Society of Surgical Oncology Consensus Statement: Assessing the Evidence for and Utility of Gene Expression Profiling of Primary Cutaneous Melanoma

INTRODUCTION

Gene expression profiling (GEP) of primary cutaneous melanoma aims to offer prognostic and predictive information to guide clinical care. Despite limited evidence of clinical utility, these tests are increasingly incorporated into clinical care.

METHODS

A panel of melanoma experts from the Society of Surgical Oncology convened to develop recommendations regarding the use of GEP to guide management of patients with melanoma. The use of currently available GEP tests were evaluated in three clinical scenarios: (1) the utility in patient selection for sentinel lymph node biopsy; (2) the utility to guide surveillance; and (3) the utility to inform adjuvant therapy. As a basis for these recommendations, the panel performed a systematic review of the literature, including articles published from January 2012 until August 2023.

RESULTS

After review of 137 articles, 50 met the inclusion criteria. These articles included evidence related to three available GEP tests: 31-GEP, CP-GEP, and 11-GEP. The consensus recommendations were finalized using a modified Delphi process. The panel found that current evidence often fails to account for known clinicopathologic risk factors and lacks high-level data. The panel recognizes that the study of GEP tests is still evolving. The integration of GEP into routine clinical practice for predicting sentinel lymph node status and patient prognosis in melanoma is therefore not currently recommended.

CONCLUSION

At present, GEP should be considered primarily an investigational tool, ideally used in the context of clinical trials or specialized research settings.

How Many Sentinel Lymph Nodes Should We Excise in Patients With Melanoma?

INTRODUCTION

The management of many patients with early-stage melanoma includes sentinel lymph node (SLN) biopsy for prognostic and treatment planning purposes. While the minimum necessary number of SLNs to examine has been determined for patients with other malignancies, it has not been delineated in melanoma. The current study evaluates risk factors for SLN positivity and the associated number of SLNs that are necessary to examine for appropriate staging.

MATERIALS AND METHODS

The National Cancer Database participant user file from 2018 to 2020 was queried for clinically node-negative patients who underwent SLN biopsy. Descriptive statistics were obtained. Analysis of variance statistical analyses were performed.

RESULTS

Eight thousand forty eight melanoma patients out of 48,748 were identified from 2018 to 2020 that had lymph node positivity on SLN biopsy. The median age of patients was 64. The male-to-female ratio was 1.47. Chi-squared analysis revealed that there was a statistically significant difference in positivity rate between at least two groups (P = 0.006) for primary melanoma site, male sex (P < 0.01), race, age, histologic type, Breslow thickness, and lymphovascular invasion (P < 0.001). SLN positivity rate increased with the number of SLNs examined until plateauing at 4 SLNs. There was no statistical difference between positivity for 3 SLNs and larger numbers of SLNs examined. Propensity matching revealed no statistically significant difference in positive rate when more than 2 SLNs were biopsied.

CONCLUSIONS

SLN positivity is proportionally related to the number of SLNs examined, suggesting that surgeons should attempt to remove a minimum of 2 SLNs for the optimal staging of patients with melanoma.

IFN-γ-responsiveness of lymphatic endothelial cells inhibits melanoma lymphatic dissemination via AMPK-mediated metabolic control

The integrity of the lymphatic system is critical for preventing the dissemination of tumor cells, such as melanoma, to distant parts of the body. IFN-γ is well studied as a negative regulator for lymphangiogenesis, which is strongly associated with cancer metastasis. However, the exact mechanisms underlying this process remain unclear. In the present study, we investigated whether IFN-γ signaling in lymphatic endothelial cells (LECs) affects tumor cell dissemination by regulating the barrier function of tumor-associated lymphatic vessels. Using LEC-specific IFN-γ receptor (IFN-γR) knockout mice, we found that the loss of IFN-γR in LECs increased the dissemination of melanoma cells into the draining lymph nodes. Notably, IFN-γ signaling in LECs inhibited trans-lymphatic endothelial cell migration of melanoma cells, indicating its regulation of lymphatic barrier function. Further investigations revealed that IFN-γ upregulated the expression of the tight junction protein Claudin-3 in LECs, while knockdown of Claudin-3 in LECs abolished IFN-γ-induced inhibition of trans-lymphatic endothelial migration activity. Mechanistically, IFN-γ inhibits AMPK signaling activation, which is involved in the regulation of fatty acid metabolism. Modulating fatty acid metabolism and AMPK activation in LECs also affected the lymphatic dissemination of melanoma cells, further confirming that this process is involved in IFN-γ-induced regulation of lymphatic barrier function. These results provide novel insights into how IFN-γ modulates tight junctions in LECs, inhibiting the dissemination of melanoma cells via the lymphatic vessels.

