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

Metastasis diagnosis using attenuated total reflection-Fourier transform infra-red (ATR-FTIR) spectroscopy

The suitability of Fourier transform infrared spectroscopy as a metastasis prognostic tool has not been reported for some cancer types. Our main aim was to show spectroscopic differences between live un-preprocessed cancer cells of different metastatic levels. Spectra of four cancer cell pairs, including colon cancer (SW480, SW620); human melanoma (WM115, WM266.4); murine melanoma (B16F01, B16F10); and breast cancer (MCF7, MDA-MB-231); each pair having the same genetic background, but different metastatic level were analyzed in the regions 1400-1700 cm-1 and 3100-3500 cm-1 using Principal Component Analysis, curve fitting, multifractal dimension and receiver operating characteristic (ROC) curves. The results show spectral markers I1540/I1473, I1652/I1473, [Formula: see text], and multifractal dimension of the spectral images are significantly different for the cells based on their metastatic levels. ROC curve analysis showed good diagnostic performance of the spectral markers in separating cells based on metastatic degree, with areas under the ROC curves having 95% confidence interval lower limits greater than 0.5 for most instances. These spectral features can be important in predicting the probability of metastasis in primary tumors, providing useful guidance for treatment planning. Our markers are effective in differentiating metastatic levels without sample fixation or drying and therefore could be compactible for future use in in-vivo procedures involving spectroscopic cancer diagnosis.

Relationship of Histopathologic Parameters and Gene Expression Profiling in Malignant Melanoma

BACKGROUND

Histopathologic characteristics (HC) are a mainstay in melanoma prognosis; gene expression profiling (GEP) has emerged as a potential additional independent value.

OBJECTIVE

To elucidate HC predictive of groups obtained via GEP of malignant melanoma.

METHODS

A retrospective study analyzing HC of 265 melanomas submitted for GEP over the course of 8 years. GEP was conducted as a part of regular clinicopathologic workup through Castle Biosciences Decision Dx®.

RESULTS

Of the 265 cases, the major HC found to have an association with reported gene expression profiles were melanoma histology subtype, depth of invasion, and presence of ulcer.

LIMITATIONS

This study is limited by its cross-sectional nature. Causation and long-term related outcomes of the use of GEP versus American Joint Committee on Cancer histopathologic staging cannot be ascertained by this design.

CONCLUSIONS

An association, but no definitive prediction, exists between histopathologic categories of depth of invasion, melanoma subtype, and presence or absence of ulcer and gene expression profiles. GEP adds valuable data to the evaluation of malignant melanomas that cannot be definitively predicted by conventional models. The findings add to needed groundwork for comparison of traditional markers and molecular genotyping and begins to build a robust predictive model for better outcomes in patients with malignant melanoma.

Advances in melanoma: epidemiology, diagnosis, and prognosis

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

An update on methods for detection of prognostic and predictive biomarkers in melanoma

The approval of immunotherapy for stage II-IV melanoma has underscored the need for improved immune-based predictive and prognostic biomarkers. For resectable stage II-III patients, adjuvant immunotherapy has proven clinical benefit, yet many patients experience significant adverse events and may not require therapy. In the metastatic setting, single agent immunotherapy cures many patients but, in some cases, more intensive combination therapies against specific molecular targets are required. Therefore, the establishment of additional biomarkers to determine a patient's disease outcome (i.e., prognostic) or response to treatment (i.e., predictive) is of utmost importance. Multiple methods ranging from gene expression profiling of bulk tissue, to spatial transcriptomics of single cells and artificial intelligence-based image analysis have been utilized to better characterize the immune microenvironment in melanoma to provide novel predictive and prognostic biomarkers. In this review, we will highlight the different techniques currently under investigation for the detection of prognostic and predictive immune biomarkers in melanoma.

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.

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

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

New Technologies in Diagnosis and Prognosis of Melanocytic Lesions

Analysis of morphological characteristics for the diagnosis of melanoma remains a challenge. New technologies for the diagnosis and prognosis of melanocytic lesions have been emerging to ensure earlier and more accurate detection. In this article, we review multiple technologies that improve melanoma diagnostic accuracy such as electrical impedance spectroscopy, pigmented lesion assay, reflectance confocal microscopy, and gene expression profile tests.

