Circulating tumor DNA reveals complex biological features with clinical relevance in metastatic breast cancer

Aleix Prat, Fara Brasó-Maristany, Olga Martínez-Sáez, Esther Sanfeliu, Youli Xia, Meritxell Bellet, Patricia Galván, Débora Martínez, Tomás Pascual, Mercedes Marín-Aguilera, Anna Rodríguez, Nuria Chic, Barbara Adamo, Laia Paré, Maria Vidal, Mireia Margelí, Ester Ballana, Marina Gómez-Rey, Mafalda Oliveira, Eudald FelipJudit Matito, Rodrigo Sánchez-Bayona, Anna Suñol, Cristina Saura, Eva Ciruelos, Pablo Tolosa, Montserrat Muñoz, Blanca González-Farré, Patricia Villagrasa, Joel S Parker, Charles M Perou, Ana Vivancos

Research output: Contribution to journalArticleResearchpeer-review

16 Citations (Scopus)

Abstract

Liquid biopsy has proven valuable in identifying individual genetic alterations; however, the ability of plasma ctDNA to capture complex tumor phenotypes with clinical value is unknown. To address this question, we have performed 0.5X shallow whole-genome sequencing in plasma from 459 patients with metastatic breast cancer, including 245 patients treated with endocrine therapy and a CDK4/6 inhibitor (ET + CDK4/6i) from 2 independent cohorts. We demonstrate that machine learning multi-gene signatures, obtained from ctDNA, identify complex biological features, including measures of tumor proliferation and estrogen receptor signaling, similar to what is accomplished using direct tumor tissue DNA or RNA profiling. More importantly, 4 DNA-based subtypes, and a ctDNA-based genomic signature tracking retinoblastoma loss-of-heterozygosity, are significantly associated with poor response and survival outcome following ET + CDK4/6i, independently of plasma tumor fraction. Our approach opens opportunities for the discovery of additional multi-feature genomic predictors coming from ctDNA in breast cancer and other cancer-types.

Original languageEnglish
Article number1157
Number of pages16
JournalNature communications
Volume14
DOIs
Publication statusE-pub ahead of print - 1 Mar 2023

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