A phenocopy signature of TP53 loss predicts response to chemotherapy.

In preclinical studies, p53 loss of function impacts chemotherapy response, but this has not been consistently validated clinically. We trained a TP53-loss phenocopy gene expression signature from pan-cancer clinical samples in the TCGA. In vitro, the TP53-loss phenocopy signature predicted chemotherapy response across cancer types.

Clinical cell-surface targets in metastatic and primary solid cancers.

Therapies against cell-surface targets (CSTs) represent an emerging treatment class in solid malignancies. However, high-throughput investigations of CST expression across cancer types have been reliant on data sets of mostly primary tumors, despite therapeutic use most commonly in metastatic disease. We identified a total of 818 clinical trials of CST therapies with 78 CSTs.

A platform-independent AI tumor lineage and site (ATLAS) classifier.

Histopathologic diagnosis and classification of cancer plays a critical role in guiding treatment. Advances in next-generation sequencing have ushered in new complementary molecular frameworks. However, existing approaches do not independently assess both site-of-origin (e.

Machine Learning & Molecular Radiation Tumor Biomarkers.

Developing radiation tumor biomarkers that can guide personalized radiotherapy clinical decision making is a critical goal in the effort towards precision cancer medicine. High-throughput molecular assays paired with modern computational techniques have the potential to identify individual tumor-specific signatures and create tools that can help understand heterogenous patient outcomes in response to radiotherapy, allowing clinicians to fully benefit from the technological advances in molecular profiling and computational biology including machine learning. However, the increasingly complex nature of the data generated from high-throughput and "omics" assays require careful selection of analytical strategies.