Going Beyond Staging for Gene Expression Profiling in Melanoma and Cutaneous Squamous Cell Carcinoma

Key Takeaways

• Gene Expression Profile (GEP) testing quantifies primary tumor mRNA expression to stratify prognostic risk of tumor progression independent of, and complementary to, TNM staging.
• The 31-GEP (DecisionDx-Melanoma) classifies tumors into four risk classes; large population-based data show Class 2B is an independent predictor of melanoma-specific mortality (HR 7.00).
• The 2026 NCCN Melanoma Guidelines include the CP-GEP (Merlin) assay for SLNB shared decision-making in T1b/T2a disease.
• The 40-GEP (DecisionDx-SCC) stratifies cSCC into three risk classes, with Class 2B 3-year metastasis-free survival of 44%, with particular clinical utility in guiding adjuvant radiation therapy decisions.
• GEP is most actionable in patients for whom staging provides inadequate prognostic utility and where a molecular result would meaningfully change management.

Staging has served as the foundation of oncologic decision-making in dermatology for decades. Yet every clinician who has practiced long enough has encountered the patient with a seemingly low-risk tumor who progresses, and conversely, the patient with a seemingly high-risk tumor who does not. Gene expression profiling (GEP) represents an attempt to move beyond population-level risk estimates and toward a more individualized understanding of tumor biology. By measuring mRNA expression of defined gene sets in primary tumor tissue, GEP tests aim to capture molecular features that predict behavior independent of traditional histopathologic variables.^1,2

Three commercially available GEP tests have accumulated substantial clinical validation for the most common and consequential cutaneous malignancies: the 31-gene expression profile (31-GEP; DecisionDx-Melanoma, Castle Biosciences) for melanoma, the Clinicopathologic-gene expression profile (CP-GEP, Merlin), and the 40-gene expression profile (40-GEP; DecisionDx-SCC, Castle Biosciences) for cutaneous squamous cell carcinoma (cSCC). The tests not only were included in National Cancer Comprehensive Netowrk (NCCN) guidelines in 2026 but have also generated a growing literature that includes both manufacturer-supported validation studies and independent institutional data.^3,4,5 Understanding what this evidence actually shows, including its limitations, is essential for clinicians deciding on when, how, and for which patients to order these tests.

WHY STAGING ALONE IS NOT ENOUGH

The American Joint Committee on Cancer (AJCC) 8th edition provides a validated and globally standardized framework for melanoma prognostication, incorporating Breslow thickness, ulceration, mitotic rate, and nodal status.6 For cSCC, both the AJCC 8th edition and the Brigham and Women’s Hospital (BWH) system offer clinically useful risk stratification, with BWH T2b associated with nodal metastasis in approximately 21% of patients and disease-specific death in approximately 20%.7 These are definitive, meaningful, population-level estimates. The problem is that they do not tell us which individual patient in that population will experience recurrence.

An AJCC stage IIA melanoma carries an estimated 10-year survival of approximately 80%, meaning one in five patients at this stage will recur, yet we cannot identify them prospectively using pathology alone.6 In cSCC, even within the same BWH stage stratum, outcomes vary substantially. The molecular biology of the primary tumor, including patterns of gene regulation, immune evasion, and epithelial plasticity, drives metastatic behavior in ways that Breslow thickness and perineural invasion cannot fully capture. GEP attempts to read this biology directly from the tumor tissue.

As outlined by Mehrmal et al in a two-part Journal of the American Academy of Dermatology (JAAD) CME series on GEP in dermatology, GEP test development follows a stepwise process: discovery, analytical validation, clinical validity, and clinical utility. Each step must be demonstrated independently, and it is important for clinicians to understand at which step a given test’s evidence is strongest and where gaps remain.^1,2