Clinical, dermatoscopic, histological and molecular predictive factors of distant melanoma metastasis: A systematic review and meta-analysis

BACKGROUND

Melanoma metastasis to distant sites is associated with diminished survival rates and poor prognosis. Except of Breslow thickness and ulceration that are currently used in melanoma staging, the investigation of additional clinicopathological, dermatoscopic and molecular factors that could predict tumors with aggressive biologic behavior is of paramount importance.

METHODS

A literature search was conducted in PubMed, Scopus, Cochrane databases and gray literature until November 2023. Observational studies (including cohorts and case-control studies) were included and clinical and histopathological factors of primary cutaneous melanomas, along with dermatoscopic and molecular predictors of distant metastasis (DM) and distant metastasis-free survival (DMFS) were assessed. Random - effect models were preferred, the results were presented as Hazard Ratios (HRs) with 95 %Confidence Intervals (CIs) and the I2 index quantified heterogeneity. Subgroup analysis according to AJCC stage and sensitivity analysis were also conducted.

RESULTS

One hundred forty-three and 101 studies were included in the qualitive and quantitative synthesis, respectively. Regarding clinical factors, males, compared to females, and head and neck location, compared to trunk, demonstrated higher risk for DM [n=36, HR 1.49, 95%CI 1.36 - 1.63, I2 33% and n=21, HR 1.24, 95 %CI 1.01 - 1.52, I2 62 %]. Both factors had similar effects on DMFS. Breslow thickness and ulceration were significant predictors or DM. Additional factors that posed an increased risk for DM were nodular (n=15, HR 2.51, 95 %CI 1.83 - 3.43, I2 56 %) and lentigo maligna subtypes (n=12, HR 1.87, 95 %CI 1.27 - 2.75, I2 0 %), compared to superficial spreading subtype, lymphovascular invasion (n=9, HR 2.05, 95 %CI 1.18 - 3.58, I2 78 %), SLN positivity and BRAF+ mutational status. In contrast, regression was a negative predictor of DM (n=15, HR 0.59, 95 %CI 0.44 - 0.79, I2 68 %). Two studies focused on dermatoscopic factors and found that low pigmentation and the presence of blue-white veil might predict DM development. The results of subgroup analysis for stage I-II patients were essentially similar and sensitivity analysis did not reveal significant alterations, despite the moderate or high heterogeneity in some categories.

CONCLUSIONS

Clinical and histological characteristics of the tumor along with dermatoscopic features and molecular parameters hold significant prognostic information and could be incorporated into models to predict melanomas with high metastatic potential.

Management of melanoma: can we use gene expression profiling to help guide treatment and surveillance?

Although the incidence of cutaneous melanoma (CM) has been increasing annually, the mortality rate has been decreasing, likely due to better prevention, earlier detection, improved surveillance, and the development of new therapies. Current clinical management guidelines by the National Comprehensive Cancer Network (NCCN) are based on patient risk assignment using staging criteria established by the American Joint Committee on Cancer (AJCC). However, some patients with localized disease (stage I-II), generally considered to have a good prognosis, will develop metastatic disease and die, whereas some patients with later stage disease (stage III-IV) will be cured by surgery, adjuvant therapy, and/or systemic therapy. These results emphasize the importance of identifying patients whose risk may be over or underestimated with standard staging. Gene expression profile (GEP) tests are noninvasive molecular tests that assess the expression levels of a panel of validated genes, providing information about tumor prognosis, including the risk of recurrence, metastasis, and cancer-specific death. GEP tests can provide prognostic information beyond standard staging that may aid clinicians and patients in treatment and surveillance management decisions. This review describes how combining clinicopathologic staging with a robust assessment of tumor biology may provide information that will allow more refined intervention and long-term management.

