Model Combining Tumor Molecular and Clinicopathologic Risk Factors Predicts Sentinel Lymph Node Metastasis in Primary Cutaneous Melanoma

Risk factors for sentinel lymph node metastasis in Korean acral and non-acral melanoma patients

Breslow thickness, ulceration, and mitotic rate are well-known prognostic factors for sentinel lymph node (SLN) metastasis in cutaneous melanoma. We investigated risk factors, including especially the degree of pigmentation, for SLN metastasis in Korean melanoma patients. We enrolled 158, composed of Korean 107 acral and 51 non-acral melanoma patients who underwent SLN biopsy. Clinicopathologic features such as Breslow thickness, ulceration, mitotic rate, and the degree of pigmentation were evaluated. The recurrence-free survival (RFS) rate and date of recurrence were determined. Fifty-four patients (34.2%) had a positive SLN biopsy result. In a multivariate analysis, Breslow thickness (odds ratio [OR] 1.93; 95% confidence interval [CI], 1.12-3.47; p = .022) and heavy pigmentation (OR 13.14; 95% CI, 2.96-95.20, p = .002) were associated with SLN metastasis. Positive SLN patients had a higher rate of loco-regional and/or distant recurrence (hazard ratio 6.32; 95% CI, 3.39-11.79; p < .001). Heavy pigmentation was associated with poor RFS. Heavy pigmentation is an independent predictor of SLN metastasis in both acral and non-acral melanoma. Our results suggest the need for in-depth SLN evaluation of cutaneous melanoma patients with heavy pigmentation and provide clinicians with important information for determining patient prognosis.

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.

Validation of the Melanoma Institute of Australia's Sentinel Lymph Node Biopsy Risk Prediction Tool for Cutaneous Melanoma

BACKGROUND

For patients with cutaneous melanoma, sentinel lymph node biopsy (SLNB) is used to stage regional lymph nodes pathologically and inform prognosis, treatment, and surveillance. To reduce unnecessary surgeries, predictive tools aim to identify those at lowest risk for node-positive disease. The Melanoma Institute of Australia (MIA)'s Prediction Tool for Sentinel Node Metastasis Risk estimates risk of a positive SLNB using patient age and primary melanoma Breslow depth, histologic subtype, ulceration, mitotic rate, and lymphovascular invasion.

METHODS

A single-institution validation was performed of the MIA Calculator with 982 cutaneous melanoma patients that included all relevant clinicopathologic factors and SLNB pathology outcomes. The study evaluated discrimination via receiver operating characteristic (ROC) curves, calibration via calibration plots, and clinical utility via decision curve analysis of the MIA model in various subgroups. The data were fit to MIA model parameters via a generalized linear model to assess the odds ratio of parameters in our dataset.

RESULTS

The Calculator demonstrated limited discrimination based on ROC curves (C-statistic, 0.709) and consistently underestimated risk of SLN positivity. It did not provide a net benefit over SLNB performed on all patients or reduce unnecessary procedures in the risk domain of 0% to 16%. Compared with the original development and validation cohorts, the current study cohort had thinner tumors and a larger proportion of acral melanomas.

CONCLUSIONS

The Calculator generally underestimated SLN positivity risk, including assessment in patients who would be counseled to forego SLNB based on a predicted risk lower than 5%. Recognition of the tool's current limitations emphasizes the need to refine it further for use in medical decision-making.

The effect of surgical timing in nonmetastatic melanoma

BACKGROUND AND OBJECTIVES

There is no consensus guidelines on the best timing to perform Sentinel lymph node biopsy (SLNB) in high-risk melanoma patients. We aimed to understand the impact of surgical timing on nodal upstaging in patients with cutaneous melanoma.

METHODS

We queried the National Cancer Database from 2004 to 2018 for patients with T2-T4, N0, M0 melanomas, who underwent melanoma excision and nodal surgery. We included patients who underwent surgery within 2-19 weeks postdiagnosis. We aimed to determine the association of surgical delay (weeks) with nodal positivity.

RESULTS

A total of 53 355 patients were included, of whom 20.9% had positive lymph nodes. Patients underwent surgery at a median of 5 (4-7) weeks after diagnosis. The rate of positive nodes increased with increased weeks to surgery (line of best-fit slope = 0.38). Multivariable regression analysis identified an association between time to surgery and nodal positivity (2.4% increased risk per week, p < 0.05). Our analysis showed significantly increased likelihood of nodal positivity beginning 9 weeks after diagnosis (odds ratio [OR] = 1.3, p < 0.05). Furthermore, patients with T2-3 tumors had a significant increase in nodal positivity with increased time to surgery (OR = 1.03 per week, p < 0.001). However, no significant trend in nodal positivity was identified for patients with T4 melanomas (OR = 1.01 per week, p = 0.596).

CONCLUSION

Surgery within 9 weeks of melanoma diagnosis was not associated with increased likelihood of nodal positivity. These data can guide clinical conversations regarding the importance of surgical timing for melanoma.

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.