GEP IN MELANOMA

What the 31-GEP Test Measures

The assay uses RT-PCR to quantify the expression of 28 discriminant genes, normalized against 3 control genes, in the primary tumor, generating a continuous 31-GEP score from 0 to 1. The four class designations are derived from this score: Class 1A (0.00 to 0.41), Class 1B (0.42 to 0.49), Class 2A (0.50 to 0.58), and Class 2B (0.59 to 1.00). The 31-GEP score is then fed into two validated neural network algorithms: i31-SLNB, which generates a personalized probability of sentinel lymph node (SLN) positivity benchmarked against NCCN thresholds of less than 5%, 5 to 10%, and greater than 10% (validated by Whitman et al),⁸ and i31-ROR, which generates patient-specific 5-year Risk of Recurrence (ROR) and Risk of Nodal Metastasis (RONM) estimates, including melanoma-specific survival, distant metastasis-free survival, and recurrence-free survival for AJCC Stage I to III disease (validated by Jarell et al).9 These individualized probability outputs, not the class call alone, are what most directly inform clinical decisions about surveillance intensity and SLN biopsy (SLNB) candidacy. In the DECIDE studies, prospective multicenter data confirmed the algorithm: no patient with an i31-SLNB predicted risk under 5% had a positive sentinel node (0 of 35 patients), and 3-year recurrence-free survival in that group was 97.8%.^10,11,12 

Clinical Validity: What the Data Show About 31-GEP

The foundational clinical validation study by Gerami et al, published in JAAD in 2015, enrolled 217 patients undergoing SLNB and demonstrated that 31-GEP class was a more significant independent predictor of disease-free, distant metastasis-free, and overall survival than SLNB status alone (P < 0.0001 for all endpoints).¹³ Among patients with a Class 2 (high-risk) result and a negative SLNB, 5-year distant metastasis-free survival was 49%, underscoring that a negative sentinel node does not fully exclude biologically aggressive tumor behavior.

Independent validation in a cohort of 523 patients by Zager et al demonstrated 5-year recurrence-free survival of 88% for Class 1 vs 52% for Class 2 tumors, and 5-year distant metastasis-free survival of 93% vs 60%, respectively (P < 0.001).¹⁴ GEP class remained an independent predictor in multivariate analysis inclusive of Breslow thickness, ulceration, mitotic rate, and SLN status. A meta-analysis of 1,479 patients by Greenhaw et al further confirmed that Class 2B designation was consistently associated with higher rates of recurrence, distant metastasis, and death across pooled cohorts.¹⁵

The largest population-based dataset comes from a collaboration between Castle Biosciences and the National Cancer Institute’s SEER program, which is a useful resource for future research projects. Bailey et al linked 31-GEP clinical results for 4,687 stage I–III melanoma patients to 17 SEER registries and found that 3-year melanoma-specific survival (MSS) was 99.7% for Class 1A, 97.1% for Class 1B/2A, and 89.6% for Class 2B (P < 0.001). Class 2B was an independent predictor of MSS with a hazard ratio of 7.00 (95% CI, 2.70–18.00). Notably, patients who underwent 31-GEP testing showed 29% lower MSS mortality compared to propensity-matched, untested patients—an association the authors attributed to risk-aligned management decisions enabled by the test.¹⁶

REAL-WORLD PERFORMANCE OF 31-GEP

Not all institutional data align with manufacturer-sponsored results. A real-world performance analysis of the 31-GEP at Mayo Clinic by Pazhava et al, published in the International Journal of Dermatology in 2025, reviewed 31-GEP testing across 8 years of clinical use and found that test results did not alter standard clinical management in 81.5% of cases. SLNB decisions were unaffected in 92% of patients with pre-SLNB results. Contrary to expectations, the rate of nodal metastasis was numerically higher in low-risk than in high-risk GEP cases in their cohort, and survival curve analysis showed overlapping recurrence-free and MSS curves between GEP classes.3 This is an important institutional counterpoint. It does not invalidate the larger validation literature, but it reinforces that GEP results must be interpreted within the full clinical context and that their impact on management is variable depending on the practice setting and patient population. A key methodological distinction worth noting is that the Mayo analysis evaluated class designations rather than the integrated i31-GEP algorithmic outputs; the prospective DECIDE data from Whitman et al and Beard et al, which employed the full i31-SLNB algorithm rather than class calls alone, demonstrated substantially stronger predictive performance in identifying patients at the NCCN-defined thresholds for SLNB decision-making.^8,10,11,12

