In Nature communications ; h5-index 260.0
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.
Prat Aleix, Brasó-Maristany Fara, Martínez-Sáez Olga, Sanfeliu Esther, Xia Youli, Bellet Meritxell, Galván Patricia, Martínez Débora, Pascual Tomás, Marín-Aguilera Mercedes, Rodríguez Anna, Chic Nuria, Adamo Barbara, Paré Laia, Vidal Maria, Margelí Mireia, Ballana Ester, Gómez-Rey Marina, Oliveira Mafalda, Felip Eudald, Matito Judit, Sánchez-Bayona Rodrigo, Suñol Anna, Saura Cristina, Ciruelos Eva, Tolosa Pablo, Muñoz Montserrat, González-Farré Blanca, Villagrasa Patricia, Parker Joel S, Perou Charles M, Vivancos Ana
2023-Mar-01