Clinical evaluation of the clinicopathologic and gene expression profile (CP-GEP) in patients with melanoma eligible for sentinel lymph node biopsy: A multicenter prospective Dutch study

Sentinel lymph node biopsy (SLNB) is recommended for patients with >pT1b cutaneous melanoma, and should be considered and discussed with patients diagnosed with pT1b cutaneous melanoma for the purpose of staging, prognostication and determining eligibility for adjuvant therapy. Previously, the clinicopathologic and gene expression profile (CP-GEP, Merlin Assay®) model was developed to identify patients who can forgo SLNB because of a low risk for sentinel node metastasis. The aim of this study was to evaluate the clinical use and implementation of the CP-GEP model in a prospective multicenter study in the Netherlands. Both test performance and feasibility for clinical implementation were assessed in 260 patients with T1-T4 melanoma. The CP-GEP model demonstrated an overall negative predictive value of 96.7% and positive predictive value of 23.7%, with a potential SLNB reduction rate of 42.2% in patients with T1-T3 melanoma. With a median time of 16 days from initiation to return of test results, there was sufficient time left before the SLNB was performed. Based on these outcomes, the model may support clinical decision-making to identify patients who can forgo SLNB in clinical practice.

Histopathologic abundance of pigmentation correlates with disease-specific survival in malignant melanoma but is not independent of current AJCC pT stage

The increasing number of melanoma patients makes it necessary to develop best possible strategies for prognosis assessment in order to recommend appropriate therapy and follow-up. The prognostic significance of tumor cell pigmentation has not been fully elucidated. Hematoxylin and eosin (H&E)-stained sections of 775 melanomas diagnosed between 2012 and 2015 were independently assessed for melanin pigment abundance by two investigators, and the impact on melanoma-specific survival was calculated. Unpigmented melanomas (n = 99) had a melanoma-specific survival of 67.7%, melanomas with moderate pigmentation (n = 384) had a melanoma-specific survival of 85.9%, and strongly pigmented melanomas (n = 292) had a melanoma-specific survival of 91.4% (p < .001). In an analysis of melanoma-specific survival adjusted for pT stage and pigmentation, we found a nonsignificant impact of pigmentation abundance with a hazard ratio of 1.277 (p = .74). The study presented here provides evidence in a German cohort that patients with pigmented melanomas have a more favorable prognosis than those diagnosed with nonpigmented melanomas. Moreover, the abundance of pigmentation already seems to provide a first prognostic estimate. However, it does not appear to provide significant additional value for prognostic assessment according to the AJCC 2017 pT classification.

A Clinical Decision Tool to Calculate Pretest Probability of Sentinel Lymph Node Metastasis in Primary Cutaneous Melanoma

BACKGROUND

Although sentinel lymph node biopsy (SLNB) status is a strong prognostic indicator for cutaneous melanoma, unnecessary SLNBs have substantial cost and morbidity burden.

OBJECTIVE

This study was designed to develop, validate, and present a personalized, clinical, decision-making tool using nationally representative data with clinically actionable probability thresholds (Expected Lymphatic Metastasis Outcome [ELMO]).

METHODS

Data from the Surveillance, Epidemiology, and End Results (SEER) Registry from 2000 to 2017 and the National Cancer Database (NCDB) from 2004 to 2015 were used to develop and internally validate a logistic ridge regression predictive model for SLNB positivity. External validation was done with 1568 patients at a large tertiary referral center.

RESULTS

The development cohort included 134,809 patients, and the internal validation cohort included 38,518 patients. ELMO (AUC 0.85) resulted in a 29.54% SLNB reduction rate and greater sensitivity in predicting SLNB status for T1b, T2a, and T2b tumors than previous models. In external validation, ELMO had an accuracy of 0.7586 and AUC of 0.7218. Limitations of this study are potential miscoding, unaccounted confounders, and effect modification.

CONCLUSIONS

ELMO ( https://melanoma-sentinel.herokuapp.com/ ) has been developed and validated (internally and externally) by using the largest publicly available dataset of melanoma patients and was found to have high accuracy compared with other published models and gene expression tests. Individualized risk estimates for SLNB positivity are critical in facilitating thorough decision-making for healthcare providers and patients with melanoma.

Alternatives and reduced need for sentinel lymph node biopsy (SLNB) staging for melanoma

Sentinel lymph node biopsy (SLNB) has been introduced in the 1990s to identify patients who might benefit from completion lymph node dissection. Neither SLNB nor CLND improved survival, but SLNB staging did provide the best staging, above Breslow thickness and ulceration. The SLN status and SLN tumour burden were used in all trials until date looking at modern adjuvant systemic therapy with immune checkpoint inhibition (ICI) or targeted therapies (TT). Adjuvant ICI and TT are shifting towards stage II melanoma. The question is whether there is still role for SLNB in melanoma, in this day and age, and if so, how does the future look for SLNB staging? The SLN status and SLN tumour burden might be useful for a consultation to discuss the number needed to treat in a risk/benefit discussion. For stage IIB/C patients, it seems likely, however, that patients will forego a nuclear scan followed by the risk of surgical intervention and morbidity associated with SLNB if they opt to receive adjuvant therapy regardless of the SLNB result. For stage I/IIA, it is still required to detect high-risk patients who might benefit from adjuvant therapy. However, biomarkers are emerging, such as gene expression profilers (GEP), immunohistological signatures and liquid biopsies with ctDNA. There still is a role for SLNB staging in melanoma today, but we expect that the availability of therapeutic option independent of SLNB status as well as emergence of validated biomarkers to predict risk will reduce the need for SLNB staging in the upcoming decade to the point it will no longer be used.

