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.

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.

A clinical-grade liquid biomarker detects neuroendocrine differentiation in prostate cancer.

BackgroundNeuroendocrine prostate cancer (NEPC) is an aggressive subtype, the presence of which changes the prognosis and management of metastatic prostate cancer.MethodsWe performed analytical validation of a Circulating Tumor Cell (CTC) multiplex RNA qPCR assay to identify the limit of quantification (LOQ) in cell lines, synthetic cDNA, and patient samples. We next profiled 116 longitudinal samples from a prospectively collected institutional cohort of 17 patients with metastatic prostate cancer (7 NEPC, 10 adenocarcinoma) as well as 265 samples from 139 patients enrolled in 3 adenocarcinoma phase II trials of androgen receptor signaling inhibitors (ARSIs).

Identification of phenocopies improves prediction of targeted therapy response over DNA mutations alone.

DNA mutations in specific genes can confer preferential benefit from drugs targeting those genes. However, other molecular perturbations can "phenocopy" pathogenic mutations, but would not be identified using standard clinical sequencing, leading to missed opportunities for other patients to benefit from targeted treatments. We hypothesized that RNA phenocopy signatures of key cancer driver gene mutations could improve our ability to predict response to targeted therapies, despite not being directly trained on drug response.

Prognosis Associated With Luminal and Basal Subtypes of Metastatic Prostate Cancer.

Importance: Luminal and basal subtypes of primary prostate cancer have been shown to be molecularly distinct and clinically important in predicting response to therapy. These subtypes have not been described in metastatic prostate cancer.

Objectives: To identify clinical and molecular correlates of luminal and basal subtypes in metastatic castration-resistant prostate cancer (mCRPC) and investigate differences in survival, particularly after treatment with androgen-signaling inhibitors (ASIs).