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.

Overinterpretation of findings in machine learning prediction model studies in oncology: a systematic review

OBJECTIVES

In biomedical research, spin is the overinterpretation of findings, and it is a growing concern. To date, the presence of spin has not been evaluated in prognostic model research in oncology, including studies developing and validating models for individualized risk prediction.

STUDY DESIGN AND SETTING

We conducted a systematic review, searching MEDLINE and EMBASE for oncology-related studies that developed and validated a prognostic model using machine learning published between 1st January, 2019, and 5th September, 2019. We used existing spin frameworks and described areas of highly suggestive spin practices.

RESULTS

We included 62 publications (including 152 developed models; 37 validated models). Reporting was inconsistent between methods and the results in 27% of studies due to additional analysis and selective reporting. Thirty-two studies (out of 36 applicable studies) reported comparisons between developed models in their discussion and predominantly used discrimination measures to support their claims (78%). Thirty-five studies (56%) used an overly strong or leading word in their title, abstract, results, discussion, or conclusion.

CONCLUSION

The potential for spin needs to be considered when reading, interpreting, and using studies that developed and validated prognostic models in oncology. Researchers should carefully report their prognostic model research using words that reflect their actual results and strength of evidence.

Malignant Melanoma in Older Adults: Different Patient or Different Disease?

Objective In this study, we aimed to compare the clinical outcomes between older and younger patients with melanoma and to evaluate for differences in tumor genetic makeup that might explain differences in clinical behavior between older and younger cohorts. Materials and methods A consecutive sample of patients diagnosed with melanoma at a single institution from 1984 to 2019 was categorized by age into younger, middle, and older cohorts. Tumor characteristics, melanoma-specific survival, and recurrence-free survival were assessed while accounting for differential follow-up and death from other causes using Kaplan-Meier analysis with log-rank testing. Results A total of 4378 patients were included in the study. Older patients presented with a higher incidence of T3 and T4 tumors, and a lower incidence of T1 tumors (p<0.001). The same group of patients had a lower nodal positivity at any given Breslow thickness (p<0.01). Melanoma-specific survival was lower for older patients with T2 tumors (p=0.046). There was no difference in recurrence-free survival among all age groups and tumor thicknesses (p>0.05). For patients with a given genetic profile, the melanoma-specific survival and recurrence-free survival were equivalent across ages. BRAF was the most common driver in the younger group, while NRAS and other mutations increased in prevalence as age rose. Conclusions Older adults have decreased melanoma-specific survival for T2 tumors and lower nodal positivity, suggesting a different pattern of metastatic progression. The mutational drivers of cutaneous melanoma change with age and may play a role in the different metastatic progression as well as the differential melanoma-specific survival across all age cohorts.

Influencing factors associated with lymph node status in patients with cutaneous melanoma: An Asian population study

BACKGROUND

Sentinel lymph node (SLN) status is the predominant prognostic factor in patients diagnosed with clinically localized melanoma. The significance of completion lymph node dissection in patients with SLN metastasis is debatable. Not many studies have been conducted on acrallentiginous melanoma (ALM). This study aimed to characterize the prognostic factors of nodal positive ALM and confirm whether ALM patients can undergo the same treatment strategy as non-ALM patients in the Asian population.

METHODS

This is a retrospective review of patients who underwent surgery for cutaneous melanoma (CM) at Taipei Veterans General Hospital between January 1993 and December 2019. We investigated the risk factors for lymph node status. The association between clinicopathological factors and lymph node status of ALM and non-ALM patients was analyzed. Outcomes of completion lymph node dissection (CLND) performed following sentinel lymph node biopsy (SLNB) in the CM and ALM groups were compared.

RESULTS

A total of 197 patients were included in this study. ALM was the most common histological subtype, accounting for 66.5% of all the cases. Patients in the CM and ALM subgroups with metastatic SLN ( p = 0.012) or lymph nodes ( p < 0.001 and p = 0.001) exhibited higher mortality rate. Multivariate analysis showed that patients with clinical presentation of T4 category tumor ( p = 0.012) and lymphovascular invasion ( p = 0.012) had a significantly higher risk of positive lymph nodes. The overall survival of patients with lymph nodes metastasis was not associated with the performance of CLND.