Identification of stage I/II melanoma patients at high risk for recurrence using a model combining clinicopathologic factors with gene expression profiling (CP-GEP)

PURPOSE

Patients with cutaneous melanoma stage I/IIA disease are currently not eligible for adjuvant therapy, despite their risk for relapses and death. This study validates the ability of a model combining clinicopathologic factors with gene expression profiling (CP-GEP) to identify patients at high risk for disease recurrence in stage I/II and subgroup stage I/IIA.

PATIENTS AND METHODS

543 patients with stage I/II primary cutaneous melanoma from the University of Tuebingen diagnosed between 2000 and 2017 were analysed. All patients received sentinel lymph node biopsy (SLNB). Analysis was conducted for a separate group of 80 patients who did not undergo SLNB.

RESULTS

CP-GEP stratified 424 stage I/IIA patients (78% of the cohort) according to their risk for recurrence, with five-year relapse-free survival (RFS) rates of 77.8% and 93% for CP-GEP high risk (195 patients) and low risk (229 patients), respectively, and hazard ratio of 3.53 (p-value <0.001). In patients who did not receive SLNB biopsy, CP-GEP captured 6 out of 7 relapses.

CONCLUSION

CP-GEP can be used to identify primary cutaneous melanoma patients with a high risk for disease recurrence - especially for stage I/IIA, who are considered low risk by AJCC 8th. These patients may benefit from adjuvant therapy. Also, in the future, when SLNB may become irrelevant, CP-GEP may serve as a risk stratification tool.

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.

Optimizing treatment approaches for patients with cutaneous melanoma by integrating clinical and pathologic features with the 31-gene expression profile test

BACKGROUND

Many patients with low-stage cutaneous melanoma will experience tumor recurrence, metastasis, or death, and many higher staged patients will not.

OBJECTIVE

To develop an algorithm by integrating the 31-gene expression profile test with clinicopathologic data for an optimized, personalized risk of recurrence (integrated 31 risk of recurrence [i31-ROR]) or death and use i31-ROR in conjunction with a previously validated algorithm for precise sentinel lymph node positivity risk estimates (i31-SLNB) for optimized treatment plan decisions.

METHODS

Cox regression models for ROR were developed (n = 1581) and independently validated (n = 523) on a cohort with stage I-III melanoma. Using National Comprehensive Cancer Network cut points, i31-ROR performance was evaluated using the midpoint survival rates between patients with stage IIA and stage IIB disease as a risk threshold.

RESULTS

Patients with a low-risk i31-ROR result had significantly higher 5-year recurrence-free survival (91% vs 45%, P < .001), distant metastasis-free survival (95% vs 53%, P < .001), and melanoma-specific survival (98% vs 73%, P < .001) than patients with a high-risk i31-ROR result. A combined i31-SLNB/ROR analysis identified 44% of patients who could forego sentinel lymph node biopsy while maintaining high survival rates (>98%) or were restratified as being at a higher or lower risk of recurrence or death.

LIMITATIONS

Multicenter, retrospective study.

CONCLUSION

Integrating clinicopathologic features with the 31-GEP optimizes patient risk stratification compared to clinicopathologic features alone.

Cost evaluation of the Merlin assay for predicting melanoma sentinel lymph node biopsy metastasis

BACKGROUND

The Merlin assay for melanoma-risk assessment has become commercially available to reduce the rate of unnecessary sentinel lymph node biopsies (SLNB) in SLNB-eligible patients with cutaneous melanoma. Merlin low-risk patients are recommended to undergo wide local excision (WLE) of the primary tumor, whereas Merlin high-risk patients are recommended to undergo both SLNB and WLE. Here, we compared the cost of a Merlin testing strategy to that of a no-testing strategy (usual care) before prescribing SLNB.

METHODS

We identified T1 and T2 patients who underwent WLE and SLNB but not completion lymph node dissection between 2007 and 2018. Controls were T1 patients who only underwent WLE. Costs for WLE and SLNB were calculated by converting institutional cost data to standardized Medicare reimbursement rates. We then developed a decision tree to compare the cost of Merlin testing to that of a no-testing strategy (usual care).

RESULTS

The average standardized cost of WLE was $2066, whereas the cost of WLE and SLNB was $11,976 based on Medicare rates. At a cost below $7350 for T1b melanoma and $4600 for T1b to T2 melanoma, Merlin testing was cost-saving compared to a no-testing strategy (usual care), assuming Medicare reimbursement rates.

CONCLUSION

Merlin testing for T1b and T2 melanoma is potentially cost saving depending on the cost of the molecular assay and SLNB reimbursement rates. In addition to being cost saving, Merlin is expected to improve health-related quality of life.

Sentinel Lymph Node Biopsy in Cutaneous Melanoma, a Clinical Point of View

Sentinel lymph node biopsy (SLNB) is a surgical procedure that has been used in patients with cutaneous melanoma for nearly 30 years. It is used for both staging and regional disease control with minimum morbidity, as proven by numerous worldwide prospective studies. It has been incorporated in the recommendations of national and professional guidelines. In this article, we provide a summary of the general information on SLNB in the clinical guidelines for the management of cutaneous malignant melanoma (American Association of Dermatology, European Society of Medical Oncology, National Comprehensive Cancer Network, and Cancer Council Australia) and review the most relevant literature to provide an update on the existing recommendations for SLNB.