CDCA3 is a prognostic biomarker for cutaneous melanoma and is connected with immune infiltration

Introduction

Dysregulation of cell cycle progression (CCP) is a trait that distinguishes cancer from other diseases. In several cancer types, CCP-related genes serve as the primary risk factor for prognosis, but their role in cutaneous melanoma remains unclear.

Methods

Data from cutaneous melanoma patients were acquired from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Using a Wilcoxon test, the level of CCP-related gene expression in cutaneous melanoma patient tissues was compared to that in normal skin tissues. Logistic analysis was then utilized to calculate the connection between the CCP-related genes and clinicopathological variables. The important functions of the CCP-related genes were further investigated using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and single-sample Gene Set Enrichment Analysis (ssGSEA). Univariate and multivariate Cox analyses and Kaplan-Meier analysis were used to estimate the association between CCP-related genes and prognosis. In addition, using Cox multivariate analysis, a nomogram was constructed to forecast the influence of CCP-related genes on survival rates.

Results

High expression of CCP-related genes was associated with TNM stage, age, pathological grade, and Breslow depth (P < 0.05). Multivariate analysis demonstrated that CCP-related genes were an independent factor in overall survival and disease-specific survival. High levels of gene expression originating from CCP were shown by GSEA to trigger DNA replication, the G1-S specific transcription factor, the mitotic spindle checkpoint, and the cell cycle. There was a negative association between CCP-related genes and the abundance of innate immune cells. Finally, we revealed that knockdown of cell division cycle-associated gene 3 (CDCA3) significantly suppressed the proliferation and migration ability of cutaneous melanoma cells.

Conclusion

According to this study, CCP-related genes could serve as potential biomarkers to assess the prognosis of cutaneous melanoma patients and are crucial immune response regulators.

Updates in Cutaneous Oncology

Cutaneous oncology is currently a rapidly evolving field. Dermoscopy, total body photography, biomarkers, and artificial intelligence are affecting the way skin cancers, especially melanoma, are diagnosed and monitored. The medical management of locally advanced and metastatic skin cancer is also changing. In this article, we will discuss recent developments in cutaneous oncology with a particular focus on treatment of advanced cancers.

Using Gene Expression Profiling to Personalize Skin Cancer Management

Risk-stratification of cancer, traditionally performed through staging, directs optimal disease management decisions with the result of improved patient outcomes. Many forms of cutaneous cancer have overall excellent survival rates, but conventional staging methods are imperfect in identifying high-risk patients. Gene expression profiling (GEP) is a clinically available, objective metric that can be used in conjunction with traditional clinicopathological staging to help clinicians stratify risk in patients with skin cancer, even in those who lack traditional risk markers. For patients with melanoma, the 31-GEP test provides personalized prognostic information that can guide risk-appropriate clinical management and surveillance decisions. The i31-GEP integrates 31-GEP results with clinicopathological features to provide a risk of recurrence (i31-GEP for ROR) and likelihood of having a positive sentinel lymph node biopsy (SLNB) (i31-GEP for SLNB) for patients with melanoma. For patients with cutaneous squamous cell carcinoma who have at least one risk factor, the 40-GEP test allows for better risk stratification by identifying the high-risk patients who are most likely to develop metastasis. These tests can be easily integrated into clinical practice to help guide treatment choices.

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.

Contrast mechanisms in pump-probe microscopy of melanin

Pump-probe microscopy of melanin in tumors has been proposed to improve diagnosis of malignant melanoma, based on the hypothesis that aggressive cancers disaggregate melanin structure. However, measured signals of melanin are complex superpositions of multiple nonlinear processes, which makes interpretation challenging. Polarization control during measurement and data fitting are used to decompose signals of melanin into their underlying molecular mechanisms. We then identify the molecular mechanisms that are most susceptible to melanin disaggregation and derive false-coloring schemes to highlight these processes in biological tissue. We demonstrate that false-colored images of a small set of melanoma tumors correlate with clinical concern. More generally, our systematic approach of decomposing pump-probe signals can be applied to a multitude of different samples.

