An integrated approach to biomarker discovery reveals gene signatures highly predictive of cancer progression.

Current cancer biomarkers present variability in their predictive power and demonstrate limited clinical efficacy, possibly due to the lack of functional relevance of biomarker genes to cancer progression. To address this challenge, a biomarker discovery pipeline was developed to integrate gene expression profiles from The Cancer Genome Atlas and essential survival gene datasets from The Cancer Dependency Map, the latter of which catalogs genes driving cancer progression. By applying this pipeline to lung adenocarcinoma, lung squamous cell carcinoma, and glioblastoma, genes highly associated with cancer progression were identified and designated as progression gene signatures (PGSs).

DNA sequencing of anatomy lab cadavers to provide hands-on precision medicine introduction to medical students.

Background: Medical treatment informed by Precision Medicine is becoming a standard practice for many diseases, and patients are curious about the consequences of genomic variants in their genome. However, most medical students' understanding of Precision Medicine derives from classroom lectures. This format does little to foster an understanding for the potential and limitations of Precision Medicine.

Abundance of ethnically biased microsatellites in human gene regions.

Microsatellites-a type of short tandem repeat (STR)-have been used for decades as putatively neutral markers to study the genetic structure of diverse human populations. However, recent studies have demonstrated that some microsatellites contribute to gene expression, cis heritability, and phenotype. As a corollary, some microsatellites may contribute to differential gene expression and RNA/protein structure stability in distinct human populations.

Author Correction: Differentiating between cancer and normal tissue samples using multi-hit combinations of genetic mutations.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Estimating the number of genetic mutations (hits) required for carcinogenesis based on the distribution of somatic mutations.

Individual instances of cancer are primarily a result of a combination of a small number of genetic mutations (hits). Knowing the number of such mutations is a prerequisite for identifying specific combinations of carcinogenic mutations and understanding the etiology of cancer. We present a mathematical model for estimating the number of hits based on the distribution of somatic mutations.

Differentiating between cancer and normal tissue samples using multi-hit combinations of genetic mutations.

Cancer is known to result from a combination of a small number of genetic defects. However, the specific combinations of mutations responsible for the vast majority of cancers have not been identified. Current computational approaches focus on identifying driver genes and mutations.

Functional Blockade of Small GTPase RAN Inhibits Glioblastoma Cell Viability.

Glioblastoma, the most common malignant tumor in the brain, lacks effective treatments and is currently incurable. To identify novel drug targets for this deadly cancer, the publicly available results of RNA interference screens from the Project Achilles database were analyzed. Ten candidate genes were identified as survival genes in 15 glioblastoma cell lines.

CAGm: a repository of germline microsatellite variations in the 1000 genomes project.

The human genome harbors an abundance of repetitive DNA; however, its function continues to be debated. Microsatellites-a class of short tandem repeat-are established as an important source of genetic variation. Array length variants are common among microsatellites and affect gene expression; but, efforts to understand the role and diversity of microsatellite variation has been hampered by several challenges.

Casein Kinase 1 Epsilon Regulates Glioblastoma Cell Survival.

Glioblastoma is the most common malignant brain cancer with a dismal prognosis. The difficulty in treating glioblastoma is largely attributed to the lack of effective therapeutic targets. In our previous work, we identified casein kinase 1 ε (CK1ε, also known as CSNK1E) as a potential survival factor in glioblastoma.

Whole-exome sequencing reveals microsatellite DNA markers for response to dofetilide initiation in patients with persistent atrial fibrillation: A pilot study.

Background: Dofetilide is a class III antiarrhythmic drug effective for the treatment of atrial fibrillation (AF). Dofetilide initiation (DI) associates with corrected QT interval (QTc) prolongation. Significant QTc prolongation during DI mandates dose adjustment or discontinuation of the drug.

The Role of Class IA Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunits in Glioblastoma.

Phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K) plays a critical role in the pathogenesis of cancer including glioblastoma, the most common and aggressive form of brain cancer. Targeting the PI3K pathway to treat glioblastoma has been tested in the clinic with modest effect. In light of the recent finding that PI3K catalytic subunits (PIK3CA/p110α, PIK3CB/p110β, PIK3CD/p110δ, and PIK3CG/p110γ) are not functionally redundant, it is imperative to determine whether these subunits play divergent roles in glioblastoma and whether selectively targeting PI3K catalytic subunits represents a novel and effective strategy to tackle PI3K signaling.

as a Risk and Mortality Marker for Breast Cancer in African American Women.

African American woman are 43% more likely to die from breast cancer than white women and have increased the risk of tumor recurrence despite lower incidence. We investigate variations in microsatellite genomic regions-a type of repetitive DNA-and possible links to the breast cancer mortality gap. We screen 33 854 microsatellites in germline DNA of African American women with and without breast cancer: 4 are statistically significant.

PIK3CB/p110β is a selective survival factor for glioblastoma.

Background: Glioblastoma (GBM) is difficult to treat. Phosphoinositide 3-kinase (PI3K) is an attractive therapeutic target for GBM; however, targeting this pathway to effectively treat GBM is not successful because the roles of PI3K isoforms remain to be defined. The aim of this study is to determine whether PIK3CB/p110β, but not other PI3K isoforms, is a biomarker for GBM recurrence and important for cell survival.

Survival kinase genes present prognostic significance in glioblastoma.

Cancer biomarkers with a strong predictive power for diagnosis/prognosis and a potential to be therapeutic targets have not yet been fully established. Here we employed a loss-of-function screen in glioblastoma (GBM), an infiltrative brain tumor with a dismal prognosis, and identified 20 survival kinase genes (SKGs). Survival analyses using The Cancer Genome Atlas (TCGA) datasets revealed that the expression of CDCP1, CDKL5, CSNK1E, IRAK3, LATS2, PRKAA1, STK3, TBRG4, and ULK4 stratified GBM prognosis with or without temozolomide (TMZ) treatment as a covariate.

Connexin 43 Inhibition Sensitizes Chemoresistant Glioblastoma Cells to Temozolomide.

Resistance of glioblastoma (GBM) to the front-line chemotherapeutic agent temozolomide (TMZ) continues to challenge GBM treatment efforts. The repair of TMZ-induced DNA damage by O-6-methylguanine-DNA methyltransferase (MGMT) confers one mechanism of TMZ resistance. Paradoxically, MGMT-deficient GBM patients survive longer despite still developing resistance to TMZ.

Deep resequencing reveals excess rare recent variants consistent with explosive population growth.

Accurately determining the distribution of rare variants is an important goal of human genetics, but resequencing of a sample large enough for this purpose has been unfeasible until now. Here, we applied Sanger sequencing of genomic PCR amplicons to resequence the diabetes-associated genes KCNJ11 and HHEX in 13,715 people (10,422 European Americans and 3,293 African Americans) and validated amplicons potentially harbouring rare variants using 454 pyrosequencing. We observed far more variation (expected variant-site count ∼578) than would have been predicted on the basis of earlier surveys, which could only capture the distribution of common variants.