Detection of breast cancer stem cell gene mutations in circulating free DNA during the evolution of metastases.

PURPOSE: Limited knowledge exists on the detection of breast cancer stem cell (BCSC)-related mutations in circulating free DNA (cfDNA) from patients with advanced cancers. Identification of new cancer biomarkers may allow for earlier detection of disease progression and treatment strategy modifications. METHODS: We conducted a prospective study to determine the feasibility and prognostic utility of droplet digital polymerase chain reaction (ddPCR)-based BCSC gene mutation analysis of cfDNA in patients with breast cancer. RESULTS: Detection of quantitative BCSC gene mutation in cfDNA by ddPCR mirrors disease progression and thus may represent a valuable and cost-effective measure of tumor burden. We have previously shown that hematological and neurological expressed 1-like (HN1L), ribosomal protein L39 (RPL39), and myeloid leukemia factor 2 (MLF2) are novel targets for BCSC self-renewal, and targeting these genetic alterations could be useful for personalized genomic-based therapy. CONCLUSION: BCSC mutation detection in cfDNA may have important implications for diagnosis, prognosis, and serial monitoring.

Comprehensive Genomic Characterization of Upper Tract Urothelial Carcinoma.

BACKGROUND: Upper urinary tract urothelial cancer (UTUC) may have unique etiologic and genomic factors compared to bladder cancer. OBJECTIVE: To characterize the genomic landscape of UTUC and provide insights into its biology using comprehensive integrated genomic analyses. DESIGN, SETTING, AND PARTICIPANTS: We collected 31 untreated snap-frozen UTUC samples from two institutions and carried out whole-exome sequencing (WES) of DNA, RNA sequencing (RNAseq), and protein analysis. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Adjusting for batch effects, consensus mutation calls from independent pipelines identified DNA mutations, gene expression clusters using unsupervised consensus hierarchical clustering (UCHC), and protein expression levels that were correlated with relevant clinical variables, The Cancer Genome Atlas, and other published data. RESULTS AND LIMITATIONS: WES identified mutations in FGFR3 (74.1%; 92% low-grade, 60% high-grade), KMT2D (44.4%), PIK3CA (25.9%), and TP53 (22.2%). APOBEC and CpG were the most common mutational signatures. UCHC of RNAseq data segregated samples into four molecular subtypes with the following characteristics. Cluster 1: no PIK3CA mutations, nonsmokers, high-grade