An Epidemiologic Analysis of Melanoma Overdiagnosis in the United States, 1975-2017

The primary cause of the increase in melanoma incidence in the United States has been suggested to be overdiagnosis. We used Surveillance, Epidemiology, and End Result Program data from 1975 to 2017 to examine epidemiologic trends of melanoma incidence and mortality and better characterize overdiagnosis in white Americans. Over the 43-year period, incidence and mortality showed discordant temporal changes across population subgroups; trends most suggestive of overdiagnosis alone were present in females aged 55-74. Other groups showed mixed changes suggestive of overdiagnosis plus changes in underlying disease risk (decreasing risk in younger individuals and increasing risk in older males). Cohort effects were identified for male and female mortality and male incidence but were not as apparent for female incidence, suggesting that period effects have had a greater influence on changes in incidence over time in females. Encouraging trends included long-term declines in mortality in younger individuals and recent stabilization of invasive incidence in individuals aged 15-44 years and males aged 45-54 years. Melanoma in situ incidence, however, has continued to increase throughout the population. Overdiagnosis appears to be relatively greater in American females and for melanoma in situ.

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.

Adjuvant and Neoadjuvant Therapies in Cutaneous Melanoma

Melanoma is the most common cause of skin cancer-related death in the United States. Cutaneous melanoma is most prevalent in the head and neck. The long-term prognosis has been poor and chemotherapy is not curative. Complete surgical resection with locally advanced disease can be challenging and melanoma is resistant to radiation. Advances made in immunotherapy and genomically targeted therapy have transformed the treatment of metastatic melanoma; as of 2021, the 5-year survival for metastatic melanoma is greater than 50%. Ongoing clinical studies are underway to integrate these life-saving therapies into the presurgical or postsurgical settings. This article reviews that effort.

Classification and Staging of Melanoma in the Head and Neck

The rates of melanoma continue to rise, with recent estimates have shown that 18% to 22% of new melanoma cases occur within the head and neck in the United States each year. The mainstay of treatment of nonmetastatic primary melanomas of the head and neck includes the surgical resection and management of regional disease as indicated. Thorough knowledge of the classification and staging of melanoma is paramount to evaluate prognosis, determine the appropriate surgical intervention, and assess eligibility for adjuvant therapy and clinic trials. The traditional clinicopathologic classification of melanoma is based on morphologic aspects of the growth phase and distinguishes 4 of the most common subtypes as defined by the World Health Organization: superficial spreading, nodular, acral lentiginous, and lentigo maligna melanoma. The data used to derive the AJCC TNM Categories are based on superficial spreading melanoma and nodular subtypes. Melanoma is diagnosed histopathologically following initial biopsy that will assist with classifying the tumor to guide treatment. Classification is based on tumor thickness and ulceration (T stage, Breslow Staging), Regional Lymph Node Involvement (N Stage), and presence of metastasis (M Stage). Tumor thickness (Breslow thickness) and ulceration are 2 independent prognostic factors that have been shown to be the strongest predictors of survival and outcome. Clark level of invasion and mitotic rate are no longer incorporated into the current AJCC staging system, but still have shown to be important prognostic factors for cutaneous melanoma. For patients with metastatic (Stage IV) disease Lactate Dehydrogenase remains an independent predictor of survival. The Maxillofacial surgeon must remain up to date on the most current management strategies in this patient population. Classification systems and staging provide the foundation for clinical decision making and prognostication for the Maxillofacial surgeon when caring for these patients.

Treat Now or Treat Later: Comparative Effectiveness of Adjuvant Therapy in Resected Stage IIIA Melanoma

BACKGROUND

Adjuvant therapy for most sentinel-node-positive (stage IIIA) melanoma may have limited clinical benefit for older patients given the competing risk of non-cancer death. The objective of this study is to model the clinical effect and cost of adjuvant therapy in stage IIIA melanoma across age groups.