Integration of lncRNAs, Protein-Coding Genes and Pathology Images for Detecting Metastatic Melanoma

Melanoma is a lethal skin disease that develops from moles. This study aimed to integrate multimodal data to predict metastatic melanoma, which is highly aggressive and difficult to treat. The proposed EnsembleSKCM method evaluated the prediction performances of long noncoding RNAs (lncRNAs), protein-coding messenger genes (mRNAs) and pathology images (images) for metastatic melanoma. Feature selection was used to screen for metastatic biomarkers in the lncRNA and mRNA datasets. The integrated EnsembleSKCM model was built based on the weighted results of the lncRNA-, mRNA- and image-based models. EnsembleSKCM achieved 0.9444 in the prediction accuracy of metastatic melanoma and outperformed the single-modal prediction models based on the lncRNA, mRNA and image data. The experimental data suggest the importance of integrating the complementary information from the three data modalities. WGCNA was used to analyze the relationship of molecular-level features and image features, and the results show connections between them. Another cohort was used to validate our prediction.

Clinically Significant Risk Thresholds in the Management of Primary Cutaneous Melanoma: A Survey of Melanoma Experts

BACKGROUND

Risk-based thresholds to guide management are undefined in the treatment of primary cutaneous melanoma but are essential to advance the field from traditional stage-based treatment to more individualized care.

METHODS

To estimate treatment risk thresholds, hypothetical clinical melanoma scenarios were developed and a stratified random sample was distributed to expert melanoma clinicians via an anonymous web-based survey. Scenarios provided a defined 5-year risk of recurrence and asked for recommendations regarding clinical follow-up, imaging, and adjuvant therapy. Marginal probability of response across the spectrum of 5-year recurrence risk was estimated. The risk at which 50% of respondents recommended a treatment was defined as the risk threshold.

RESULTS

The overall response rate was 56% (89/159). Three separate multivariable models were constructed to estimate the recommendations for clinical follow-up more than twice/year, for surveillance cross-sectional imaging at least once/year, and for adjuvant therapy. A 36% 5-year risk of recurrence was identified as the threshold for recommending clinical follow-up more than twice/year. The thresholds for recommending cross-sectional imaging and adjuvant therapy were 30 and 59%, respectively. Thresholds varied with the age of the hypothetical patient: at younger ages they were constant but increased rapidly at ages 60 years and above.

CONCLUSIONS

To our knowledge, these data provide the first estimates of clinically significant treatment thresholds for patients with cutaneous melanoma based on risk of recurrence. Future refinement and adoption of thresholds would permit assessment of the clinical utility of novel prognostic tools and represents an early step toward individualizing treatment recommendations.

Association of tumor molecular factors with in-transit metastasis in primary cutaneous melanoma

BACKGROUND

In-transit metastases (ITM) are a form of locoregional relapse representing intralymphatic metastatic spread and occur in approximately 4-9% of patients with melanoma >1 mm Breslow thickness. Our objective was to evaluate a combination of clinicopathologic risk factors and gene expression biomarkers predictive of ITM risk.

METHODS

We used PCR to quantify gene expression in diagnostic biopsy tissue across a prospectively designed archival cohort of 854 consecutive thin and intermediate thickness primary cutaneous melanomas. The outcome of interest was ITM >90 days after a melanoma diagnosis. Cox proportional hazard models were fit to estimate each clinicopathologic and molecular characteristic's association with the risk of ITM.

RESULTS

The 5-year cumulative incidence of ITM was 3.2%. Clinical factors univariately associated with an increased risk of ITM were older age, greater Breslow thickness, greater mitotic rate, lower extremity location, ulceration, and a positive SLN biopsy. Of 108 genes tested, five were significantly upregulated and five significantly downregulated when evaluated in Cox models adjusted for age, Breslow thickness, mitotic rate, and lower extremity location. Among the upregulated genes, the strongest association was observed for interleukin-8 (IL8).

CONCLUSION

A subset of gene expression biomarkers was identified as independently associated with the risk of ITM after adjusting for key covariates. Once sufficiently validated, our results may lead the way to regional therapy trials for a small, selected group of high-risk patients.

Predictors of Nodal Metastasis in Cutaneous Head and Neck Cancers

PURPOSE OF REVIEW

The complex and varied drainage patterns in the head and neck present a challenge in the regional control of cutaneous neoplasms. Lymph node involvement significantly diminishes survival, often warranting more aggressive treatment. Here, we review the risk factors associated with lymphatic metastasis, in the context of the evolving role of sentinel lymph node biopsy.

RECENT FINDINGS

In cutaneous head and neck melanomas, tumor thickness, age, size, mitosis, ulceration, and specific histology have been associated with lymph node metastasis (LNM). In head and neck cutaneous squamous cell carcinomas, tumor thickness, size, perineural invasion, and immunosuppression are all risk factors for nodal metastasis. The risk factors for lymph node involvement in Merkel cell carcinoma are not yet fully defined, but emerging evidence indicates that tumor thickness and size may be  associated with regional metastasis. The specific factors that predict a greater risk of LNM for cutaneous head and neck cancers generally include depth of invasion, tumor size, mitotic rate, ulceration, immunosuppression, and other histopathological factors.

