REFLECTions on Combination Therapies Empowered by Data Sharing

Article Oncology

Precision Combination Therapies Based on Recurrent Oncogenic Coalterations

Xubin Li et al.

Summary: The study presents a comprehensive resource of precision combination therapies tailored to oncogenic coalterations. The resource is generated using the REFLECT method, which integrates machine learning and cancer informatics algorithms. Validations show that REFLECT-selected therapies have significantly improved efficacy and patient survival outcomes.

CANCER DISCOVERY (2022)

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Article Genetics & Heredity

Predicting heterogeneity in clone-specific therapeutic vulnerabilities using single-cell transcriptomic signatures

Chayaporn Suphavilai et al.

Summary: This study emphasizes the importance of intra-tumor transcriptomic heterogeneity (ITTH) in predicting clinical outcomes and drug responses. By leveraging single-cell RNA-seq and a recommender system, accurate predictions of drug response and vulnerability targeting can be achieved, accelerating drug repurposing studies.

GENOME MEDICINE (2021)

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Article Pathology

Clinical Validation of Whole Genome Sequencing for Cancer Diagnostics

Paul Roepman et al.

Summary: Whole genome sequencing (WGS) in cancer diagnosis demonstrates high sensitivity and precision, reliably detecting various mutation types and serving as the most comprehensive genetic testing method.

JOURNAL OF MOLECULAR DIAGNOSTICS (2021)

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Editorial Material Biochemical Research Methods

Reproducibility standards for machine learning in the life sciences

Benjamin J. Heil et al.

Summary: By adhering to standards based on data, model and code publication, programming best practices and workflow automation, researchers in the life sciences can enhance the computational reproducibility of machine-learning analyses and ensure their trustworthiness.

NATURE METHODS (2021)

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Article Genetics & Heredity