STUDY DESIGN

A Markov decision analysis model simulated the overall survival of patients with resected stage IIIA melanoma treated with adjuvant therapy vs observation. In the adjuvant approach, patients are modeled to receive adjuvant pembrolizumab (BRAF wild type) or dabrafenib/trametinib (BRAF mutant). In the observation approach, treatment is deferred until recurrence. Transition variables were derived from landmark randomized trials in adjuvant and salvage therapy. The model was analyzed for age groups spanning 40 to 89 years. The primary outcome was the number needed to treat (NNT) to prevent one melanoma-related death at 10 years. Cost per mortality avoided was estimated using Medicare reimbursement rates.

RESULTS

Projections for NNT among BRAF wild type patients increased by age from 14.71 (age 40 to 44) to 142.86 (age 85 to 89), with patients in cohorts over the age of 75 having an NNT over 25. The cost per mortality avoided ranged from $2.75 million (M) (age 40 to 44) to $27.57M (age 85 to 89). Corresponding values for BRAF mutant patients were as follows: NNT 18.18 to 333.33; cost per mortality avoided ranged from $2.75M to $54.70M.

CONCLUSION

Universal adjuvant therapy for stage IIIA melanoma is costly and provides limited clinical benefit in patients older than 75 years.

Improved cutaneous melanoma survival stratification through integration of 31-gene expression profile testing with the American Joint Committee on Cancer 8th Edition Staging

Cutaneous melanoma (CM) survival is assessed using averaged data from the American Joint Committee on Cancer 8th edition (AJCC8). However, subsets of AJCC8 stages I-III have better or worse survival than the predicted average value. The objective of this study was to determine if the 31-gene expression profile (31-GEP) test for CM can further risk-stratify melanoma-specific mortality within each AJCC8 stage. This retrospective multicenter study of 901 archival CM samples obtained from patients with stages I-III CM assessed 31-GEP test predictions of 5-year melanoma-specific survival (MSS) using Kaplan-Meier and Cox proportional hazards. In stage I-III CM population, patients with a Class 2B result had a lower 5-year MSS (77.8%) than patients with a Class 1A result (98.7%) and log-rank testing demonstrated significant stratification of MSS [χ2 (2df, n = 901) = 99.7, P < 0.001). Within each stage, 31-GEP data provided additional risk stratification, including in stage I [χ2 (2df, n = 415) = 11.3, P = 0.004]. Cox regression multivariable analysis showed that the 31-GEP test was a significant predictor of melanoma-specific mortality (MSM) in patients with stage I-III CM [hazard ratio: 6.44 (95% confidence interval: 2.61-15.85), P < 0.001]. This retrospective study focuses on Class 1A versus Class 2B results. Intermediate results (Class 1B/2A) comprised 21.6% of cases with survival rates between Class 1A and 2B, and similar to 5-year MSS AJCC stage values. Data from the 31-GEP test significantly differentiates MSM into lower (Class 1A) and higher risk (Class 2B) groups within each AJCC8 stage. Incorporating 31-GEP results into AJCC8 survival calculations has the potential to more precisely assess survival and enhance management guidance.

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.

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.

Comparison Analysis of Gene Expression Profiles Proximity Metrics

The problems of gene regulatory network (GRN) reconstruction and the creation of disease diagnostic effective systems based on genes expression data are some of the current directions of modern bioinformatics. In this manuscript, we present the results of the research focused on the evaluation of the effectiveness of the most used metrics to estimate the gene expression profiles’ proximity, which can be used to extract the groups of informative gene expression profiles while taking into account the states of the investigated samples. Symmetry is very important in the field of both genes’ and/or proteins’ interaction since it undergirds essentially all interactions between molecular components in the GRN and extraction of gene expression profiles, which allows us to identify how the investigated biological objects (disease, state of patients, etc.) contribute to the further reconstruction of GRN in terms of both the symmetry and understanding the mechanism of molecular element interaction in a biological organism. Within the framework of our research, we have investigated the following metrics: Mutual information maximization (MIM) using various methods of Shannon entropy calculation, Pearson’s χ2 test and correlation distance. The accuracy of the investigated samples classification was used as the main quality criterion to evaluate the appropriate metric effectiveness. The random forest classifier (RF) was used during the simulation process. The research results have shown that results of the use of various methods of Shannon entropy within the framework of the MIM metric disagree with each other. As a result, we have proposed the modified mutual information maximization (MMIM) proximity metric based on the joint use of various methods of Shannon entropy calculation and the Harrington desirability function. The results of the simulation have also shown that the correlation proximity metric is less effective in comparison to both the MMIM metric and Pearson’s χ2 test. Finally, we propose the hybrid proximity metric (HPM) that considers both the MMIM metric and Pearson’s χ2 test. The proposed metric was investigated within the framework of one-cluster structure effectiveness evaluation. To our mind, the main benefit of the proposed HPM is in increasing the objectivity of mutually similar gene expression profiles extraction due to the joint use of the various effective proximity metrics that can contradict with each other when they are used alone.