Using the Merlin assay for reducing sentinel lymph node biopsy complications in melanoma: a retrospective cohort study

BACKGROUND

The assessment of the sentinel lymph node is a cornerstone of melanoma staging. However, ~80% of sentinel lymph node biopsies (SLNB) are negative and nontherapeutic, and patients are unnecessarily exposed to surgery-related complications. Here, we gauged the potential of the Merlin assay to reduce SLNB-associated complications. The Merlin assay uses clinicopathologic variables and tumor gene expression profiling to identify low-risk patients who may forgo SLNB.

METHODS

We utilized the Merlin test development cohort to determine SLNB complication rates for procedures performed between 2004 and 2018 at Mayo Clinic. Complications evaluated were lymphedema, seroma, infection/cellulitis, hematoma, and wound dehiscence. Patients who underwent a completion lymph node dissection were excluded.

RESULTS

A total of 558 patients were included. The overall 90-day complication rate specific to SLNB (1 year for lymphedema) was 17.4%. The most common complications were seroma (9.3%), infection/cellulitis (4.8%), and lymphedema (4.3%). All three were more common in patients with a lower extremity primary tumor location versus other locations. With Merlin test results applied, SLNB-related complications would have decreased by 59%.

CONCLUSION

SLNB is a safe procedure but carries a significant complication rate. Merlin testing might reduce the need for SLNB and its associated complications.

Using a Clinicopathologic and Gene Expression (CP-GEP) Model to Identify Stage I-II Melanoma Patients at Risk of Disease Relapse

Background: The current standard of care for patients without sentinel node (SN) metastasis (i.e., stage I−II melanoma) is watchful waiting, while >40% of patients with stage IB−IIC will eventually present with disease recurrence or die as a result of melanoma. With the prospect of adjuvant therapeutic options for patients with a negative SN, we assessed the performance of a clinicopathologic and gene expression (CP-GEP) model, a model originally developed to predict SN metastasis, to identify patients with stage I−II melanoma at risk of disease relapse. Methods: This study included patients with cutaneous melanoma ≥18 years of age with a negative SN between October 2006 and December 2017 at the Sahlgrenska University Hospital (Sweden) and Erasmus MC Cancer Institute (The Netherlands). According to the CP-GEP model, which can be applied to the primary melanoma tissue, the patients were stratified into high or low risk of recurrence. The primary aim was to assess the 5-year recurrence-free survival (RFS) of low- and high-risk CP-GEP. A secondary aim was to compare the CP-GEP model with the EORTC nomogram, a model based on clinicopathological variables only. Results: In total, 535 patients (stage I−II) were included. CP-GEP stratification among these patients resulted in a 5-year RFS of 92.9% (95% confidence interval (CI): 86.4−96.4) in CP-GEP low-risk patients (n = 122) versus 80.7% (95%CI: 76.3−84.3) in CP-GEP high-risk patients (n = 413; hazard ratio 2.93 (95%CI: 1.41−6.09), p < 0.004). According to the EORTC nomogram, 25% of the patients were classified as having a ‘low risk’ of recurrence (96.8% 5-year RFS (95%CI 91.6−98.8), n = 130), 49% as ‘intermediate risk’ (88.4% 5-year RFS (95%CI 83.6−91.8), n = 261), and 26% as ‘high risk’ (61.1% 5-year RFS (95%CI 51.9−69.1), n = 137). Conclusion: In these two independent European cohorts, the CP-GEP model was able to stratify patients with stage I−II melanoma into two groups differentiated by RFS.

Development of an Image Analysis-Based Prognosis Score Using Google's Teachable Machine in Melanoma

BACKGROUND

The increasing number of melanoma patients makes it necessary to establish new strategies for prognosis assessment to ensure follow-up care. Deep-learning-based image analysis of primary melanoma could be a future component of risk stratification.

OBJECTIVES

To develop a risk score for overall survival based on image analysis through artificial intelligence (AI) and validate it in a test cohort.

METHODS

Hematoxylin and eosin (H&E) stained sections of 831 melanomas, diagnosed from 2012-2015 were photographed and used to perform deep-learning-based group classification. For this purpose, the freely available software of Google's teachable machine was used. Five hundred patient sections were used as the training cohort, and 331 sections served as the test cohort.

RESULTS

Using Google's Teachable Machine, a prognosis score for overall survival could be developed that achieved a statistically significant prognosis estimate with an AUC of 0.694 in a ROC analysis based solely on image sections of approximately 250 × 250 µm. The prognosis group "low-risk" (n = 230) showed an overall survival rate of 93%, whereas the prognosis group "high-risk" (n = 101) showed an overall survival rate of 77.2%.

CONCLUSIONS

The study supports the possibility of using deep learning-based classification systems for risk stratification in melanoma. The AI assessment used in this study provides a significant risk estimate in melanoma, but it does not considerably improve the existing risk classification based on the TNM classification.

