NCT05856565: HELIOPREDICT

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

Recruiting now Last updated 15 April 2026

What this trial tests

trial testing Photo in Metastatic Melanoma in 700 participants. Currently enrolling.

Timeline

24 July 2023

Primary endpoint
24 July 2028

24 July 2028

Quick facts

Lead sponsor	Nantes University Hospital
Status	Recruiting now
Study type	OBSERVATIONAL
Enrollment	700
Start date	24 July 2023
Primary completion	24 July 2028
Estimated completion	24 July 2028
Sites	20 locations across France

Drugs / interventions tested

• Photo

Conditions studied

• Metastatic Melanoma — all drugs for Metastatic Melanoma →

Sponsor

Nantes University Hospital

Who can join

18 and older, any sex, with Metastatic Melanoma. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

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.

Publications & conference data

No peer-reviewed publications indexed yet for this trial.