Generation of an Artificial Intelligence Algorithm Based on the Analysis of Melanoma Peri-scar Dermatoheliosis, as a Predictive Factor of Response to Anti-PD-1

Study Overview
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Brief Summary
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In the last decade, the advent of immunotherapies with inhibitors of immune checkpoints, such as anti-PD-1 and anti-CTLA-4, has revolutionized the treatment of advanced or metastatic melanoma. However, the clinical benefit remains limited to a subset of patients. Identifying the patients most likely to benefit from these novel therapies (and avoiding unnecessary toxicity in non-responding patients) is therefore critical. Previous studies found a significant link between the high mutational load of a tumor (TMB) and its response to anti-PD-1 monotherapy, regardless of the histological type of cancer. Unfortunately, TMB measurement is expensive, and requires extensive sequencing approaches difficult to implement in clinical practice. I have shown that melanomas known to be secondary to mutagenic ultraviolet rays (UVR) often carry a high TMB. The cumulative UVR damage translates into visible stigmas termed dermatoheliosis on patients' skin, easy to recognize with the naked eye of the clinician around the scar of the primary melanoma. My project proposes to establish, for the first time, dermatoheliosis as a novel predictive factor of response to anti-PD-1 immunotherapy, to be used within multidisciplinary tumor boards as a powerful decision-support tool to select the best treatment option. Specifically, I will 1) develop, validate and test in a prospective manner, an artificial intelligence (AI)-based algorithm, to assess features of pericicatricial dermatoheliosis based on a collection of photographs obtained from patients with unresectable locally advanced or metastatic melanoma 2) demonstrate the link between dermatoheliosis, TMB, immune and treatment response by characterizing pericicatricial skin single cell transcriptomics, as well as tumor DNA, RNA and host immunological profiles of the patients. This directly accessible, non-invasive, surrogate marker for TMB will be a game changer in clinical practice and will subsequently be translated to other skin cancers.

Official Title
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Generation of an Artificial Intelligence Algorithm Based on the Analysis of Melanoma Peri-scar Dermatoheliosis, as a Predictive Factor of Response to Anti-PD-1

Conditions
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Metastatic Melanoma

Intervention / Treatment
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* Other: Photo


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Adult patients with inoperable stage III or IV melanoma, or inoperable skin carcinoma (cutaneous squamous cell carcinoma or basal cell carcinoma) Retrospective cohort: patients who received systemic treatment for their inoperable skin cancer for at least 3 months, with at least 6 months of follow-up, without immunosuppression and whose site of the primary tumor is not altered by a concomitant dermatosis Prospective cohort: Patients naïve to immunotherapy for the management of their melanoma at the introduction of systemic treatment. Adjuvant immunotherapy tolerated if it has been stopped for at least 6 months before starting the curative treatment Patients who have expressed their agreement to participate in the research and who have signed an image rights authorization Exclusion Criteria: Retrospective cohort: Patients who received their systemic skin cancer treatment for less than 90 days Patients who received adjuvant immunotherapy in the 6 months preceding the curative treatment Patients whose primary skin cancer site cannot be photographed (example of choroidal melanomas, mucosal melanomas except for melanomas with a vulvar or penile starting point, etc.) Patients treated with systemic corticosteroids (dose greater than 10 mg/day) at the introduction of the immunotherapy under consideration Immunocompromised patients (associated blood disease, human immunodeficiency virus infection, transplant patient, etc.) Patients with iatrogenic peri-scarring vitiligo Patients who refused to participate in the research Adults protected by law Pregnant women

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Retrospective<br>Photograph | Other: Photo<br>* Photography intake<br>|
| Prospective<br> | Other: Photo<br>* Photography intake<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Predictive performance of tumor progression score | The predictive score for tumor progression at 6 months will be calculated from the photograph of the excision scar of the patient's primary tumor, as well as the clinical characteristics associated with the prognosis: patient age, sex, phototype, anatomical location and Breslow index of the primary tumour, stage of the skin cancer and WHO performance status at the initiation of the treatment, nature of the treatment administered | after 6 months |