Within the T1b subgroup specifically, Joshi et al analyzed 31-GEP testing in AJCC pT1b melanomas using the SEER-DecisionDx database and found that the test provided meaningful risk stratification for predicting SLNB positivity within this staging category, which is clinically important given the heterogeneity of outcomes among T1b tumors where SLNB decisions are debated.¹⁷ A companion analysis by the same group evaluated pT1a melanomas and found that the test similarly provided prognostic stratification within this earliest staging category.¹⁸ Taken together, real-world performance data are most informative when evaluated using the integrated i31-GEP algorithm rather than class calls in isolation, given that the algorithm incorporates the continuous gene expression score alongside clinicopathologic variables to generate individualized probability estimates rather than categorical risk bins.^8,11,12

What the CP-GEP Test Measures

The CP-GEP analyzes the expression of eight melanoma-associated genes from the primary tumor with two clinicopathologic variables: Breslow thickness and patient age.4  The results output into a single binary value of high risk or low risk for sentinel node metastasis. It is the only commercially available GEP assay that formally integrates clinical and molecular data into one unified classification.^4,5

Clinical Validity: What the Data Show About the CP-GEP

The MERLIN_001 prospective validation by Hieken et al enrolled 1,761 patients with T1-T3 melanoma who underwent SLNB.⁵ Of patients tested, 37% were classified as low risk and 63% as high-risk. SLN positivity was 7.1% in the low-risk group versus 23.8% in the high-risk group, approximately a three-fold difference based on classification alone. It is important to note that the primary endpoint of negative predictive value in the low-risk group was not met in their study. A pointed critique by Farberg and colleagues, published in Dermatology Times, noted that the test was designed to identify patients with less than 5% SLN positivity risk (the current NCCN-defined threshold below which SLNB is not recommended) yet the actual Low-Risk positivity rate in MERLIN_001 was 7.1%, falling short of that benchmark. This is a clinically meaningful limitation: a test intended to identify patients who can safely forgo SLNB should ideally achieve the accepted threshold for doing so, and at 7.1%, some clinicians argue the evidence does not yet support routine deferral based on a Low-Risk result alone.

Real-World Peformance of the CP-GEP

A US validation cohort of 208 patients from Mayo Clinic and West Virginia University demonstrated an SLNB reduction rate of 41.8% at a net predictive value (NPV) of 93.8% in T1–T2 tumors, with similar performance in patients aged 65 and older.¹⁹ A Dutch independent validation study by Zijlker et al in 252 patients found that 5-year overall survival for CP-GEP low-risk patients was 89.6%, raising the hypothesis that CP-GEP low risk may be a more precise risk selector than a negative sentinel node.²⁰ A Tübingen cohort of 930 patients with early-stage melanoma who did not undergo SLNB demonstrated remarkably strong prognostic separation: 10-year recurrence-free survival HR 20.07, distant metastasis-free survival HR 19.39, and melanoma-specific survival HR 35.85 between Low- and High-Risk groups (P < 0.001 for all).²¹

SNLB Decision-Making and the 2026 NCCN Update

One of the most clinically consequential applications of melanoma GEP testing is its potential to inform SLNB decisions. The DECIDE study, a prospective multicenter trial, evaluated the i31-GEP algorithm and demonstrated that use of the i31-GEP was associated with a reduced number of SLNB procedures in patients deemed low risk, without apparent harm to those who deferred the procedure. Importantly, the risk reduction in the DECIDE data was specifically anchored to the NCCN-defined threshold of less than 5% predicted SLN positivity risk, not merely less than 10%. Patients identified by the i31-GEP as below this 5% threshold had a 0% observed SLN positivity rate and a 3-year recurrence-free survival of 97.8%, meeting the accepted benchmark for safe deferral of the procedure.¹⁰ However, the 2026 NCCN Melanoma Guidelines (version 1.2026) mentions the CP-GEP (Merlin assay), which integrates 8-gene expression data with clinicopathologic variables, for shared decision-making regarding SLNB in patients with T1b and T2a melanoma based on prospective MERLIN_001 trial data. The i31-GEP retains a role in risk stratification and surveillance planning, but the 2026 guidelines have yet to include it as a SLNB decision tool.²²

THE 40-GEP IN CUTANEOUS SQUAMOUS CELL CARCINOMA (cSCC)

Assay Design and Technical Performance

The 40-GEP analyzes 34 discriminant gene targets and 6 normalization genes from primary cSCC tissue using RT-PCR to classify tumors as Class 1 (low risk), Class 2A (higher risk), or Class 2B (highest risk) of regional or distant metastasis within 3 years of diagnosis.^23,24 The test is designed for patients with at least one high-risk clinicopathologic feature by current staging criteria, but it is not validated for recurrent tumor tissue or for patients without any risk factors.