Tetraspanin CD9 Expression Predicts Sentinel Node Status in Patients with Cutaneous Melanoma

The tetraspanin CD9 is considered a metastasis suppressor in many cancers, however its role is highly debated. Currently, little is known about CD9 prognostic value in cutaneous melanoma. Our aim was to analyse CD9 expression in melanocytic nevi and primary cutaneous melanomas through immunohistochemistry and immunofluorescence approaches to determine its correlation with invasiveness and metastatic potential. CD9 displayed homogeneous staining in all melanocytic nevi. In contrast, it showed a complete loss of reactivity in all thin melanomas. Interestingly, CD9 was re-expressed in 46% of intermediate and thick melanomas in small tumor clusters predominantly located at sites of invasion near or inside the blood or lymphatic vessels. The most notable finding is that all CD9 stained melanomas presented sentinel node positivity. Additionally, a direct association between CD9 expression and presence of distant metastasis was reported. Finally, we confirm that CD9 expression is consistent with an early protective role against tumorigenesis, however, our data endorse in melanoma a specific function of CD9 in vascular dissemination during late tumor progression. The presence of CD9 hotspots could be essential for melanoma cell invasion in lymphatic and endothelial vessels. CD9 could be a valid prognostic factor for lymph node metastasis risk.

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.

Follow-up of primary melanoma patients with high risk of recurrence: recommendations based on evidence and consensus

In spite of the good prognosis of patients with early-stage melanoma, there is a substantial proportion of them that develop local or distant relapses. With the introduction of targeted and immune therapies for advanced melanoma, including at the adjuvant setting, early detection of recurrent melanoma and/or second primary lesions is crucial to improve clinical outcomes. However, there is a lack of universal guidelines regarding both frequency of surveillance visits and diagnostic imaging and/or laboratory evaluations. In this article, a multidisciplinary expert panel recommends, after careful review of relevant data in the field, a consensus- and experience-based follow-up strategy for melanoma patients, taking into account prognostic factors and biomarkers and the high-risk periods and patterns of recurrence in each (sub) stage of the disease. Apart from the surveillance intensity, healthcare professionals should focus on patients' education to perform regular self-examinations of the skin and palpation of lymph nodes.

Comparison of Two Rapid Assays for the Detection of BRAF V600 Mutations in Metastatic Melanoma including Positive Sentinel Lymph Nodes

Testing for the BRAF mutation is mandatory for the management of patients with locally advanced or metastatic melanoma. Molecular analysis based on DNA sequencing remains the gold-standard method for the screening of the different BRAF mutations. These methods must be rapid, sensitive, and specific enough to allow optimal therapeutic management in daily practice and also to include patients in clinical trials. Here, we compared the Idylla BRAF Mutation Test and the anti-BRAF V600E (clone VE1) immunohistochemistry (IHC) in 90 melanoma samples, with a focus on a challenging cohort of 32 positive sentinel lymph nodes. The BRAF status was assessed with both methods independently of the percentage of tumor cells. The concordance rate was calculated excluding both non-contributory analyses and BRAFV600K/R/M mutants due to the specific V600E-IHC test design. The incidence of the BRAFV600E mutation was 33% with both BRAF Idylla and BRAF IHC. The agreement rate was 91% (72/79). Although the agreement rate was high, we suggest that the use of IHC is more suitable for rapid BRAF testing on sentinel lymph node biopsies when associated with a low percentage and scattered tumor cells, which gave a high risk of non-contributory analysis and/or false negative results with the Idylla^TMBRAF Mutation Test.

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.

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.

Validation of a clinicopathological and gene expression profile model to identify patients with cutaneous melanoma where sentinel lymph node biopsy is unnecessary

BACKGROUND

In patients with cutaneous melanoma, sentinel lymph node biopsy (SLNB) serves as an important technique to asses disease stage and to guide adjuvant systemic therapy. A model using clinicopathologic and gene expression variables (CP-GEP; Merlin Assay) has recently been introduced to identify patients that may safely forgo SLNB. Herein we present data from an independent validation cohort of the CP-GEP model in Swedish patients.

METHODS

Archival histological material (primary melanoma tissue) from a prospectively collected cohort of 421 consecutive patients with pT1-T4 melanoma undergoing SLNB between 2006 and 2014 was analyzed using the CP-GEP model. CP-GEP combines Breslow thickness and patient age with the expression levels of eight genes from the primary melanoma. Stratification is based on their risk for nodal metastasis: CP-GEP Low Risk or CP-GEP High Risk.

RESULTS

The SLNB positivity rate was 13%. Of 421 primary melanomas, the CP-GEP model identified 86 patients as having a low risk for nodal metastasis. In patients with pT1-2 melanomas, the SLNB reduction rate was 35.4% (95% CI: 29.4-41.8) with a negative predictive value (NPV) of 96.5% (95% CI: 90.0-99.3). Among patients with pT1-3 melanomas, CP-GEP suggested a SLNB reduction rate of 24.0% (95% CI: 19.7-28.8) and a NPV of 96.5% (95% CI: 90.1-99.3). Only one of 118 pT3 tumors was classified as CP-GEP Low Risk, and all pT4 tumors were classified as being high risk for nodal metastasis.

CONCLUSION

This study demonstrates that CP-GEP can identify patients with a low risk for nodal metastasis. Patients with pT1-2 melanomas have the highest clinical benefit from using the test, where 35% of the patients could forgo a SLNB procedure.