Risk Stratification of Patients with Stage I Cutaneous Melanoma Using 31-Gene Expression Profiling

BACKGROUND

While patients with localized cutaneous melanoma (CM) generally have good five-year melanoma-specific survival rates, identifying patients with localized disease at a high risk of recurrence could allow them access to additional follow-up or surveillance.

OBJECTIVE

We sought to examine the prognostic value of the 31-gene expression profile (31-GEP) test for the risk of recurrence in stage I CM patients according to 31-GEP main class (low risk: Class 1 vs. high-risk: Class 2) and the lowest and highest risk 31-GEP subclasses (Class 1A vs. Class 2B).

METHODS

Data from a previously described meta-analysis detailing the 31-GEP results for patients with stage I CM (N = 623) were re-analyzed to determine 31-GEP accuracy.

RESULTS

Patients with stage I CM and a Class 1 31-GEP result were less likely to have a recurrence (15/556; 2.7% vs. 6/67; 9.0%; p=0.018) than patients with a Class 2 result and had a higher five-year recurrence-free survival (RFS) (96% vs. 85%). Patients with a Class 2 result were 2.8 times as likely to experience a recurrence (positive likelihood ratio: 2.82; 95% confidence interval: 1.38-5.77). In a subset of patients with stage I CM stratified further into 31-GEP subclasses (n = 206), patients with a Class 1A result had a higher five-year RFS than those with a Class 2B result (98% vs. 73%). Patients with a Class 2B result were also 6.5 times as likely to experience a recurrence (positive likelihood ratio: 6.45; 95% confidence interval: 2.44-17.00) than those with a Class 1A result, and the 31-GEP had a negative predictive value of 96.3% (95% confidence interval: 92.3%-98.4%).

CONCLUSION

The 31-GEP test significantly differentiates between low and high recurrence risk in patients with stage I CM.

Identifying High-Risk Tumors within AJCC Stage IB-III Melanomas Using a Seven-Marker Immunohistochemical Signature

Simple Summary

Immunotherapy and targeted therapy are widely accepted for stage III and IV melanoma patients. Clinical investigation of adjuvant therapy in stage II melanoma has already started. Therefore, methods for relapse prediction in lower stage melanoma patients apart from sentinel node biopsies are much needed to guide (neo)adjuvant therapies. Gene scores such as the “DecisionDx-Melanoma” and the “MelaGenix” score can help assist therapy decisions. However, a seven-marker immunohistochemical signature could add valuable feasibility to the biomarker toolbox.

Abstract

Background: We aim to validate a seven-marker immunohistochemical signature, consisting of Bax, Bcl-X, PTEN, COX-2, (loss of) ß-Catenin, (loss of) MTAP and (presence of) CD20, in an independent patient cohort and test clinical feasibility. Methods: We performed staining of the mentioned antibodies in tissue of 88 primary melanomas and calculated a risk score for each patient. Data were correlated with clinical parameters and outcome (recurrence-free, distant metastasis-free and melanoma-specific survival). Results: The seven-marker signature was able to identify high-risk patients within stages IB-III melanoma patients that have a significantly higher risk of disease recurrence, metastasis, and death. In particular, the high sensitivity of relapse prediction (>94%) in sentinel negative patients (stages IB–IIC) was striking (negative predictive value of 100% for melanoma-specific survival and distant metastasis-free survival, and 97.5% for relapse-free survival). For stage III patients (positive nodal status), the negative predictive value was 100% with the seven-marker signature. Conclusions: The seven-marker signature can help to further select high-risk patients in stages IIB-C but also in earlier stages IB–IIA and be a useful tool for therapy decisions in the adjuvant and future neo-adjuvant settings. Stage III patients with measurable lymph node disease classified as high-risk with the seven-marker signature are potential candidates for neoadjuvant immunotherapy.