CONCLUSION

Patients in the CM or ALM subgroups with metastatic SLNs or lymph nodes exhibited significantly poorer overall survival. Advanced Breslow thickness and lymphovascular invasion were independent predictive factors for CM and ALM patients with positive lymph node status. There was no significant difference in survival between CM and ALM patients following SLNB, regardless of CLND being performed.

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.

Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review

BACKGROUND

Describe and evaluate the methodological conduct of prognostic prediction models developed using machine learning methods in oncology.

METHODS

We conducted a systematic review in MEDLINE and Embase between 01/01/2019 and 05/09/2019, for studies developing a prognostic prediction model using machine learning methods in oncology. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, Prediction model Risk Of Bias ASsessment Tool (PROBAST) and CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) to assess the methodological conduct of included publications. Results were summarised by modelling type: regression-, non-regression-based and ensemble machine learning models.

RESULTS

Sixty-two publications met inclusion criteria developing 152 models across all publications. Forty-two models were regression-based, 71 were non-regression-based and 39 were ensemble models. A median of 647 individuals (IQR: 203 to 4059) and 195 events (IQR: 38 to 1269) were used for model development, and 553 individuals (IQR: 69 to 3069) and 50 events (IQR: 17.5 to 326.5) for model validation. A higher number of events per predictor was used for developing regression-based models (median: 8, IQR: 7.1 to 23.5), compared to alternative machine learning (median: 3.4, IQR: 1.1 to 19.1) and ensemble models (median: 1.7, IQR: 1.1 to 6). Sample size was rarely justified (n = 5/62; 8%). Some or all continuous predictors were categorised before modelling in 24 studies (39%). 46% (n = 24/62) of models reporting predictor selection before modelling used univariable analyses, and common method across all modelling types. Ten out of 24 models for time-to-event outcomes accounted for censoring (42%). A split sample approach was the most popular method for internal validation (n = 25/62, 40%). Calibration was reported in 11 studies. Less than half of models were reported or made available.

CONCLUSIONS

The methodological conduct of machine learning based clinical prediction models is poor. Guidance is urgently needed, with increased awareness and education of minimum prediction modelling standards. Particular focus is needed on sample size estimation, development and validation analysis methods, and ensuring the model is available for independent validation, to improve quality of machine learning based clinical prediction models.

RIPK3 and AXL Expression Study in Primary Cutaneous Melanoma Unmasks AXL as Predictor of Sentinel Node Metastasis: A Pilot Study

Malignant melanoma (MM) is the most lethal skin cancer. AXL is a tyrosine kinase receptor involved in several oncogenic processes and might play a role in blocking necroptosis (a regulated cell death mechanism) in MM through the downregulation of the necroptotic-related driver RIPK3. The aim of this study was to evaluate the clinical impact of the expression of AXL and RIPK3 in 108 primary cutaneous MMs. Association between AXL and RIPK3 immunoreactivity and clinical-pathological variables, sentinel lymph node status, and tumor-infiltrating lymphocytes (TILs) was assessed. Immunoreaction in tumor cells was detected in 30 cases (28%; range, 5%-80%) and in 17 cases (16%; range, 5%-50%) for AXL and RIPK3, respectively. Metastases in the sentinel lymph nodes were detected in 14 out of 61 patients, and these were associated with AXL-positive immunoreactivity in the primary tumor (p < 0.0001). No association between AXL and TILs was found. RIPK3 immunoreactivity was not associated with any variables. A final logistic regression analysis showed Breslow and AXL-positive immunoreactivity as the stronger predictor for positive sentinel node status [area under the receiver operating characteristic curve (AUC) of 0.96]. AXL could be a potential new biomarker for MM risk assessment, and it deserves to be further investigated in larger studies.