Clinical Validation

The pivotal validation study by Wysong et al, published in JAAD in 2021, collected archival tissue and clinicopathologic data from 586 patients at 23 independent centers in a prospectively designed biomarker study.²³ A GEP signature was developed in a discovery cohort (n = 202) and validated in a separate, nonoverlapping independent cohort (n = 324). In the validation cohort, 3-year metastasis-free survival differed substantially by class: 91.4% for Class 1, 80.6% for Class 2A, and 44.0% for Class 2B.²³ The positive predictive value of metastasis for the Class 2B group was 60% and the 40-GEP remained an independent predictor of metastatic risk after adjustment for AJCC and BWH staging factors.

Extended validation in a multicenter performance study by Wysong et al incorporating 897 patients from 58 centers further confirmed that 40-GEP class significantly stratified metastatic risk profiles, with nested Cox regression models demonstrating that combining the 40-GEP with current clinicopathologic risk classification systems improved metastatic risk prediction over staging alone.²⁵ A complementary analysis by Ibrahim et al demonstrated enhanced metastatic risk assessment with the 40-GEP specifically within high-risk clinicopathologic subgroups,  including immunosuppressed patients, patients with perineural invasion, and head and neck tumors, where staging systems have historically performed poorly.²⁶ The clinical implication of this stratification capacity was demonstrated by Ruiz et al, who showed that 40-GEP Class 2B tumors were associated with the greatest magnitude of benefit from adjuvant radiation therapy (ART), with ART-treated Class 2B patients showing a median 50% reduction in 5-year disease progression rate compared with untreated Class 2B patients; this differential benefit was not observed in Class 1 tumors, supporting the use of 40-GEP to select patients most likely to benefit from ART.²⁷

Clinical Utility: Treatment Decisions and ART

Perhaps the most mature application of the 40-GEP is its role in ART decision-making. Current guidelines broadly recommend ART consideration for patients with multiple high-risk cSCC features, but patient selection is imprecise: many patients receiving ART may not benefit from it, while others who need it may not receive it. The 40-GEP provides molecular risk stratification that can sharpen this selection.

A study by Arron et al in 2024 examined the association between 40-GEP class and metastatic disease progression, and specifically evaluated whether 40-GEP could identify which patients benefited from ART.^28,27 The investigators demonstrated that 40-GEP Class 2B tumors had the highest rates of metastatic progression, and that ART was associated with a meaningful reduction in metastatic events in the high-risk molecular group—providing one of the first molecular frameworks for ART selection in cSCC.^28,27

Multidisciplinary consensus guidelines for integrating 40-GEP results into ART recommendations proposed a risk-stratified algorithm in which Class 2B tumors warrant strong consideration of ART given their substantially elevated metastatic risk.²⁹ Class 2A tumors represent a shared decision-making scenario, and Class 1 tumors may allow clinicians to de-escalate ART consideration when staging criteria are otherwise borderline.²⁹ A clinical algorithm integrating 40-GEP results with staging and other management decisions for high-risk cSCC was similarly described by Singh, Tolkachjov, and Farberg, offering a practical framework for incorporating test results into multidisciplinary care.³⁰

In summary, when a patient desires de-escalation while the staging suggests escalation of imaging and adjuvant care in SCC, the 40-GEP may provide extra information to make a shared decision. Traditional staging and guidelines should still be weighed more heavily, and de-escalation based on a Class 1 40-GEP should be carefully discussed and done with caution. If the 40-GEP suggests escalation (Class 2A/2B) and staging is low, imaging and closer follow-up may be best in low-stage patients while reinforcement of escalation for adjuvant imaging, radiation, and possibly immunotherapy may be best in high-stage and Class 2A/2B patients. As few test results show a Class 2B status, these patients should be closely monitored at any stage, and a medical oncology referral for imaging, radiation, and immunotherapy may be considered.