Multidisciplinary Care of BRAF-Mutant Stage III Melanoma: A Physicians Perspective Review

Prognosis among patients with stage III melanoma can vary widely depending on the risk of disease relapse. Therefore, it is vital to optimize patient care through accurate diagnosis and staging as well as thoughtful treatment planning. A multidisciplinary team (MDT) approach, which involves active collaboration among physician specialists across a patient's disease journey, has been increasingly adopted as the standard of care for treatment of a variety of cancers, including melanoma. This review provides an overview of MDT care principles for patients with BRAF‐mutant–positive, stage III cutaneous melanoma and summarizes current literature, clinical experiences, and institutional best practices. Therapeutic goals from dermatologic, surgical, and medical oncologist perspectives regarding MDT care throughout a patient's disease course are discussed. Additionally, the role of each specialty's involvement in testing for predictive biomarkers at relevant time points to facilitate informed treatment decisions is discussed. Last, instances of successful MDT treatment of other cancers and key lessons to optimize MDT patient care in cutaneous melanoma are provided. Several aspects of MDT patient care are considered vital, such as the importance of staging via pathological examination and imaging, biomarker testing, and interdisciplinary physician and patient engagement throughout the course of treatment. Use of MDTs has the potential to improve patient care in cutaneous melanoma by improving the speed and accuracy of diagnosis, implementing a personalized treatment plan early on, and being proactive in adverse event management. Physician perspectives described in this review may lead to better outcomes, quality of life, and overall patient satisfaction.

A multidisciplinary team (MDT) approach has been increasingly adopted as the standard of care for treatment of a variety of cancers, including melanoma. This review provides an overview of MDT care principles for patients with BRAF‐mutant–positive, stage III cutaneous melanoma and summarizes current literature, clinical experiences, and institutional best practices.

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.

A Dermatologist's Guide to Implementation of Gene Expression Profiling in the Management of Melanoma

BACKGROUND. With the advent of effective therapeutics, melanoma mortality rates have decreased, yet incidence rates are continuing to rise, making accurate prognostication for risk of recurrence increasingly important. Gene expression profiling (GEP) is a clinically available, objective metric that can be used in conjunction with traditional clinicopathological staging to help physicians stratify risk in melanoma patients. There is a gap in guidance from the American Joint Committee on Cancer (AJCC) and the National Comprehensive Cancer Network (NCCN) regarding how to utilize GEP in melanoma care. OBJECTIVE. An expert panel of 31-GEP test users sought to provide clarification of use options and a rational clinical workflow to guide appropriate application of the 31- GEP test in everyday practice. METHODS. The authors participated in an in-depth review of the literature and panel discussion regarding current limitations of melanoma risk assessment and opportunities for improvement with GEP. The panel reviewed 1) validation and clinical impact data supporting the use of sentinel lymph node biopsy (SLNB), 2) existing primary data and meta-analyses for 31-GEP testing in melanoma risk assessment, 3) AJCC, NCCN, and Melanoma Prevention Working Group (MPWG) data and guidelines for GEP use in melanoma risk assessment, and 4) experiences, rationales, and scenarios in which 31-GEP testing may be helpful for risk assessment. RESULTS. The 31-GEP test is useful and actionable for patient care when applied in accordance with current NCCN guidelines. Stratification of patients into low (Class 1a), intermediate (Class 1b or 2a), or high (Class 2b) risk categories can inform multidisciplinary conference discussion and can assist with determining the intensity of imaging, surveillance, and follow-up care. Patient-specific features of the disease and individual circumstances should be considered in the decision to use 31-GEP testing. CONCLUSION. The authors suggest a clinical workflow that integrates 31-GEP testing under the umbrella of current national guidelines. Application of the test in appropriate patient populations can improve risk assessment and inform clinical decision-making.

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

Importance

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

Objective

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

Data Sources

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

Study Selection

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

Data Extraction and Synthesis

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

Main Outcomes and Measures

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

Results

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

Conclusions and Relevance

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