# Micrometastasis-derived models enable drug testing for early-stage, high-risk melanoma patients

## TL;DR

This study shows that models made from early-stage melanoma cells can help find better treatments before the cancer relapses.

## Contribution

The study introduces a method to generate preclinical models from early-stage melanoma cells for timely drug testing.

## Key findings

- Models were successfully generated in over 80% of cases before relapse and always before death.
- Drug screening identified new therapeutic candidates and revealed T-cell responses to immunotherapy.
- Model success can be predicted based on the number of melanoma cells in lymph nodes.

## Abstract

Relapse in melanoma after targeted or immune therapy necessitates the rapid identification of effective alternatives. To address this gap, we investigated whether the timely generation of preclinical models for functional drug testing could reveal additional therapeutic options. Our study focused on: (i) the feasibility of generating in vivo and in vitro models from melanoma lymph node (LN)-derived disseminated cancer cells (DCCs) before relapse, (ii) the implementation of preclinical models to identify therapeutic alternatives, and (iii) the ability to detect patients who could benefit from early functional in vitro drug testing. Successful model generation was significantly associated with DCC quantity, LN origin, and mortality risk. All patient-derived xenograft models were available before patient death and, in 82% of cases, before relapse. Proof-of-concept in vitro drug screening using 315 anti-cancer drugs identified additional candidates, and coculture of DCCs and LN cells revealed specific T-cell activation and responses to immunotherapy. Our data establish a process for selecting melanoma patients at high risk of progression, enabling the timely generation of patient-derived models to support functionally guided treatment decisions at relapse.

Melanoma patients presenting at diagnosis without metastasis have a variable risk of relapse and progression. Current clinical regimens applied after relapse often fall short of cure as they are mostly based on trial and error.

In many cases cell models can be established from macroscopically detectable and undetectable lymph node metastasis.Successful model generation can be predicted from the number of melanoma cells per million lymph node cells.In >80% of successful cases models will be available prior to relapse and in all cases prior to death.Sufficient melanoma cell numbers can be obtained to perform drug screens and characterize DCC and immune cell interaction.

In many cases cell models can be established from macroscopically detectable and undetectable lymph node metastasis.

Successful model generation can be predicted from the number of melanoma cells per million lymph node cells.

In >80% of successful cases models will be available prior to relapse and in all cases prior to death.

Sufficient melanoma cell numbers can be obtained to perform drug screens and characterize DCC and immune cell interaction.

Melanoma patients presenting at diagnosis without metastasis have a variable risk of relapse and progression. Current clinical regimens applied after relapse often fall short of cure as they are mostly based on trial and error.