TY - JOUR
T1 - Circulating tumor DNA reveals complex biological features with clinical relevance in metastatic breast cancer
AU - Prat, Aleix
AU - Brasó-Maristany, Fara
AU - Martínez-Sáez, Olga
AU - Sanfeliu, Esther
AU - Xia, Youli
AU - Bellet, Meritxell
AU - Galván, Patricia
AU - Martínez, Débora
AU - Pascual, Tomás
AU - Marín-Aguilera, Mercedes
AU - Rodríguez, Anna
AU - Chic, Nuria
AU - Adamo, Barbara
AU - Paré, Laia
AU - Vidal, Maria
AU - Margelí, Mireia
AU - Ballana, Ester
AU - Gómez-Rey, Marina
AU - Oliveira, Mafalda
AU - Felip, Eudald
AU - Matito, Judit
AU - Sánchez-Bayona, Rodrigo
AU - Suñol, Anna
AU - Saura, Cristina
AU - Ciruelos, Eva
AU - Tolosa, Pablo
AU - Muñoz, Montserrat
AU - González-Farré, Blanca
AU - Villagrasa, Patricia
AU - Parker, Joel S
AU - Perou, Charles M
AU - Vivancos, Ana
N1 - © 2023. The Author(s).
Publisher Copyright:
© 2023, The Author(s).
PY - 2023/3/1
Y1 - 2023/3/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85149407213&partnerID=8YFLogxK
UR - http://www.ncbi.nlm.nih.gov/pubmed/36859416
http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC9977734
UR - https://www.mendeley.com/catalogue/ed38a5ad-d2a8-3cb8-afca-dbe64dcdc609/
UR - https://portalrecerca.uab.cat/en/publications/c51b5200-7534-4c1c-a565-8104de435047
U2 - https://doi.org/10.1038/s41467-023-36801-9
DO - https://doi.org/10.1038/s41467-023-36801-9
M3 - Article
C2 - 36859416
VL - 14
M1 - 1157
ER -