WHEN IS GEP TESTING ACTUALLY HEPLFUL?

The most important question clinicians face is not whether GEP testing is prognostically valid but whether the result will change what you do for a specific patient sitting in front of you. If the answer is no, ordering the test adds cost and potentially anxiety without benefit. If the answer is yes, it may materially affect decisions about surgery, radiation, surveillance intensity, and specialist referral. Given the multiple validation studies and incorporation of some tests into the NCCN, it is now obvious that GEP testing is not only valid but also does change management. 

Where the 31-GEP Adds Value

The 31-GEP is most useful in T1b and T2 melanomas where the clinical picture is ambiguous: a patient who is a borderline SLNB candidate, a patient who declines SLNB, or a patient treated with Mohs micrographic surgery (MMS) at a center where SLNB is not performed concurrently. In these settings, a Class 2B result provides objective molecular data supporting higher-risk classification, potentially prompting SLNB referral, medical oncology consultation, or more intensive imaging surveillance. A Class 1A result in the same setting offers additional reassurance, though it does not eliminate recurrence risk. Beyond the class designation, the i31-SLNB algorithm generates a personalized probability of SLN positivity and the i31-ROR algorithm outputs patient-specific 5-year ROR and RONM estimates, allowing more individualized counseling than class designation alone.^8,11,12

A nomogram integrating 31-GEP class with T stage, developed and validated in a large multicenter MMS cohort by Jarell et al, demonstrated superior metastatic risk prediction over either variable alone.9 For practices that perform MMS for melanoma, this nomogram represents a practical tool: GEP can be ordered from the same Mohs tissue specimen, providing molecular prognostic data at the point of definitive surgical management.

In stage I melanoma, specifically, the question of who is truly at low risk is clinically important. A patient with a T1b melanoma and a Class 2B result sits in a very different risk category than a T1b/Class 1A patient, and this distinction may meaningfully inform surveillance scheduling and patient counseling even when SLNB is not being pursued. The i31-ROR algorithm can translate this class distinction into patient-specific 5-year ROR and RONM estimates, giving clinicians concrete percentages to use during shared decision-making conversations.9 The SEER data from Bailey et al showing a 3-year MSS of 89.6% for Class 2B vs 99.7% for Class 1A across 4,687 real-world patients provides concrete numbers for this conversation.¹⁶

One of the most useful clinical scenarios for the 31-GEP in dermatology is in the node-negative patient. When a patient has a negative SLNB, most oncologists will refer back to dermatology. However, only clinical exams and review of systems are recommended. Knowing that some melanomas are biologically more aggressive while others are less, the 31-GEP may suggest a closer follow-up or serial imaging.³¹ In Class 2A/2B, routine imaging every 6 months can help capture recurrences and metastases earlier yet still be judicious in the use of resources.    

Where the CP-GEP Adds Value

The CP-GEP is most valuable in T1b and T2a melanoma, where the SLNB conversation is most genuinely uncertain.²² These are patients who meet current NCCN criteria for SLNB consideration but where the absolute probability of nodal disease spans a wide range, and where the morbidity of the procedure, patient age, comorbidities, and individual preference all legitimately factor into the decision. A low-risk CP-GEP result in an elderly patient with a 0.9 mm, non-ulcerated T1b melanoma on the head and neck, where SLNB carries meaningful procedural complexity and the baseline positivity rate is already lower, shifts the conversation meaningfully between patient and physician.5 The estimated SLN positivity in that low-risk group in MERLIN_001 was 6.5% for T1b tumors, and 4.9% for head and neck primaries, providing a concrete, evidence-based probability to anchor shared decision-making.5 

Conversely, a high-risk result in a patient or provider who was leaning toward surveillance over SLNB provides objective molecular data supporting proceeding with the procedure. The CP-GEP adds the least value at the extremes: in a healthy 35-year-old with a T2b melanoma where SLNB is straightforwardly indicated regardless, or in a T1a tumor where SLNB is not being considered at all.

