Current and future perspectives of liquid biopsies in genomics-driven oncology

By E. Heitzer, I. Haque, Charles E. S. Roberts, Michael R. Speicher. Nature reviews genetics, 2018

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From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment. (2023, background): “8 This is particularly important as cancer therapeutics become increasingly targeted and mechanistic.”
Liquid Biopsy: From Discovery to Clinical Application. (2021, methodology): “, array comparative genomic hybridization, whole-genome sequencing, or exome sequencing) aimed to screen the entire genome (42).”
How Machine Learning will Transform Biomedicine (2020, neutral): “Some examples from cancer biology include using DNA methylation (Kang et al., 2017) and nucleosome positioning (Heitzer et al., 2019) from blood to predict tumor tissue of origin, quantifying cellular pathway activation levels in biopsies and other tissue samples (Way and Greene, 2019; Way et al.,…”
Molecular profiling for precision cancer therapies (2020, background): “for the detection and analysis of these biomarkers and their clinical utility remains unclear [50].”
Multimodal biomedical AI (2022, methodology): “Although synthetic control trials are still relatively novel 43 , the FDA has already approved medications based on historical controls 44 and has developed a framework for the utilization of real-world evidence 45 .”