Joseph R. Nevins

Bio: Joseph R. Nevins is an academic researcher from Duke University. The author has contributed to research in topics: E2F & Transcription factor. The author has an hindex of 105, co-authored 261 publications receiving 42988 citations. Previous affiliations of Joseph R. Nevins include Thomas Jefferson University & Durham University.

Oncogenic pathway signatures in human cancers as a guide to targeted therapies.

Abstract: The development of an oncogenic state is a complex process involving the accumulation of multiple independent mutations that lead to deregulation of cell signalling pathways central to the control of cell growth and cell fate. The ability to define cancer subtypes, recurrence of disease and response to specific therapies using DNA microarray-based gene expression signatures has been demonstrated in multiple studies. Various studies have also demonstrated the potential for using gene expression profiles for the analysis of oncogenic pathways. Here we show that gene expression signatures can be identified that reflect the activation status of several oncogenic pathways. When evaluated in several large collections of human cancers, these gene expression signatures identify patterns of pathway deregulation in tumours and clinically relevant associations with disease outcomes. Combining signature-based predictions across several pathways identifies coordinated patterns of pathway deregulation that distinguish between specific cancers and tumour subtypes. Clustering tumours based on pathway signatures further defines prognosis in respective patient subsets, demonstrating that patterns of oncogenic pathway deregulation underlie the development of the oncogenic phenotype and reflect the biology and outcome of specific cancers. Predictions of pathway deregulation in cancer cell lines are also shown to predict the sensitivity to therapeutic agents that target components of the pathway. Linking pathway deregulation with sensitivity to therapeutics that target components of the pathway provides an opportunity to make use of these oncogenic pathway signatures to guide the use of targeted therapeutics.

E2F: a link between the Rb tumor suppressor protein and viral oncoproteins

Abstract: The cellular transcription factor E2F, previously identified as a component of early adenovirus transcription, has now been shown to be important in cell proliferation control. E2F appears to be a functional target for the action of the tumor suppressor protein Rb that is encoded by the retinoblastoma susceptibility gene. The disruption of this E2F-Rb interaction, as well as a complex involving E2F in association with the cell cycle-regulated cyclin A-cdk2 kinase complex, may be a common mechanism of action for the oncoproteins encoded by the DNA tumor viruses.

Predicting the clinical status of human breast cancer by using gene expression profiles.

Abstract: Prognostic and predictive factors are indispensable tools in the treatment of patients with neoplastic disease. For the most part, such factors rely on a few specific cell surface, histological, or gross pathologic features. Gene expression assays have the potential to supplement what were previously a few distinct features with many thousands of features. We have developed Bayesian regression models that provide predictive capability based on gene expression data derived from DNA microarray analysis of a series of primary breast cancer samples. These patterns have the capacity to discriminate breast tumors on the basis of estrogen receptor status and also on the categorized lymph node status. Importantly, we assess the utility and validity of such models in predicting the status of tumors in crossvalidation determinations. The practical value of such approaches relies on the ability not only to assess relative probabilities of clinical outcomes for future samples but also to provide an honest assessment of the uncertainties associated with such predictive classifications on the basis of the selection of gene subsets for each validation analysis. This latter point is of critical importance in the ability to apply these methodologies to clinical assessment of tumor phenotype.

Multiple Ras-dependent phosphorylation pathways regulate Myc protein stability

Abstract: Our recent work has shown that activation of the Ras/Raf/ERK pathway extends the half-life of the Myc protein and thus enhances the accumulation of Myc activity. We have extended these observations by investigating two N-terminal phosphorylation sites in Myc, Thr 58 and Ser 62, which are known to be regulated by mitogen stimulation. We now show that the phosphorylation of these two residues is critical for determining the stability of Myc. Phosphorylation of Ser 62 is required for Ras-induced stabilization of Myc, likely mediated through the action of ERK. Conversely, phosphorylation of Thr 58, likely mediated by GSK-3 but dependent on the prior phosphorylation of Ser 62, is associated with degradation of Myc. Further analysis demonstrates that the Ras-dependent PI-3K pathway is also critical for controlling Myc protein accumulation, likely through the control of GSK-3 activity. These observations thus define a synergistic role for multiple Ras-mediated phosphorylation pathways in the control of Myc protein accumulation during the initial stage of cell proliferation.

Regularization and variable selection via the elastic net

Abstract: Summary. We propose the elastic net, a new regularization and variable selection method. Real world data and a simulation study show that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation. In addition, the elastic net encourages a grouping effect, where strongly correlated predictors tend to be in or out of the model together.The elastic net is particularly useful when the number of predictors (p) is much bigger than the number of observations (n). By contrast, the lasso is not a very satisfactory variable selection method in the

Gene expression profiling predicts clinical outcome of breast cancer

Abstract: Breast cancer patients with the same stage of disease can have markedly different treatment responses and overall outcome. The strongest predictors for metastases (for example, lymph node status and histological grade) fail to classify accurately breast tumours according to their clinical behaviour. Chemotherapy or hormonal therapy reduces the risk of distant metastases by approximately one-third; however, 70-80% of patients receiving this treatment would have survived without it. None of the signatures of breast cancer gene expression reported to date allow for patient-tailored therapy strategies. Here we used DNA microarray analysis on primary breast tumours of 117 young patients, and applied supervised classification to identify a gene expression signature strongly predictive of a short interval to distant metastases ('poor prognosis' signature) in patients without tumour cells in local lymph nodes at diagnosis (lymph node negative). In addition, we established a signature that identifies tumours of BRCA1 carriers. The poor prognosis signature consists of genes regulating cell cycle, invasion, metastasis and angiogenesis. This gene expression profile will outperform all currently used clinical parameters in predicting disease outcome. Our findings provide a strategy to select patients who would benefit from adjuvant therapy.