PRACTICAL DIFFERENCES BETWEEN 31-GEP AND CP-GEP

Published uses of these tests have been discussed. In summary, the CP-GEP test offers a binary (high or low risk) result for patients that may safely forgo SLNB in SLNB-eligible patients with a recent (<90 days) diagnosis of cutaneous melanoma. A meaningful comparison between the CP-GEP and the 31-GEP should reference the i31-ROR algorithm outputs rather than class call designations alone: the i31-ROR generates patient-specific 5-year ROR and RONM estimates by integrating the continuous GEP score with clinicopathologic variables, providing algorithmic probability outputs that are directly analogous to the binary classification framework of CP-GEP.9

Both use clinicopathologic factors as part of their algorithms. The CP-GEP was developed integrating genes, age, and Breslow depth simultaneously, while the 31-GEP was first developed as an independent genetic prognostic variable with clinicopathologic factors like Breslow thickness, mitotic rate, ulceration, and age added for an algorithm of risk of SLNB positivity and subsequent risk of recurrence with node status and tumor location added to the other factors.

Both companies have proprietary and differing explanations of which genes are relevant to melanoma, which are discriminant, and which are control, making it hard to compare “gene specificity” and which genes, if not all, truly affect the algorithm and clinical outcomes.  

cSCC: Where the 40-GEP Adds Value

For cSCC, the 40-GEP is most clinically useful in BWH T2a and T2b tumors, where risk is high enough to warrant attention but heterogeneous enough that staging alone does not guide management with precision. A T2b/Class 2B patient with a 3-year metastasis-free survival of 44% warrants a fundamentally different management conversation than a T2b/Class 1 patient. The former should prompt consideration of PET-CT or dedicated nodal imaging, SLNB evaluation, radiation oncology consultation for ART, and intensive nodal surveillance. The latter may be appropriately managed with clinical and ultrasound surveillance without aggressive upfront intervention.

For T1 tumors with high-risk features that nonetheless fall short of T2a staging criteria, the 40-GEP may also add value. Ibrahim et al demonstrated that the test improved metastatic risk stratification in clinically relevant subgroups including perineural invasion and head and neck location—two scenarios where staging systems have limited discriminatory ability.^25,26 In immunosuppressed patients, where cSCC behavior is particularly aggressive and unpredictable, GEP may provide molecular context that informs the intensity of post-excision management.

WHEN GEP ADDS LITTLE

In stage III or IV melanoma with confirmed nodal or distant disease, GEP adds little to clinical management as systemic therapy with checkpoint inhibitors or targeted agents is already clearly indicated. In very low-risk cSCC (BWH T1, single risk factor, complete excision with clear margins), the baseline metastasis risk is low enough that even a Class 2B result may not dramatically change management, and the absolute benefit of the test requires careful contextualization. Circulating tumor DNA (ctDNA) in this context may be of utility as monitoring of melanoma after systemic therapy. Unlike GEP, which captures the biology of the primary tumor at excision, ctDNA provides a longitudinal, treatment-responsive assessment of disease burden particularly relevant for patients receiving adjuvant immunotherapy, where serial monitoring may identify molecular recurrence before radiographic progression.³² GEP is also of limited utility in the postoperative setting when the primary tumor block is unavailable, when tumor content in the submitted sample is insufficient, or when the patient has already progressed to regional or systemic disease. In patients where GEP was obtained at diagnosis, the i31-ROR algorithm’s continuous ROR output can help contextualize the decision to pursue ctDNA monitoring: a patient with a calculated 5-year ROR exceeding 10–12% represents a population for whom serial ctDNA surveillance may provide clinically meaningful longitudinal risk assessment beyond what the primary tumor biopsy alone could offer.

A NOTE ON EVIDENCE QUALITY AND LIMITATIONS

The GEP evidence base, while substantial in volume, has meaningful limitations that clinicians should understand. Many validation studies, including the large multicenter studies, have been conducted or sponsored by the manufacturers of GEP assays. Manufacturer involvement does not invalidate study findings, but independent replication is important. 

