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). Analysis of area under the receiver operating characteristics curve revealed that PGSs predicted patient survival more accurately than previously identified cancer biomarkers. Moreover, PGSs stratified patients with high risk for progressive disease indicated by worse prognostic outcomes, increased frequency of cancer progression, and poor responses to chemotherapy. The robust performance of these PGSs were recapitulated in four independent microarray datasets from Gene Expression Omnibus and were further verified in six freshly dissected tumors from glioblastoma patients. Our results demonstrate the power of an integrated approach to cancer biomarker discovery and the possibility of implementing PGSs into clinical biomarker tests.

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. To close this gap, we implemented a hands-on Precision Medicine training program utilizing exome sequencing to prepare a clinical genetic report of cadavers studied in the anatomy lab. The program reinforces Precision Medicine related learning objectives for the Genetics curriculum. METHODS: Pre-embalmed blood samples and embalmed tissue were obtained from cadavers (donors) used in the anatomy lab. DNA was isolated and sequenced and illustrative genetic reports provided to the students. The reports were used to facilitate discussion with students on the implications of pathogenic genomic variants and the potential correlation of these variants in each "donor" with any anatomical anomalies identified during cadaver dissection. RESULTS: In 75% of cases, analysis of whole exome sequencing data identified a variant associated with increased risk for a disease/abnormal condition noted in the donor's cause of death or in the students' anatomical findings. This provided students with real-world examples of the potential relationship between genomic variants and disease risk. Our students also noted that diseases associated with 92% of the pathogenic variants identified were not related to the anatomical findings, demonstrating the limitations of Precision Medicine. CONCLUSION: With this study, we have established protocols and classroom procedures incorporating hands-on Precision Medicine training in the medical student curriculum and a template for other medical educators interested in enhancing their Precision Medicine training program. The program engaged students in discovering variants that were associated with the pathophysiology of the cadaver they were studying, which led to more exposure and understanding of the potential risks and benefits of genomic 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. To test this hypothesis, we investigate genotype frequencies, functional relevance, and adaptive potential of microsatellites in five super-populations (ethnicities) drawn from the 1000 Genomes Project. We discover 3,984 ethnically-biased microsatellite loci (EBML); for each EBML at least one ethnicity has genotype frequencies statistically different from the remaining four. South Asian, East Asian, European, and American EBML show significant overlap; on the contrary, the set of African EBML is mostly unique. We cross-reference the 3,984 EBML with 2,060 previously identified expression STRs (eSTRs); repeats known to affect gene expression (64 total) are over-represented. The most significant pathway enrichments are those associated with the matrisome: a broad collection of genes encoding the extracellular matrix and its associated proteins. At least 14 of the EBML have established links to human disease. Analysis of the 3,984 EBML with respect to known selective sweep regions in the genome shows that allelic variation in some of them is likely associated with adaptive evolution.

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. The model is fundamentally different from previous approaches, which are based on cancer incidence by age. Our somatic mutation based model is likely to be more robust than age-based models since it does not require knowing or accounting for the highly variable mutation rate, which can vary by over three orders of magnitude. In fact, we find that the number of somatic mutations at diagnosis is weakly correlated with age at cancer diagnosis, most likely due to the extreme variability in mutation rates between individuals. Comparing the distribution of somatic mutations predicted by our model to the actual distribution from 6904 tumor samples we estimate the number of hits required for carcinogenesis for 17 cancer types. We find that different cancer types exhibit distinct somatic mutational profiles corresponding to different numbers of hits. Why might different cancer types require different numbers of hits for carcinogenesis? The answer may provide insight into the unique etiology of different cancer types.

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. Although individually these mutations can increase the risk of cancer they do not result in cancer without additional mutations. We present a fundamentally different approach for identifying the cause of individual instances of cancer: we search for combinations of genes with carcinogenic mutations (multi-hit combinations) instead of individual driver genes or mutations. We developed an algorithm that identified a set of multi-hit combinations that differentiate between tumor and normal tissue samples with 91% sensitivity (95% Confidence Interval (CI) = 89-92%) and 93% specificity (95% CI = 91-94%) on average for seventeen cancer types. We then present an approach based on mutational profile that can be used to distinguish between driver and passenger mutations within these genes. These combinations, with experimental validation, can aid in better diagnosis, provide insights into the etiology of cancer, and provide a rational basis for designing targeted combination therapies.