Clinicopathologic models predicting non-sentinel lymph node metastasis in cutaneous melanoma patients: Are they useful for patients with a single positive sentinel node?

Background and Objectives

Of clinically node‐negative (cN0) cutaneous melanoma patients with sentinel lymph node (SLN) metastasis, between 10% and 30% harbor additional metastases in non‐sentinel lymph nodes (NSLNs). Approximately 80% of SLN‐positive patients have a single positive SLN.

Methods

To assess whether state‐of‐the‐art clinicopathologic models predicting NSLN metastasis had adequate performance, we studied a single‐institution cohort of 143 patients with cN0 SLN‐positive primary melanoma who underwent subsequent completion lymph node dissection. We used sensitivity (SE) and positive predictive value (PPV) to characterize the ability of the models to identify patients at high risk for NSLN disease.

Results

Across Stage III patients, all clinicopathologic models tested had comparable performances. The best performing model identified 52% of NSLN‐positive patients (SE = 52%, PPV = 37%). However, for the single SLN‐positive subgroup (78% of cohort), none of the models identified high‐risk patients (SE > 20%, PPV > 20%) irrespective of the chosen probability threshold used to define the binary risk labels. Thus, we designed a new model to identify high‐risk patients with a single positive SLN, which achieved a sensitivity of 49% (PPV = 26%).

Conclusion

For the largest SLN‐positive subgroup, those with a single positive SLN, current model performance is inadequate. New approaches are needed to better estimate nodal disease burden of these patients.

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.

A disease network-based deep learning approach for characterizing melanoma

Multiple types of genomic variations are present in cutaneous melanoma and some of the genomic features may have an impact on the prognosis of the disease. The access to genomics data via public repositories such as The Cancer Genome Atlas (TCGA) allows for a better understanding of melanoma at the molecular level, therefore making characterization of substantial heterogeneity in melanoma patients possible. Here, we proposed an approach that integrates genomics data, a disease network, and a deep learning model to classify melanoma patients for prognosis, assess the impact of genomic features on the classification and provide interpretation to the impactful features. We integrated genomics data into a melanoma network and applied an autoencoder model to identify subgroups in TCGA melanoma patients. The model utilizes communities identified in the network to effectively reduce the dimensionality of genomics data into a patient score profile. Based on the score profile, we identified three patient subtypes that show different survival times. Furthermore, we quantified and ranked the impact of genomic features on the patient score profile using a machine-learning technique. Follow-up analysis of the top-ranking features provided us with the biological interpretation of them at both pathway and molecular levels, such as their mutation and interactome profiles in melanoma and their involvement in pathways associated with signaling transduction, immune system and cell cycle. Taken together, we demonstrated the ability of the approach to identify disease subgroups using a deep learning model that captures the most relevant information of genomics data in the melanoma network.

More sentinel lymph node biopsies for thin melanomas after transition to AJCC 8th edition do not increase positivity rate: A Danish population-based study of 7148 patients

BACKGROUND

We evaluated the outcome of sentinel lymph node biopsies (SLNB) in patients with thin melanoma before and after the implementation of AJCC 8th edition (AJCC8) and identified predictors of positive sentinel lymph nodes (+SLN).

METHODS

Patients diagnosed with T1 melanomas (Breslow thickness ≤1 mm) during 2016-2017 as per AJCC 7th edition (AJCC7) (n = 3414) and 2018-2019 as per AJCC8 (n = 3734) were identified in the Danish Melanoma Database.

RESULTS

More SLNBs were performed in the AJCC8 cohort compared to the AJCC7 (22.2% vs. 16.2%, p < 0.001), with no significant difference in +SLN rates (4.7% vs. 6.7%, p = 0.118). In the AJCC7 + SLN subgroup, no melanomas were ulcerated, 94.6% had mitotic rate (MR) ≥ 1, 67.6% were ≥0.8 mm and 32.4% would be T1a according to AJCC8. In the AJCC8 + SLN subgroup, 10.3% were ulcerated, 74.4% had MR≥ 1, 97.4% were ≥0.8 mm and 23.1% would be T1a according to AJCC7. On multivariable analysis younger age and MR ≥ 1 were significant predictors of +SLN.

CONCLUSION

More SLNBs were performed in T1 melanomas after transition to AJCC8 without an increase in +SLN rate. None of the AJCC8 T1b criteria were significant predictors of +SLN. We suggest that mitosis and younger age should be considered as indications for SLNB in thin melanoma.

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.

Preoperative clinical and tumor genomic features associated with pathologic lymph node metastasis in clinical stage I and II lung adenocarcinoma

While next-generation sequencing (NGS) is used to guide therapy in patients with metastatic lung adenocarcinoma (LUAD), use of NGS to determine pathologic LN metastasis prior to surgery has not been assessed. To bridge this knowledge gap, we performed NGS using MSK-IMPACT in 426 treatment-naive patients with clinical N2-negative LUAD. A multivariable logistic regression model that considered preoperative clinical and genomic variables was constructed. Most patients had cN0 disease (85%) with pN0, pN1, and pN2 rates of 80%, 11%, and 9%, respectively. Genes altered at higher rates in pN-positive than in pN-negative tumors were STK11 (p = 0.024), SMARCA4 (p = 0.006), and SMAD4 (p = 0.011). Fraction of genome altered (p = 0.037), copy number amplifications (p = 0.001), and whole-genome doubling (p = 0.028) were higher in pN-positive tumors. Multivariable analysis revealed solid tumor morphology, tumor SUVmax, clinical stage, SMARCA4 and SMAD4 alterations were independently associated with pathologic LN metastasis. Incorporation of clinical and tumor genomic features can identify patients at risk of pathologic LN metastasis; this may guide therapy decisions before surgical resection.