A second limitation is the absence of prospective randomized data demonstrating that GEP-guided management decisions improve survival outcomes, as opposed to simply predicting them. Whether knowing a Class 2B result leads clinicians to take actions that materially extend survival beyond what standard of care already achieves is not yet proven. As Mehrmal et al note in the JAAD CME series, moving from clinical validity to clinical utility is the hardest step in GEP test development, and it requires outcome data that the field is still generating^.1,2

Finally, GEP results should never override clinical and pathologic context. The evolution toward an integrated algorithmic approach, as embodied by the i31-ROR and i31-SLNB algorithms, represents a meaningful advancement: by combining the continuous gene expression score with clinicopathologic variables to yield patient-specific probability estimates, this approach moves beyond a static class call system and provides individualized melanoma-specific survival insights that can inform shared decision-making at every stage of disease.^8,9,11,12 A Class 2B GEP in a 0.5-mm, non-ulcerated melanoma with no other adverse features requires more careful interpretation than the same result in a 1.8-mm, ulcerated tumor. The same class does not convey the same absolute risk across all clinical scenarios. 

COUNSELING PATIENTS ON GEP RESULTS

GEP results should be delivered with the same thoughtfulness as any molecular diagnostic result. Patients need to understand that GEP is a genetic probability tool, telling us about the biology of their specific tumor, but it does not determine their fate. A Class 2B result means the tumor carries molecular features associated with higher metastatic risk in patients with similar tumors, but it does not mean metastasis is certain. A Class 1A result is reassuring, but it does not eliminate the need for standard surveillance or preclude recurrence.

The authors recommend framing results in absolute terms when possible. Saying a tumor is “high risk by molecular testing” is less meaningful than explaining that in validated studies of similar tumors, Class 2B cSCC had a metastasis rate of approximately 56% at 3 years, compared to approximately 8.6% for Class 1. This shapes how aggressively we approach adjuvant therapy and surveillance. Presenting both the staging-based and GEP-based risk estimates together gives patients the most complete picture and supports genuine shared decision-making.

If this were a family member whose staging suggests elevated but imprecise risk, you would want every piece of objective prognostic information available. GEP provides an additional data point grounded in the biology of that individual tumor. That data point may confirm the risk suggested by staging, reveal unexpectedly high-risk biology despite favorable staging, or offer reassurance that allows de-escalation of planned intervention. None of those outcomes are trivial.

GEP testing is most useful when ordered proactively at the time of primary tumor excision or Mohs surgery, when the tissue block is available and clinical decisions are still open. Retrospective ordering after sentinel node biopsy, adjuvant therapy decisions, or disease progression has already occurred offers little actionable value beyond research and clinical validation.

CONCLUSION

GEP in melanoma and cSCC represents a genuine advance in the molecular characterization of individual tumor risk. The 31-GEP, CP-GEP, and 40-GEP have each accumulated substantial prospective and population-level validation data supporting their prognostic utility, and have earned recognition in current NCCN guidelines.^22,16,26 Their integration into established workflows, particularly Mohs micrographic surgery, where tissue supports both definitive margin assessment and molecular risk profiling, makes GEP practically accessible at the point of care.

At the same time, clinicians should approach GEP testing with clear-eyed appreciation of its limitations: the evidence is predominantly manufacturer-sponsored and prospective outcomes data proving that GEP-guided decisions improve survival are still emerging. 

Used thoughtfully in the right patients, at the right clinical decision point, interpreted in context rather than in isolation, GEP testing offers dermatologists a meaningful tool for the individualized management of cutaneous malignancies. As the JAAD CME series by Mehrmal et al tells us, the goal is not to replace staging with molecular data but to give each patient the most complete and accurate understanding of their tumor’s biology that current science allows.^1,2 When patients ask for “personalized medicine,” “a patient-specific approach,” or just “more information than is present in staging alone,” GEP tests offer another data point to complement traditional staging. n

1. Mehrmal S, Tan MG, Arron ST, et al. Gene expression profiling (GEP) in dermatology, part 1: introduction, development, benefits, limitations, and future directions of GEP. J Am Acad Dermatol. Published online October 27, 2025. https://doi.org/10.1016/j.jaad.2025.10.084

2. Mehrmal S, Tan MG, Arron ST, et al. Gene expression profiling (GEP) in dermatology, part 2: clinical applications of GEP in dermatology. J Am Acad Dermatol. Published online October 24, 2025. https://doi.org/10.1016/j.jaad.2025.10.086

