease relapse using a clinicopathologic and gene expression model

PURPOSE

Patients with stage I/IIA cutaneous melanoma (CM) are currently not eligible for adjuvant therapies despite uncertainty in relapse risk. Here, we studied the ability of a recently developed model which combines clinicopathologic and gene expression variables (CP-GEP) to identify stage I/IIA melanoma patients who have a high risk for disease relapse.

PATIENTS AND METHODS

Archival specimens from a cohort of 837 consecutive primary CMs were used for assessing the prognostic performance of CP-GEP. The CP-GEP model combines Breslow thickness and patient age, with the expression of eight genes in the primary tumour. Our specific patient group, represented by 580 stage I/IIA patients, was stratified based on their risk of relapse: CP-GEP High Risk and CP-GEP Low Risk. The main clinical end-point of this study was five-year relapse-free survival (RFS).

RESULTS

Within the stage I/IIA melanoma group, CP-GEP identified a high-risk patient group (47% of total stage I/IIA patients) which had a considerably worse five-year RFS than the low-risk patient group; 74% (95% confidence interval [CI]: 67%-80%) versus 89% (95% CI: 84%-93%); hazard ratio [HR] = 2.98 (95% CI: 1.78-4.98); P < 0.0001. Of patients in the high-risk group, those who relapsed were most likely to do so within the first 3 years.

CONCLUSION

The CP-GEP model can be used to identify stage I/IIA patients who have a high risk for disease relapse. These patients may benefit from adjuvant therapy.

Integrating the melanoma 31-gene expression profile test with surgical oncology practice within national guideline and staging recommendations

Aim: Define changes in clinical management resulting from the use of the prognostic 31-gene expression profile (31-GEP) test for cutaneous melanoma in a surgical oncology practice. Patients & methods: Management plans for 112 consecutively tested patients with stage I-III melanoma were evaluated for duration and number of clinical visits, blood work and imaging. Results: 31-GEP high-risk (class 2; n = 46) patients received increased management compared with low-risk (class 1; n = 66) patients. Test results were most closely associated with follow-up and imaging. Of class 1 patients, 65% received surveillance intensity within guidelines for stage I-IIA patients; 98% of class 2 patients received surveillance intensity equal to stage IIB-IV patients. Conclusion: We suggest clinical follow-up and metastatic screening be adjusted according to 31-GEP test results.

Performance of Gene Expression Profile Tests for Prognosis in Patients With Localized Cutaneous Melanoma: A Systematic Review and Meta-analysis

Importance

The performance of prognostic gene expression profile (GEP) tests for cutaneous melanoma is poorly characterized.

Objective

To systematically assess the performance of commercially available GEP tests in patients with American Joint Committee on Cancer (AJCC) stage I or stage II disease.

Data Sources

For this systematic review and meta-analysis, comprehensive searches of PubMed/MEDLINE, Embase, and Web of Science were conducted on December 12, 2019, for English-language studies of humans without date restrictions.

Study Selection

Two reviewers identified GEP external validation studies of patients with localized melanoma. After exclusion criteria were applied, 7 studies (8%; 5 assessing DecisionDx-Melanoma and 2 assessing MelaGenix) were included.

Data Extraction and Synthesis

Data were extracted using an adaptation of the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS-PF). When feasible, meta-analysis using random-effects models was performed. Risk of bias and level of evidence were assessed with the Quality in Prognosis Studies tool and an adaptation of Grading of Recommendations Assessment, Development, and Evaluation.

Main Outcomes and Measures

Proportion of patients with or without melanoma recurrence correctly classified by the GEP test as being at high or low risk.

Results

In the 7 included studies, a total of 1450 study participants contributed data (age and sex unknown). The performance of both GEP tests varied by AJCC stage. Of patients tested with DecisionDx-Melanoma, 623 had stage I disease (6 true-positive [TP], 15 false-negative, 61 false-positive, and 541 true-negative [TN] results) and 212 had stage II disease (59 TP, 13 FN, 78 FP, and 62 TN results). Among patients with recurrence, DecisionDx-Melanoma correctly classified 29% with stage I disease and 82% with stage II disease. Among patients without recurrence, the test correctly classified 90% with stage I disease and 44% with stage II disease. Of patients tested with MelaGenix, 88 had stage I disease (7 TP, 15 FN, 15 FP, and 51 TN results) and 245 had stage II disease (59 TP, 19 FN, 95 FP, and 72 TN results). Among patients with recurrence, MelaGenix correctly classified 32% with stage I disease and 76% with stage II disease. Among patients without recurrence, the test correctly classified 77% with stage I disease and 43% with stage II disease.

Conclusions and Relevance

The prognostic ability of GEP tests among patients with localized melanoma varied by AJCC stage and appeared to be poor at correctly identifying recurrence in patients with stage I disease, suggesting limited potential for clinical utility in these patients.