Gene expression profiling
Another aspect of AI is gene expression profiling (GEP), which Dr. Patel defined as the evaluation of frequency and intensity of genetic activity at once to create a global picture of cellular function. “It’s AI that uses machine learning to evaluate genetic expression to assess lesion behavior,” he explained.
One GEP test on the market is the Pigmented Lesion Assay (PLA) from DermTech, a noninvasive test that looks at the expression of two genes to predict if a lesion is malignant or not. “Based on their validation set, they have shown some impressive numbers,” with sensitivities above 90%, and published registry data that have shown higher sensitivities “and even specificities above 90%,” he said.
“On the surface, it looks like this would be a useful test,” Dr. Patel said. A study published in 2021 looked at the evidence of applying real-world evidence with this test to see if results held up. Based on the authors’ analysis, he noted, “you would need a sensitivity and specificity of 95% to yield a positivity rate of 9.5% for the PLA test, which is what has been reported in real-world use. So, there’s a disconnect somewhere and we are not quite there yet.” That may be a result of the dataset itself not being as uniform between the validation and the training datasets, he continued. Also, the expression of certain genes is different “if you don’t have a clean input variable” of what the test is being used for, he added.
“If you’re not mirroring the dataset, you’re not going to get clean data,” he said. “So, if you’re using this on younger patients or for sun-damaged lesional skin or nonmelanocytic lesions around sun-damaged areas, there are variable expressions that may not be accurately captured by that algorithm. This might help explain the real-world variation that we’re seeing.”
Another GEP test in use is the 31-Gene Expression Profile Test for Melanoma, which evaluates gene expressions in melanoma tumors and what the behavior of that tumor may be. The test has been available for more than a decade “and there is a lot of speculation about its use,” Dr. Patel said. “A recent paper attempted to come up with an algorithm of how to use this, but there’s a lot of concern about the endpoints of what changes in management might result from this test. That is what we need to be thinking about. There’s a lot of back and forth about this.”
In 2020, authors of a consensus statement on prognostic GEP in cutaneous melanoma concluded that before GEP testing is routinely used, the clinical benefit in the management of patients with melanoma should be established through further clinical investigation. Dr. Patel recommended the accompanying editorial on GEP in melanoma, written by Hensin Tsao, MD, PhD, and Warren H. Chan, MS, in JAMA Dermatology.
In Dr. Patel’s opinion, T1a melanomas (0.8 mm, nonulcerated) do not need routine GEP, but the GEP test may be useful in cases that are in the “gray zone,” such as those with T1b or some borderline T2a melanomas (> 0.8 mm, < 1.2mm, nonulcerated, but with high mitosis, etc.); patients with unique coexisting conditions such as pregnancy, and patients who may not tolerate sentinel lymph node biopsy (SLNB) or adjuvant therapy.
Echoing sentiments expressed in the JAMA Dermatology editorial, he advised dermatologists to “remember your training and know the data. GEP predicting survival is not the same as SLNB positive rate. GEP should not replace standard guidelines in T2a and higher melanomas. Nodal sampling remains part of all major guidelines and determines adjuvant therapy.”
He cited the characterization of GEP in the editorial as “a powerful technology” that heralds the age of personalized medicine, but it is not ready for ubiquitous use. Prospective studies and time will lead to highly accurate tools.”
Dr. Patel disclosed that he is chief medical officer for Lazarus AI.