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

Jarell, Abel; Gastman, Brian; Dillon, Larry D; Hsueh, Eddy C.; Podlipnik, Sebastián; Covington, Kyle R.; Cook, Robert W.; Bailey, Christine; Quick, Ann P.; Martin, Brian J.; Kurley, Sarah J.; Goldberg, Matthew S.; Puig, Susana

doi:10.1016/j.jaad.2022.06.1202

Cited by 20 publications

(16 citation statements)

References 35 publications

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“…A few CM prognosis models have been established, such as the hypoxia-related risk model and the cuproptosis-related model. Meanwhile, new biomarkers related to DNA and RNA molecular mechanisms have also been investigated to study the prognosis of melanoma in recent years ( Riefolo et al, 2019 ; Jarell et al, 2022 ). However, the efficacy and prognosis are poor due to the metastasis of CM.…”

Section: Discussionmentioning

confidence: 99%

A novel risk model based on anoikis: Predicting prognosis and immune infiltration in cutaneous melanoma

et al. 2023

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Cutaneous melanoma (CM) is a highly aggressive malignancy with a dimal prognosis and limited treatment options. Anoikis is believed to involve in the regeneration, migration, and metastasis of tumor. The exact role of anoikis-related genes (ARGs) in the development and progression of cutaneous melanoma, however, remains elusive. Four ARGs (SNAI2, TFDP1, IKBKG, and MCL1) with significant differential expression were selected through Cox regression and LASSO analyses. Data for internal and external cohorts validated the accuracy and clinical utility of the prognostic risk model based on ARGs. The Kaplan–Meier curve indicated a much better overall survival rate of low-risk patients. Notably, we also found that the action of ARGs in the CM was mediated by immune-related signaling pathways. Consensus clustering and TIME landscape analysis also indicated that the low-risk score patients have excellent immune status. Moreover, the results of immunotherapy response and drug sensitivity also confirmed the potential implications of informing individualized immune therapeutic strategies for CM. Collectively, the predictive risk model constructed based on ARGs provides an excellent and accurate prediction tool for CM patients. This present research provides a rationale for the joint application of targeted therapy and immunotherapy in CM treatment. The approach could have great therapeutic value and make a contribution to personalized medicine therapy.

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Section: Discussionmentioning

confidence: 99%

A novel risk model based on anoikis: Predicting prognosis and immune infiltration in cutaneous melanoma

et al. 2023

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“…After screening the articles, 32 met inclusion criteria and were distributed to the panelists for review prior to the roundtable discussion. Of these articles, the number of papers that specifically studied the validity, accuracy, or clinical utility of each test was: 22 for the 31-GEP test 4,[19][20][21][22][23][24][26][27][28][29][30][31][34][35][36][37][38][39][40][41][42] , 2 for the 11-GEP test 11,34 , and 7 for the 8-GEP + CP test. 5,13,23,[43][44][45][46]…”

Section: Comprehensive Literature Searchmentioning

confidence: 99%

“…For the 8-GEP + CEP test, a validation study analyzing the test's ability to predict SLNB positivity found that this model has a NPV of 90.5% (95% Confidence Interval [CI]: 77.9-96.2%) in T1-4 CMs. 44 In comparing the three GEP tests, the panel consensus was that there were significantly more studies supporting the validity, accuracy, and clinical utility of the 31-GEP test compared to the 11-GEP and 8-GEP + CP tests (22 studies validating the 31-GEP test 4,[19][20][21][22][23][24][26][27][28][29][30][31][34][35][36][37][38][39][40][41][42] , compared to just 2 studies validating the 11-GEP test 11,34 and 7 studies validating the 8-GEP + CP test 5,13,23,[43][44][45][46] ). Based on the limited studies for the 8-GEP + CP and the 11-GEP test, the panel concluded that there was insufficient data to assess their validity and utility or currently recommend usage in the clinical setting until further studies are performed.…”

Section: Consensus Recommendationsmentioning

confidence: 99%

Incorporating Prognostic Gene Expression Profile Assays into the Management of Cutaneous Melanoma: An Expert Consensus Panel

Zakria

Brownstone²,

Berman³

et al. 2023

J of Skin

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Background: Cutaneous melanoma (CM) guidelines put forth by the eighth edition of the American Joint Committee on Cancer (AJCC8) and the National Comprehensive Cancer Network (NCCN) do not currently account for lesion genomics when assessing prognosis. Gene Expression Profile (GEP) tests have become a widely adopted tool to help clinicians identify patients at higher risk for metastasis and recurrence. Objective: To review the available literature that has been published since a consensus panel in 2018 on three commercially available GEP tests used in the prognostic assessment of CM and create updated guidelines and consensus statements for their optimal use. Methods: A comprehensive literature search of PubMed and Google Scholar was conducted for relevant English-language original research articles, meta-analyses, and systematic reviews published from 2019 through 2022. A panel of 6 key opinion leaders in dermatology with specialized expertise in diagnosing and managing CM then convened to review the articles and create guidelines. A modified Delphi process was used to approve each statement. The panel assigned each article a level of evidence and each consensus statement a strength of recommendation using Strength of Recommendation Taxonomy (SORT) criteria. Results: The literature search identified 785 articles that met the search criteria. Of these, there were 22 articles that validated the 31-GEP test, 2 that validated the 11-GEP test, and 7 that validated the 8-GEP + CP test. The panel unanimously approved 6 usage guidelines and 5 consensus supporting statements for the appropriate use of these tests. Conclusion: Based on the currently available literature, GEP tests provide valuable information beyond AJCC8 and NCCN guidelines for the prognostic assessment of CM. There are significantly more validation studies supporting the use of the 31-GEP test compared to the 11-GEP test and the 8-GEP + CP test.

