Risk Information Can Influence Clinical Decision-Making in Melanoma

Cutaneous melanoma is disproportionately lethal among skin cancers, and as incidence rates of cutaneous melanoma continue to rise in the United States, so does the importance of managing melanoma cases in alignment with personalized prognoses.

Cutaneous melanoma is disproportionately lethal among skin cancers1, and as incidence rates of cutaneous melanoma continue to rise in the United States2, so does the importance of managing melanoma cases in alignment with personalized prognoses.

On a per-patient basis, melanoma management—even when informed by national staging and management guidelines—can be conflicting with patients’ actual prognoses, which in the context of melanoma, refers to the patient’s risk of experiencing metastasis or recurrence. This incongruence can result in either under management of the disease with a potential impact on patient outcomes, or over-management at the expense of efficient resource allocation. There is a clinical need for healthcare providers to assess individual prognoses accurately, in order to make the most informed treatment plan decisions.

To evaluate a melanoma patient’s prognosis, clinicians utilize current cancer staging methods which consider clinical and histopathological features (thickness, ulceration presence or absence, microsatellite presence or absence, and spread to regional lymph nodes3 in each patient presenting with a melanoma diagnosis. The resulting presumed prognosis, in turn, informs each patient’s course of management: a combination of imaging, labs, surveillance, and referrals to other relevant specialists.

While these features in aggregate serve as valuable indicators of risk, adding the tumor molecular biology can provide valuable, additional information. For example, some melanoma diagnoses indicate a sentinel lymph node biopsy procedure to assess one of those risk indicators: whether a melanoma has spread to a regional lymph node, which is thought to correlate with continued spread. However, the biopsy procedure is not without its own risks, and is a poor predictor of deaths due to melanoma, according to a study in which two-thirds of melanoma deaths were in patients who had tested negative for sentinel lymph node spread.4

Understanding each patient’s true risk level is critical to optimizing patient outcomes in melanoma, and making accurate prognoses requires input from all available features that carry high predictive value. Melanoma prognoses are currently able to benefit from the incorporation of another feature with high predictive value—the tumors’ gene expression signatures, or tumor molecular biology.

Research has identified several sets of genes whose differential expression levels are highly and independently predictive of aspects of the course of disease in several cancers. Such research has led in recent years to the development of highly accurate diagnostic and prognostic tests that can augment traditionally used risk indicators to guide cancer management in certain subsets of cancer patients—that is, validated gene expression profiles (GEPs) can contribute information about tumor biology that cannot be gathered from any other available risk indicators. GEP tests’ intended uses to assess the likelihood of various outcomes differ by indication and from test to test: results may predict response to certain therapies, predict overall survival rate, or predict other rates related to risk.5

In the case of melanoma prognoses, a 31-GEP test has been developed for use in stratifying individual patients’ risk of recurrence or metastasis in stage I, II, and III melanoma cases. The test measures the expression levels of a validated panel of 31 genes (thus its designation of “31-GEP”) based on RNA isolated from formalin-fixed, paraffin-embedded tissue samples obtained from biopsy or excision of the primary tumor. The 31 genes that the panel comprises were chosen based on their exhibiting significant differences in expression between metastatic and nonmetastatic melanomas in samples of known status.6 The test’s results stratify melanoma samples into one of three categories: low, increased, or highest risk of recurrence and/or metastasis within five years. That result, when incorporated with risk information from clinical and histopathological factors, is intended to refine the prognosis afforded by using those factors alone—not to replace them—and to help inform risk-appropriate, personalized management.

To date, the 31-GEP has in fact justified a role for gene expression profiling in melanoma, based on numerous studies demonstrating consistency in test performance and clinical use. A recent meta-analysis concluded that the 31-GEP test serves as a significant, independent predictor of metastatic and/or recurrence risk. Additionally, in those who would otherwise been classified as having a low risk of metastasis, the test reproducibly identifies melanoma patients at increased metastatic risk, based on their tumor biology, thereby indicating patients who may benefit from increased clinical surveillance as a part of their management plans.7 In clinical practice, use of the test also influenced physicians to adjust their melanoma patients’ management plans about 50% of the time, while still adhering to national management guidelines.8-11 The test has also informed physicians’ discussions with patients regarding sentinel lymph node biopsy procedures and whether patients may safely forgo the procedure, potentially reducing the need for the procedure by up to 74%.12

As melanoma cases rise, healthcare providers who manage melanoma have the means, through 31-GEP testing, to assess necessary prognostic information that can significantly contribute to improved alignment between an individual patient’s risk of recurrence/metastasis and their treatment plan, which stands to better serve individual patients as well as preserve healthcare resource allocation at large.

References:

1. American Cancer Society. Cancer Facts & Figures 2021. Atlanta: American Cancer Society; 2021.

2. Annual report to the Nation 2021: National cancer statistics. SEER. https://seer.cancer.gov/report_to_nation/statistics.html#new. Accessed July 14, 2021.

3. Keung EZ, Gershenwald JE. The eighth edition American Joint Committee on Cancer (AJCC) melanoma staging system: Implications for melanoma treatment and care. Expert Review of Anticancer Therapy. 2018;18(8):775-784. doi:10.1080/14737140.2018.1489246

4. Morton DL, Thompson JF, Cochran AJ, et al. Final trial report of sentinel-node biopsy versus nodal observation in melanoma. N Eng J Med. 2014;370(7):599-609. doi:10.1056/nejmoa1310460

5. El‐Deiry WS, Goldberg RM, Lenz HJ, et al. The current state of molecular testing in the treatment of patients with solid tumors, 2019. CA: A Cancer Journal for Clinicians. 2019. doi:10.3322/caac.21560

6. Farberg AS, Glazer AM, Winkelmann RR, Rigel DS. Assessing genetic expression profiles in melanoma prognosis. Dermatologic Clinics. 2017;35(4):545-550. doi:10.1016/j.det.2017.06.017

7. 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 1,479 patients. Journal of the American Academy of Dermatology. 2020;83(3):745-753. doi:10.1016/j.jaad.2020.03.053

8. Berger AC, Davidson RS, Poitras JK, et al. Clinical impact of A 31-gene expression profile test cor cutaneous melanoma in 156 prospectively and consecutively tested patients. Current Medical Research and Opinion. 2016;32(9):1599-1604. doi:10.1080/03007995.2016.1192997

9. Dillon LD, Gadzia JE, Davidson RS, et al. Prospective, multicenter clinical IMPACT evaluation of a 31-gene expression profile test for management of melanoma patients. SKIN The Journal of Cutaneous Medicine. 2018;2(2):111-121. doi:10.25251/skin.2.2.3

10. Farberg AS, Glazer AM, White R, Rigel DS. Impact of a 31-gene Expression Profiling Test for Cutaneous Melanoma on Dermatologists' Clinical Management Decisions. J Drugs Dermatol. 2017;16(5):428-431.

11. Schuitevoerder D, Heath M, Cook RW, et al. Impact of Gene Expression Profiling on Decision-Making in Clinically Node Negative Melanoma Patients after Surgical Staging. J Drugs Dermatol. 2018;17(2):196-199.

12. Vetto JT, Hsueh EC, Gastman BR, et al. Guidance of sentinel lymph node biopsy decisions in patients with T1–T2 melanoma using gene expression profiling. Future Oncology. 2019;15(11):1207-1217. doi:10.2217/fon-2018-0912