Sentinel Lymph Node Metastasis on Clinically Negative Patients: Preliminary Results of a Machine Learning Model Based on Histopathological Features

The reported incidence of node metastasis at sentinel lymph node biopsy is generally low, so that the majority of women underwent unnecessary invasive axilla surgery. Although the sentinel lymph node biopsy is time consuming and expensive, it is still the intra-operative exam with the highest performance, but sometimes surgery is achieved without a clear diagnosis and also with possible serious complications. In this work, we developed a machine learning model to predict the sentinel lymph nodes positivity in clinically negative patients. Breast cancer clinical and immunohistochemical features of 907 patients characterized by a clinically negative lymph node status were collected. We trained different machine learning algorithms on the retrospective collected data and selected an optimal subset of features through a sequential forward procedure. We found comparable performances for different classification algorithms: on a hold-out training set, the logistics regression classifier with seven features, i.e., tumor diameter, age, histologic type, grading, multiplicity, in situ component and Her2-neu status reached an AUC value of 71.5% and showed a better trade-off between sensitivity and specificity (69.4 and 66.9%, respectively) compared to other two classifiers. On the hold-out test set, the performance dropped by five percentage points in terms of accuracy. Overall, the histological characteristics alone did not allow us to develop a support tool suitable for actual clinical application, but it showed the maximum informative power contained in the same for the resolution of the clinical problem. The proposed study represents a starting point for future development of predictive models to obtain the probability for lymph node metastases by using histopathological features combined with other features of a different nature.

Age and Melanocytic Lesions

Age plays a dynamic role in incidence, presentation, and extent of disease for cutaneous melanoma. Even within the spectrum of juvenile melanoma, there exists a range of spitzoid and nonspitzoid melanocytic and melanoma lesions. Spitzoid melanomas, a more favorable disease in juvenile patients, are malignant lesions and require treatment as such. Lymph node metastases in melanoma occur at lower rates in older patients compared with younger counterparts, yet the rate of metastases is still high. Age appears to play an important role in the development and progression of melanoma, and understanding the differences across age populations is important when counseling patients.

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.

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.

Risk factors for positive sentinel lymph node, lymphatic or hematogenous dissemination over time in patients with cutaneous melanoma

The aim of the present study was to assess the influence of localization, age or sex and histopathological characteristics upon the chance of developing lymphatic or hematogenous metastatic spread over time, or a positive sentinel lymph node in cutaneous melanoma patients. Patients from the Department of Dermatology, County Emergency Hospital Cluj-Napoca (Cluj-Napoca, Romania), presenting with cutaneous melanoma confirmed histopathologically and a SPECT/CT or lymphoscintigraphic examination to detect the sentinel lymph node, were included in the present study. Our results revealed that Breslow index >2 mm [odds ratio (OR)=4.22, 95% confidence interval (CI) (1.12; 15.93)], presence of ulceration [OR=6.01, 95% CI (1.87; 19.35)], and positive sentinel lymph node [for at least one sentinel lymph node OR=3.58, 95% CI (1.06; 12.04)] were risk factors for hematogenous metastases. All these, except for the Breslow index >2 mm, were demonstrated to be a risk factor for lymphatic spread metastases over time. Ulceration and male sex also represented risk factors for a positive sentinel lymph node, men having a higher risk of developing sentinel lymph nodes than women [adjusted OR=2.27, 95% CI (1.00; 5.13)]. In conclusion, the predictors that influence the occurrence of lymphatic or hematogenous metastases may differ, ulceration and positive sentinel lymph node being common for both types of metastatic spread, while Breslow index being a significant predictor only for hematogenous metastases. Male sex and the presence of ulceration were demonstrated to be significant risk factors for positive sentinel lymph nodes.

Dissecting the Lymphatic System to Predict Melanoma Metastasis

Melanoma is the most lethal form of skin cancer in the United States. Current American Joint Committee on Cancer (AJCC) staging uses Breslow depth and ulceration as the two primary tumor factors that predict metastatic risk in cutaneous melanoma. Early disease stages are generally associated with high survival rates. However, in some cases, patients with thin melanomas develop advanced disease, suggesting other factors may contribute to the metastatic potential of an individual patient’s melanoma. This review focuses on the role of the lymphatic system in the metastasis of cutaneous melanoma, from recent discoveries in mechanisms of lymphangiogenesis to elements of the lymphatic system that ultimately may aid clinicians in determining which patients are at highest risk. Ultimately, this review highlights the need to integrate pathological, morphological, and molecular characteristics of lymphatics into a “biomarker” for metastatic potential.