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“…Despite these limitations, this study provides evidence that the i31-GEP for SLNB can guide risk-aligned SLNB decisions. 22,26,27 Decisions regarding patient care are made in multi-disciplinary settings, and tools such as the i31-GEP for SLNB supplement traditional clinical and pathologic factors and offer independent, objective risk prediction to aid clinicians in determining the best treatment plan for individual patients. However, clinicians need confidence that a tool will provide precise prognostic information.…”

Section: Skinmentioning

confidence: 99%

“…20 Recently, the 31-GEP score was integrated with clinical and pathological features using a neural network algorithm to accurately and precisely identify patients with a <5% risk of having a positive SLN who may forego SLNB (i31-GEP for SLNB), 21 and separately using Cox regression to identify patients at low or high risk of recurrence, metastasis, or death from melanoma (i31-GEP for ROR). 22 The i31-GEP for SLNB provides high sensitivity and negative predictive value in patients with T1aHR-T2 melanoma. 23 The purpose of this study was to compare the ability of the i31-GEP for SLNB versus the MIA nomogram outputs to predict SLN positivity and analyze the precision of the results to assess the ability to appropriately apply the results in the clinical setting in patients with T1aHR-T2 melanomas.…”

mentioning

confidence: 96%

The Integrated 31-Gene Expression Profile (i31-GEP) Test for Cutaneous Melanoma Outperforms a Clinicopathologic-only Nomogram at Identifying Patients who can Forego Sentinel Lymph Node Biopsy

Zakria¹,

Brownstone²,

Rigel

2022

J of Skin

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Introduction: National guidelines for cutaneous melanoma suggest avoiding sentinel lymph node biopsy (SLNB) if the risk of SLN positivity is <5% (T1a with no high-risk features), considering SLNB if the risk is 5-10% (T1a with additional high-risk features (T1aHR) and T1b), and offering SLNB if the risk is >10% (T2-T4). Because most patients (88%) who undergo an SLNB have a negative result, novel tools to identify patients who can safely forgo SLNB are critical. The integrated 31-gene expression profile (i31-GEP for SLNB) test for cutaneous melanoma combines tumor molecular biology with clinicopathologic features to provide a precise risk of SLN positivity. The Melanoma Institute of Australia (MIA) developed a nomogram that uses only clinicopathologic features to predict SLN positivity. Methods: We compared the i31-GEP for SLNB to the MIA nomogram in patients with T1-T2 tumors with complete data (n=582). The precision of each tool to identify patients with <5% SLN positivity risk was analyzed using 95% confidence intervals. To be considered low risk, the predicted risk must be <5% and the upper 95% confidence interval must be ≤10%, and to be considered high-risk, the predicted risk must be >10% and the lower 95% CI ≥5%. Results: The i31-GEP for SLNB identified 28.5% (166/582) of patients as having a <5% risk of SLN positivity while also having an upper 95% CI ≤10% compared with 0.9% (5/582, p<0.001) using the MIA nomogram. In patients with a pre-test likelihood of SLN positivity of 5-10% (T1aHR-T1b), the i31-GEP reclassified risk in 60.2% (171/284) of patients as being <5% or >10% compared to 13.7% (39/284, p<0.001) using the MIA nomogram. In patients with a known SLN status (n=466), the i31-GEP for SLNB identified 22.1% (103/466) of patients as having <5% risk, with a 3.9% (4/103) SLN positivity rate compared to 0.6% (3/466, p<0.001) identified by the MIA as having a <5% risk with a 33.3% (1/3) SLN positivity rate. Conclusions: The i31-GEP test outperformed the MIA nomogram in identifying patients who could safely forego SLNB. Integrating the 31-GEP molecular risk stratification tool with clinicopathologic features provides precise SLN positivity risk to better guide patient management in patients with T1-T2 tumors, for whom SLNB guidance could be most impactful.

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

Cited by 20 publications

References 35 publications

A novel risk model based on anoikis: Predicting prognosis and immune infiltration in cutaneous melanoma

A novel risk model based on anoikis: Predicting prognosis and immune infiltration in cutaneous melanoma

Incorporating Prognostic Gene Expression Profile Assays into the Management of Cutaneous Melanoma: An Expert Consensus Panel

The Integrated 31-Gene Expression Profile (i31-GEP) Test for Cutaneous Melanoma Outperforms a Clinicopathologic-only Nomogram at Identifying Patients who can Forego Sentinel Lymph Node Biopsy

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