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. Without adequate depth, both long-read and short-read sequencing may not detect the variants present in a sample; additionally, large sample sizes are needed to reveal the degree of population-level polymorphism. To address these challenges we present the Comparative Analysis of Germline Microsatellites (CAGm): a database of germline microsatellites from 2529 individuals in the 1000 genomes project. A key novelty of CAGm is the ability to aggregate microsatellite variation by population, ethnicity (super population) and gender. The database provides advanced searching for microsatellites embedded in genes and functional elements. All data can be downloaded as Microsoft Excel spreadsheets. Two use-case scenarios are presented to demonstrate its utility: a mononucleotide (A) microsatellite at the BAT-26 locus and a dinucleotide (CA) microsatellite in the coding region of FGFRL1. CAGm is freely available at http://www.cagmdb.org/.

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. However, how CK1ε controls cell survival remains elusive and whether targeting CK1ε is a possible treatment for glioblastoma requires further investigation. Here we report that CK1ε was expressed at the highest level among six CK1 isoforms in glioblastoma and enriched in high-grade glioma, but not glia cells. Depletion of CK1ε remarkably inhibited the growth of glioblastoma cells and suppressed self-renewal of glioblastoma stem cells, while having limited effect on astrocytes. CK1ε deprivation activated ß-catenin and induced apoptosis, which was further counteracted by knockdown of ß-catenin. The CK1ε inhibitor IC261, but not PF-4800567, activated ß-catenin and blocked the growth of glioblastoma cells and glioblastoma stem cells. Congruently, IC261 elicited a robust growth inhibition of human glioblastoma xenografts in mice. Together, our results demonstrate that CK1ε regulates the survival of glioblastoma cells and glioblastoma stem cells through ß-catenin signaling, underscoring the importance of targeting CK1ε as an effective treatment for 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. Microsatellite DNA are novel genetic markers associated with congenital and acquired health conditions. HYPOTHESIS: DNA microsatellite polymorphism may associate with QTc response to dofetilide initiation in patients with persistent AF. METHODS: We performed whole-exome sequencing in a cohort of patients with persistent AF undergoing DI. Electrocardiographic variables and clinical data were assessed. We defined patients as eligible for DI when no significant QTc prolongation (>20% compared with baseline) was seen with a 500-µg dose. We defined patients as ineligible for DI when significant QTc prolongation was seen during DI with 500 µg. We investigated polymorphisms for 11 919 DNA microsatellite loci in relation to QTc response to DI. RESULTS: During the study, 14 consecutive patients with persistent AF presenting for DI were enrolled. Whole-exome sequencing revealed 14 different microsatellite loci in the 2 groups. All genes or proximal genes that harbor these loci are known to have expression in the human heart. Two genes, MYH6 and TRAK2, are known to have expression in the atria. TRAK2 is known to interact with KCNJ2, the inward-rectifier potassium channel 1. CONCLUSIONS: Microsatellite DNA polymorphisms seem to associate with QTc response to DI therapy in patients with persistent AF who are deemed otherwise eligible for dofetilide therapy.

PIK3CB/p110ß 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/p110ß, but not other PI3K isoforms, is a biomarker for GBM recurrence and important for cell survival. Methods: Gene expression and clinical relevance of PI3K genes in GBM patients were analyzed using online databases. Expression/activity of PI3K isoforms was determined using immunoblotting. PI3K genes were inhibited using short hairpin RNAs or isoform-selective inhibitors. Cell viability/growth was assessed by the MTS assay and trypan blue exclusion assay. Apoptosis was monitored using the caspase activity assay. Mouse GBM xenograft models were used to gauge drug efficacy. Results: PIK3CB/p110ß was the only PI3K catalytic isoform that significantly correlated with high incidence rate, risk, and poor survival of recurrent GBM. PIK3CA/p110α, PIK3CB/p110ß, and PIK3CD/p110δ were differentially expressed in GBM cell lines and primary tumor cells derived from patient specimens, whereas PIK3CG/p110γ was barely detected. PIK3CB/p110ß protein levels presented a stronger association with the activities of PI3K signaling than other PI3K isoforms. Blocking p110ß deactivated PI3K signaling, whereas inhibition of other PI3K isoforms had no effect. Specific inhibitors of PIK3CB/p110ß, but not other PI3K isoforms, remarkably suppressed viability and growth of GBM cells and xenograft tumors in mice, with minimal cytotoxic effects on astrocytes. Conclusions: PIK3CB/p110ß is a biomarker for GBM recurrence and selectively important for GBM cell survival.