Deep learning approach to predict sentinel lymph node status directly from routine histology of primary melanoma tumours

AIM

Sentinel lymph node status is a central prognostic factor for melanomas. However, the surgical excision involves some risks for affected patients. In this study, we therefore aimed to develop a digital biomarker that can predict lymph node metastasis non-invasively from digitised H&E slides of primary melanoma tumours.

METHODS

A total of 415 H&E slides from primary melanoma tumours with known sentinel node (SN) status from three German university hospitals and one private pathological practice were digitised (150 SN positive/265 SN negative). Two hundred ninety-one slides were used to train artificial neural networks (ANNs). The remaining 124 slides were used to test the ability of the ANNs to predict sentinel status. ANNs were trained and/or tested on data sets that were matched or not matched between SN-positive and SN-negative cases for patient age, ulceration, and tumour thickness, factors that are known to correlate with lymph node status.

RESULTS

The best accuracy was achieved by an ANN that was trained and tested on unmatched cases (61.8% ± 0.2%) area under the receiver operating characteristic (AUROC). In contrast, ANNs that were trained and/or tested on matched cases achieved (55.0% ± 3.5%) AUROC or less.

CONCLUSION

Our results indicate that the image classifier can predict lymph node status to some, albeit so far not clinically relevant, extent. It may do so by mostly detecting equivalents of factors on histological slides that are already known to correlate with lymph node status. Our results provide a basis for future research with larger data cohorts.

Validation of CP-GEP (Merlin Assay) for predicting sentinel lymph node metastasis in primary cutaneous melanoma patients: A U.S. cohort study

BACKGROUND

Approximately 85% of melanoma patients who undergo a sentinel lymph node biopsy (SLNB) are node-negative. Melanoma incidence is highest in patients ≥65 years, but their SLNB positivity rate is lower than in younger patients. CP-GEP, a model combining clinicopathologic and gene expression variables, identifies primary cutaneous melanoma (CM) patients who may safely forgo SLNB due to their low risk for nodal metastasis. Here, we validate CP-GEP in a U.S. melanoma patient cohort.

METHODS

A cohort of 208 adult patients with primary CM from the Mayo Clinic and West Virginia University was used. Patients were stratified according to their risk for nodal metastasis: CP-GEP High Risk and CP-GEP Low Risk. The main performance measures were SLNB reduction rate (RR) and negative predictive value (NPV).

RESULTS

SLNB positivity rate for the entire cohort was 21%. Most patients had a T1b (34%) or T2a (31%) melanoma. In the T1-T2 group (153 patients), CP-GEP achieved an SLNB RR of 41.8% (95% CI: 33.9-50.1) at an NPV of 93.8% (95% CI: 84.8-98.3). Subgroup analysis showed similar performance in T1-T2 patients ≥65 years of age (51 patients; SLNB positivity rate, 9.8%): SLNB RR of 43.1% (95% CI: 29.3-57.8) at an NPV of 95.5% (95% CI: 77.2-99.9).

CONCLUSION

We confirmed the potential of CP-GEP to reduce negative SLNB in all relevant age groups. Our findings are especially relevant to patients ≥65 years, where surgery is often elective. CP-GEP may guide SLNB decision-making in clinical practice.

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.

Prognostic significance of an 11-gene RNA assay in archival tissue of cutaneous melanoma stage I-III patients

PURPOSE

The purpose of this study was to validate the results of an 11-gene expression profiling (GEP) assay which aims to improve the precision of individual prognosis beyond conventional American Joint Committee on Cancer staging for patients with cutaneous melanoma.

METHODS

The reverse transcriptase polymerase chain reaction test of 11 prospectively selected genes was performed on 291 formalin-fixed, paraffin-embedded primary tumours of patients with stage I-III cutaneous melanoma. The expression levels of eight prognostic and three reference genes were used in a predefined algorithm to calculate a numerical score (-0.84 to 3.53) and then assign each patient to a preselected risk group (low versus high score) for melanoma-specific survival (MSS).

RESULTS

One hundred twenty-seven patients were allocated to the low-score group, with a corresponding five-year disease-free survival (DFS) and MSS of 95% and 99%, respectively. 164 patients were allocated to the high-score group, with a corresponding five-year DFS and MSS of 78% and 88%. Continuous regression analysis demonstrated decreasing MSS probabilities with increasing scores. In a multivariate cox regression, only the 11-GEP, tumour thickness and age were statistically associated with MSS (p = 0.0068, 0.002 and 0.0159).

CONCLUSIONS

The 11-GEP has been validated as an independent predictor of outcome for melanoma patients. More specifically, using an 11-GEP score cut-off of ≤0, the assay can identify patient cohorts with 10-year survival probabilities well above 90%. This information may be used in the decision-making for a potential adjuvant therapy.

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.