Biology subjects

Campbell, J. D.

Publications and source records attributed to Campbell, J. D..

3 recordsLinked to original sources

bioRxiv2018

Molecular Subtyping reveals Immune Alterations associated with Progression of Bronchial Premalignant Lesions

Bronchial premalignant lesions (PMLs) are precursors of lung squamous cell carcinoma, but have variable outcome, and we lack tools to identify and treat PMLs at highest risk for progression to invasive cancer. Profiling endobronchial biopsies of PMLs obtained from high-risk smokers by RNA-Seq identified four PML subtypes with differences in epithelial and immune processes. One molecular subtype (Proliferative) is enriched with dysplastic lesions and exhibits up-regulation of metabolic and cell cycle pathways and down-regulation of ciliary processes. RNA-Seq profiles from normal-appearing uninvolved large airway brushings could identify subjects with Proliferative lesions with high specificity. Expression of interferon signaling and antigen processing/presentation pathways are decreased in progressive/persistent Proliferative lesions and immunofluorescence indicates a depletion of innate and adaptive immune cells in these lesions. Molecular biomarkers measured in PMLs or the uninvolved airway can enhance histopathological grading and suggests that immunoprevention strategies may be effective in intercepting the progression of PMLs to lung cancer.

cancer biology↗

bioRxiv2018

Interactive single cell RNA-Seq analysis with the Single Cell Toolkit (SCTK)

Single cell RNA-sequencing (scRNA-Seq) allows researchers to profile transcriptional activity in individual cells. However, the complex nature of these data and variability in study design and data generation requires sophisticated computational tools and informed analytical decisions. Here, we present the Single Cell Toolkit (SCTK), an interactive scRNA-Seq analysis package that enables users to perform scRNA-Seq analysis interactively using a command-line workflow or a graphical user interface (GUI) written in R/Shiny.

bioinformatics↗

bioRxiv2017

Expanding discovery from cancer genomes by integrating protein network analyses with in vivo tumorigenesis assays

Approaches that integrate molecular network information and tumor genome data could complement gene-based statistical tests to identify likely new cancer genes, but are challenging to validate at scale and their predictive value remains unclear. We developed a robust statistic (NetSig) that integrates protein interaction networks and data from 4,742 tumor exomes and used it to accurately classify known driver genes in 60% of tested tumor types and to predict 62 new candidates. We designed a quantitative experimental framework to compare the in vivo tumorigenic potential of NetSig candidates, known oncogenes and random genes in mice showing that NetSig candidates induce tumors at rates comparable to known oncogenes and 10-fold higher than random genes. By reanalyzing nine tumor-inducing NetSig candidates in 242 patients with oncogene-negative lung adenocarcinomas, we find that two (AKT2 and TFDP2) are significantly amplified. Overall, we illustrate a scalable integrated computational and experimental workflow to expand discovery from cancer genomes.

cancer biology↗