3. Pazhava A, Kim YH, Jing FZ, Pittelkow MR. 31-GEP (DecisionDx): a review of clinical utility and performance in a Mayo Clinic cohort. Int J Dermatol. 2025;64(3):563-570. https://doi.org/10.1111/ijd.17440

4. Bellomo D, Arias-Mejias SM, Ramana C, et al. Model combining tumor molecular and clinicopathologic risk factors predicts sentinel lymph node metastasis in primary cutaneous melanoma. JCO Precis Oncol. 2020;4:319-334. https://doi.org/10.1200/PO.19.00206

5. Hieken TJ, Egger ME, Angeles CV, et al. Gene expression profile-based test to predict melanoma sentinel node status: the MERLIN_001 study. JAMA Surg. 2025;160(12):1358-1366. https://doi.org/10.1001/jamasurg.2025.4399

6. Gershenwald JE, Scolyer RA, Hess KR, et al. Melanoma staging: evidence-based changes in the American Joint Committee on Cancer eighth edition cancer staging manual. CA Cancer J Clin. 2017;67(6):472-492. https://doi.org/10.3322/caac.21409

7. Que SKT, Zwald FO, Schmults CD. Cutaneous squamous cell carcinoma: incidence, risk factors, diagnosis, and staging. J Am Acad Dermatol. 2018;78(2):237-247. https://doi.org/10.1016/j.jaad.2017.08.059

8. Whitman ED, Koshenkov VP, Gastman BR, et al. Integrating 31-gene expression profiling with clinicopathologic features to optimize cutaneous melanoma sentinel lymph node metastasis prediction. JCO Precis Oncol. 2021;5:e2100162. https://doi.org/10.1200/PO.21.00162

9. Jarell A, Gastman BR, Dillon LD, et al. Optimizing treatment approaches for patients with cutaneous melanoma by integrating clinical and pathologic features with the 31-gene expression profile test. J Am Acad Dermatol. 2022;87(6):1312-1320. https://doi.org/10.1016/j.jaad.2022.06.1202

10. Guenther JM, Ward A, Martin BJ, et al. A prospective, multicenter analysis of the integrated 31-gene expression profile test for sentinel lymph node biopsy demonstrates reduced number of unnecessary SLNBs in patients with cutaneous melanoma. World J Surg Oncol. 2025;23(1):5. https://doi.org/10.1186/s12957-024-03640-x

11. Guenther JM, Ward A, Martin BJ, et al. A prospective, multicenter analysis of recurrence-free survival after sentinel lymph node biopsy decisions influenced by the 31-GEP. Cancer Med. 2025;14(7):e70839. https://doi.org/10.1002/cam4.70839

12. Beard T, Guenther JM, Leong SP, et al. The integrated 31-gene expression profile test identifies low-risk patients with cutaneous melanoma who can forego the SLNB procedure: results from a prospective, multicenter trial. Future Oncol. 2026;22(8):933-938. https://doi.org/10.1080/14796694.2026.2640227

13. Gerami P, Cook RW, Russell MC, et al. Gene expression profiling for molecular staging of cutaneous melanoma in patients undergoing sentinel lymph node biopsy. J Am Acad Dermatol. 2015;72(5):780-785.e3. https://doi.org/10.1016/j.jaad.2015.01.009

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15. Greenhaw BN, Covington KR, Kurley SJ, et al. Molecular risk prediction in cutaneous melanoma: a meta-analysis of the 31-gene expression profile prognostic test in 1479 patients. J Am Acad Dermatol. 2020;83(3):745-753. https://doi.org/10.1016/j.jaad.2020.03.053

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Umer Nadir, MD

• Research Fellow for Dr. Stan Tolkachjov, MD
• Incoming Dermatology Resident
• Baylor University Medical Center

Dallas, TX

George M. Jeha, MD

• Dermatologist
• Micrographic Surgery and Dermatologic Oncology fellow
• Baylor University Medical Center

Dallas, TX

Stanislav N. Tolkachjov, MD

• Dermatologist and Director of Mohs, Epiphany Dermatology
• Clinical Professor, Texas A&M College of Medicine
• Clinical Assistant Professor of Dermatology, UT Southwestern
• Core Faculty and MSDO Fellowship Director, Baylor University Medical Center

Dallas, TX