CDK inhibitors: positive and negative regulators of G1-phase progression

Abstract: Mitogen-dependent progression through the first gap phase (G1) and initiation of DNA synthesis (S phase) during the mammalian cell division cycle are cooperatively regulated by several classes of cyclin-dependent kinases (CDKs) whose activities are in turn constrained by CDK inhibitors (CKIs). CKIs that govern these events have been assigned to one of two families based on their structures and CDK targets. The first class includes the INK4 proteins (inhibitors of CDK4), so named for their ability to specifically inhibit the catalytic subunits of CDK4 and CDK6. Four such proteins [p16 (Serrano et al. 1993), p15 (Hannon and Beach 1994), p18 (Guan et al. 1994; Hirai et al. 1995), and p19 (Chan et al. 1995; Hirai et al. 1995)] are composed of multiple ankyrin repeats and bind only to CDK4 and CDK6 but not to other CDKs or to D-type cyclins. The INK4 proteins can be contrasted with more broadly acting inhibitors of the Cip/Kip family whose actions affect the activities of cyclin D-, E-, and A-dependent kinases. The latter class includes p21 (Gu et al. 1993; Harper et al. 1993; El-Deiry et al. 1993; Xiong et al. 1993a; Dulic et al. 1994; Noda et al. 1994), p27 (Polyak et al. 1994a,b; Toyoshima and Hunter 1994), and p57 (Lee et al. 1995; Matsuoka et al. 1995), all of which contain characteristic motifs within their amino-terminal moieties that enable them to bind both to cyclin and CDK subunits (Chen et al. 1995, 1996; Nakanishi et al. 1995; Warbrick et al. 1995; Lin et al. 1996; Russo et al. 1996). Based largely on in vitro experiments and in vivo overexpression studies, CKIs of the Cip/Kip family were initially thought to interfere with the activities of cyclin D-, E-, and A-dependent kinases. More recent work has altered this view and revealed that although the Cip/Kip proteins are potent inhibitors of cyclin Eand A-dependent CDK2, they act as positive regulators of cyclin Ddependent kinases. This challenges previous assumptions about how the G1/S transition of the mammalian cell cycle is governed, helps explain some enigmatic features of cell cycle control that also involve the functions of the retinoblastoma protein (Rb) and the INK4 proteins, and changes our thinking about how either p16 loss or overexpression of cyclin D-dependent kinases contribute to cancer. Here we focus on the biochemical interactions that occur between CKIs and cyclin Dand E-dependent kinases in cultured mammalian cells, emphasizing the manner by which different positive and negative regulators of the cell division cycle cooperate to govern the G1-to-S transition. To gain a more comprehensive understanding of the biology of CDK inhibitors, readers are encouraged to refer to a rapidly emerging but already extensive literature (for review, see Elledge and Harper 1994; Sherr and Roberts 1995; Chellappan et al. 1998; Hengst and Reed 1998a; Kiyokawa and Koff 1998; Nakayama 1998; Ruas and Peters 1998).

A Gene-Expression Signature as a Predictor of Survival in Breast Cancer

Abstract: Background A more accurate means of prognostication in breast cancer will improve the selection of patients for adjuvant systemic therapy. Methods Using microarray analysis to evaluate our previously established 70-gene prognosis profile, we classified a series of 295 consecutive patients with primary breast carcinomas as having a gene-expression signature associated with either a poor prognosis or a good prognosis. All patients had stage I or II breast cancer and were younger than 53 years old; 151 had lymph-node–negative disease, and 144 had lymph-node–positive disease. We evaluated the predictive power of the prognosis profile using univariable and multivariable statistical analyses. Results Among the 295 patients, 180 had a poor-prognosis signature and 115 had a good-prognosis signature, and the mean (±SE) overall 10-year survival rates were 54.6±4.4 percent and 94.5±2.6 percent, respectively. At 10 years, the probability of remaining free of distant metastases was 50.6±4.5 percent in the group with a...

Integrated genomic analyses of ovarian carcinoma

Abstract: A catalogue of molecular aberrations that cause ovarian cancer is critical for developing and deploying therapies that will improve patients' lives. The Cancer Genome Atlas project has analysed messenger RNA expression, microRNA expression, promoter methylation and DNA copy number in 489 high-grade serous ovarian adenocarcinomas and the DNA sequences of exons from coding genes in 316 of these tumours. Here we report that high-grade serous ovarian cancer is characterized by TP53 mutations in almost all tumours (96%); low prevalence but statistically recurrent somatic mutations in nine further genes including NF1, BRCA1, BRCA2, RB1 and CDK12; 113 significant focal DNA copy number aberrations; and promoter methylation events involving 168 genes. Analyses delineated four ovarian cancer transcriptional subtypes, three microRNA subtypes, four promoter methylation subtypes and a transcriptional signature associated with survival duration, and shed new light on the impact that tumours with BRCA1/2 (BRCA1 or BRCA2) and CCNE1 aberrations have on survival. Pathway analyses suggested that homologous recombination is defective in about half of the tumours analysed, and that NOTCH and FOXM1 signalling are involved in serous ovarian cancer pathophysiology.