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. RAN, member RAS oncogene family (RAN) was expressed in glioblastoma at the highest level among all candidates based upon cDNA microarray data. However, Kaplan-Meier survival analysis did not show any correlation between RAN mRNA levels and patient survival. Because RAN is a small GTPase that regulates nuclear transport controlled by karyopherin subunit beta 1 (KPNB1), RAN was further analyzed together with KPNB1. Indeed, GBM patients with high levels of RAN also had more KPNB1 and levels of KPNB1 alone did not relate to patient prognosis. Through a Cox multivariate analysis, GBM patients with high levels of RAN and KPNB1 showed significantly shorter life expectancy when temozolomide and promoter methylation of O6-methylguanine DNA methyltransferase were used as covariates. These results indicate that RAN and KPNB1 together are associated with drug resistance and GBM poor prognosis. Furthermore, the functional blockade of RAN and KPNB1 by importazole remarkably suppressed cell viability and activated apoptosis in GBM cells expressing high levels of RAN, while having a limited effect on astrocytes and GBM cells with undetectable RAN. Together, our results demonstrate that RAN activity is important for GBM survival and the functional blockade of RAN/KPNB1 is an appealing therapeutic approach.

ZDHHC3 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. These are located in the 3' UTR (untranslated region) of gene ZDHHC3, an intron of transcribed pseudogene INTS4L1, an intron of ribosomal gene RNA5-8S5, and an intergenic region of chromosome 16. The marker in ZDHHC3 is interesting for 3 reasons: (a) the ZDHHC3 gene is located in region 3p21 which has already been linked to early invasive breast cancer, (b) the Kaplan-Meier estimator demonstrates that ZDHHC3 alterations are associated with poor breast cancer survival in all racial/ethnic groups combined, and (c) data from cBioPortal suggest that ZDHHC3 messenger RNA expression is significantly lower in African Americans compared with whites. These independent lines of evidence make ZDHHC3 a candidate for further investigation.

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/p110ß, 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. This article summarizes recent advances in understanding the role of PI3K catalytic subunits in glioblastoma and discusses the possibility of selective blockade of one PI3K catalytic subunit as a treatment option for glioblastoma.

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. For the first time, we found that GBM patients with a high level of NEK9 and PIK3CB had a greater chance of having recurrent tumors. The expression of CDCP1, IGF2R, IRAK3, LATS2, PIK3CB, ULK4, or VRK1 in primary GBM tumors was associated with recurrence-related prognosis. Notably, the level of PIK3CB in recurrent tumors was much higher than that in newly diagnosed ones. Congruent with these results, genes in the PI3K/AKT pathway showed a significantly strong correlation with recurrence rate, further highlighting the pivotal role of PIK3CB in the disease progression. Importantly, 17 SKGs together presented a novel GBM prognostic signature. SKGs identified herein are associated with recurrence rate and present prognostic significance in GBM, thereby becoming attractive therapeutic targets.

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. Recent studies indicate that the gap junction protein connexin 43 (Cx43) renders GBM cells resistant to TMZ through its carboxyl terminus (CT). In this study, we report insights into how Cx43 promotes TMZ resistance. Cx43 levels were inversely correlated with TMZ sensitivity of GBM cells, including GBM stem cells. Moreover, Cx43 levels inversely correlated with patient survival, including as observed in MGMT-deficient GBM patients. Addition of the C-terminal peptide mimetic αCT1, a selective inhibitor of Cx43 channels, sensitized human MGMT-deficient and TMZ-resistant GBM cells to TMZ treatment. Moreover, combining αCT1 with TMZ-blocked AKT/mTOR signaling, induced autophagy and apoptosis in TMZ-resistant GBM cells. Our findings suggest that Cx43 may offer a biomarker to predict the survival of patients with MGMT-independent TMZ resistance and that combining a Cx43 inhibitor with TMZ could enhance therapeutic responses in GBM, and perhaps other TMZ-resistant cancers.

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. By comparison with earlier estimates based on common variants, our model shows a clear genetic signal of accelerating population growth, suggesting that humanity harbours a myriad of rare, deleterious variants, and that disease risk and the burden of disease in contemporary populations may be heavily influenced by the distribution of rare variants.