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Identifying the Transcriptional Drivers of Metastasis Embedded within Localized Melanoma

Lookup NU author(s): Dr Jérémie Nsengimana

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).

Abstract

©2022 The Authors; Published by the American Association for Cancer Research. In melanoma, predicting which tumors will ultimately metastasize guides treatment decisions. Transcriptional signatures of primary tumors have been utilized to predict metastasis, but which among these are driver or passenger events remains unclear. We used data from the adjuvant AVAST-M trial to identify a predictive gene signature in localized tumors that ultimately metastasized. Using a zebrafish model of primary melanoma, we interrogated the top genes from the AVAST-M signature in vivo. This identified GRAMD1B, a cholesterol transfer protein, as a bona fide metastasis suppressor, with a majority of knockout animals rapidly developing metastasis. Mechanistically, excess free cholesterol or its metabolite 27-hydroxycholesterol promotes invasiveness via activation of an AP-1 program, which is associated with increased metastasis in humans. Our data demonstrate that the transcriptional seeds of metastasis are embedded within localized tumors, suggesting that early targeting of these programs can be used to prevent metastatic relapse. SIGNIFICANCE: We analyzed human melanoma transcriptomics data to identify a gene signature predictive of metastasis. To rapidly test clinical signatures, we built a genetic metastasis platform in adult zebrafish and identified GRAMD1B as a suppressor of melanoma metastasis. GRAMD1B-associated cholesterol overload activates an AP-1 program to promote melanoma invasion. This article is highlighted in the In This Issue feature, p. 1.

Publication metadata

Author(s): Suresh S, Rabbie R, Garg M, Lumaquin D, Huang T-H, Montal E, Ma Y, Cruz NM, Tang X, Nsengimana J, Newton-Bishop J, Hunter MV, Zhu Y, Chen K, de Stanchina E, Adams DJ, White RM

Publication type: Article

Publication status: Published

Journal: Cancer Discovery

Year: 2023

Volume: 13

Issue: 1

Pages: 194-215

Print publication date: 01/01/2023

Online publication date: 09/01/2023

Acceptance date: 14/10/2022

Date deposited: 23/01/2023

ISSN (print): 2159-8274

ISSN (electronic): 2159-8290

Publisher: American Association for Cancer Research

URL: https://doi.org/10.1158/2159-8290.CD-22-0427

DOI: 10.1158/2159-8290.CD-22-0427

PubMed id: 36259947

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