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

By A. Bild, Guang Yao, Jeffrey T. Chang, Quanli Wang, A. Potti, D. Chasse, M. Joshi, D. Harpole, J. Lancaster, A. Berchuck and 5 more. Nature, 2006

2,044 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d5251a6a56190be7426b0d6a42808a9b7db4b80a

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In Molecular Biology

1. Balázs Győrffy
2. S. Piccolo
3. J. Massagué
4. Trey Ideker
5. D. Nguyen
6. V. Plaks
7. Zena Werb
8. K. Kessenbrock

Citations in the corpus

• Ontology-Based Meta-Analysis of Global Collections of High-Throughput Public Data (2010, background): “Analysis of gene sets derived from specific patient cohorts against specific disease signatures can classify a disease more precisely and help drive patient stratification and trial selection [25,26].”
• Prognostic gene signatures for non-small-cell lung cancer (2009, methodology): “Two of these studies comprise exclusively squamous cell carcinomas (13, 16), one exclusively adenocarcinomas (14), and one both (25).”
• Mechanisms to suppress multipolar divisions in cancer cells with extra centrosomes. (2008, background): “Despite the exciting development of oncogene-specific targeted therapies, at present these therapies are available for a small number of tumor types (Bild et al. 2006).”
• Rank–rank hypergeometric overlap: identification of statistically significant overlap between gene-expression signatures (2010, background): “High overlap between gene-expression changes driven by mutated KRAS2 oncogene in human lung tumors and by overexpression of KRAS2 in a cell line identify signaling events downstream of this oncogene that have in vivo relevance that can be studied effectively in vitro (Figure 3D) (35).”
• Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures (2008, methodology): “Disparate signatures have been proposed, either directly from breast cancer expression profiles [1012,18,19,21,22] or translated from model systems [1,23,24], with little agreement in the constituent genes.”
• WNT/TCF signaling through LEF1 and HOXB9 mediates lung adenocarcinoma metastasis (2009, methodology): “…a mouse model (Sweet-Cordero et al., 2005), to a dominant-negative TCF4 (dnTCF4) in colon cancer cells (van de Wetering et al., 2002), to treatment of epithelial cells with TGF-b (Padua et al., 2008), or to overexpression of c-Myc, SRC, or E2F3 in human mammary epithelial cells (Bild et al., 2006).”
• Young age at diagnosis correlates with worse prognosis and defines a subset of breast cancers with shared patterns of gene expression. (2008, methodology): “This shared biology, beyond traditional clinical parameters, included genes regulating immune function, mTOR/rapamycin pathway, hypoxia, BRCA1, stem cell biology, apoptosis, histone deacetylase, and oncogenic signaling pathways including Myc, E2F, Ras, -catenin, AKT, p53, PTEN, and MAP kinase, many ”
• A Myc rather than core pluripotency module accounts for the shared signatures of embryonic stem and cancer cells (2010, background): “(B) Average gene expression values (log2) (Bild et al., 2006) of each module tested in (A) upon induction of Myc in human epithelial cells (Myc induction) and in control cells (WT).”
• PrognoScan: a new database for meta-analysis of the prognostic value of genes (2009, background): “As of February 2009, the collection included more than 40 datasets of various cancer types spanning a wide range of cancers including bladder, blood, breast, brain, esophagus, head and neck, kidney, lung, and ovarian (Table 1) [10-35], far more comprehensive than both ITTACA, which focuses on bladde”
• The morphologies of breast cancer cell lines in three‐dimensional assays correlate with their profiles of gene expression (2007, background): “12 demonstration by Nevins and co-workers that gene expression profiles can be used to predict sensitivity and resistance to different chemotherapeutic agents (Bild et al., 2006; Potti et al., 2006) is exciting and could be used to shortlist compounds for testing in this assay.”
• SurvExpress: An Online Biomarker Validation Tool and Database for Cancer Gene Expression Data Using Survival Analysis (2013, methodology): “We used a special lung meta-base build in our research group, which is composed of more than 1,000 samples obtained from six authors (Bild [19], Raponi [20], Zhu [21], Hou [22], NCI [23], Okayama [24]), equivalent Affymetrix gene expression platform, and containing all biomarker genes.”
• A signature of chromosomal instability inferred from gene expression profiles predicts clinical outcome in multiple human cancers (2006, neutral): “Increasing tFA Incasing colation w ith FA chr1 chr2 chr3 chr4 chr5 chr6 chr7 chr8 chr9 chr10 chr11 chr12 chr13 chr14 chr15 chr16 chr17 chr18 chr19 chr20 chr21 chr22 chrX chrY (1,3] (–Inf,–7] (–7,–5] (–5,–3] (–3,–1] (–1,1] (3,5] (5,7] (7, Inf]”
• Online Survival Analysis Software to Assess the Prognostic Value of Biomarkers Using Transcriptomic Data in Non-Small-Cell Lung Cancer (2013, background): “A pre-requisite for the reproducibility of such genomic signatures is the availability of raw data, which was only ensured by publications of the last six years [9,10,11,12,13,14,15,16,17,18].”