Gregory C. Bloom

A Gene Expression Model of Intrinsic Tumor Radiosensitivity: Prediction of Response and Prognosis After Chemoradiation

PURPOSE Development of a radiosensitivity predictive assay is a central goal of radiation oncology. We reasoned a gene expression model could be developed to predict intrinsic radiosensitivity and treatment response in patients. METHODS AND MATERIALS Radiosensitivity (determined by survival fraction at 2 Gy) was modeled as a function of gene expression, tissue of origin, ras status (mut/wt), and p53 status (mut/wt) in 48 human cancer cell lines. Ten genes were identified and used to build a rank-based linear regression algorithm to predict an intrinsic radiosensitivity index (RSI, high index = radioresistance). This model was applied to three independent cohorts treated with concurrent chemoradiation: head-and-neck cancer (HNC, n = 92); rectal cancer (n = 14); and esophageal cancer (n = 12). RESULTS Predicted RSI was significantly different in responders (R) vs. nonresponders (NR) in the rectal (RSI R vs. NR 0.32 vs. 0.46, p = 0.03), esophageal (RSI R vs. NR 0.37 vs. 0.50, p = 0.05) and combined rectal/esophageal (RSI R vs. NR 0.34 vs. 0.48, p = 0.001511) cohorts. Using a threshold RSI of 0.46, the model has a sensitivity of 80%, specificity of 82%, and positive predictive value of 86%. Finally, we evaluated the model as a prognostic marker in HNC. There was an improved 2-year locoregional control (LRC) in the predicted radiosensitive group (2-year LRC 86% vs. 61%, p = 0.05). CONCLUSIONS We validate a robust multigene expression model of intrinsic tumor radiosensitivity in three independent cohorts totaling 118 patients. To our knowledge, this is the first time that a systems biology-based radiosensitivity model is validated in multiple independent clinical datasets.

Prediction of Radiation Sensitivity Using a Gene Expression Classifier

The development of a successful radiation sensitivity predictive assay has been a major goal of radiation biology for several decades. We have developed a radiation classifier that predicts the inherent radiosensitivity of tumor cell lines as measured by survival fraction at 2 Gy (SF2), based on gene expression profiles obtained from the literature. Our classifier correctly predicts the SF2 value in 22 of 35 cell lines from the National Cancer Institute panel of 60, a result significantly different from chance (P = 0.0002). In our approach, we treat radiation sensitivity as a continuous variable, significance analysis of microarrays is used for gene selection, and a multivariate linear regression model is used for radiosensitivity prediction. The gene selection step identified three novel genes (RbAp48, RGS19, and R5PIA) of which expression values are correlated with radiation sensitivity. Gene expression was confirmed by quantitative real-time PCR. To biologically validate our classifier, we transfected RbAp48 into three cancer cell lines (HS-578T, MALME-3M, and MDA-MB-231). RbAp48 overexpression induced radiosensitization (1.5- to 2-fold) when compared with mock-transfected cell lines. Furthermore, we show that HS-578T-RbAp48 overexpressors have a higher proportion of cells in G2-M (27% versus 5%), the radiosensitive phase of the cell cycle. Finally, RbAp48 overexpression is correlated with dephosphorylation of Akt, suggesting that RbAp48 may be exerting its effect by antagonizing the Ras pathway. The implications of our findings are significant. We establish that radiation sensitivity can be predicted based on gene expression profiles and we introduce a genomic approach to the identification of novel molecular markers of radiation sensitivity.

Systems Biology Modeling of the Radiation Sensitivity Network: A Biomarker Discovery Platform

PURPOSE The discovery of effective biomarkers is a fundamental goal of molecular medicine. Developing a systems-biology understanding of radiosensitivity can enhance our ability of identifying radiation-specific biomarkers. METHODS AND MATERIALS Radiosensitivity, as represented by the survival fraction at 2 Gy was modeled in 48 human cancer cell lines. We applied a linear regression algorithm that integrates gene expression with biological variables, including ras status (mut/wt), tissue of origin and p53 status (mut/wt). RESULTS The biomarker discovery platform is a network representation of the top 500 genes identified by linear regression analysis. This network was reduced to a 10-hub network that includes c-Jun, HDAC1, RELA (p65 subunit of NFKB), PKC-beta, SUMO-1, c-Abl, STAT1, AR, CDK1, and IRF1. Nine targets associated with radiosensitization drugs are linked to the network, demonstrating clinical relevance. Furthermore, the model identified four significant radiosensitivity clusters of terms and genes. Ras was a dominant variable in the analysis, as was the tissue of origin, and their interaction with gene expression but not p53. Overrepresented biological pathways differed between clusters but included DNA repair, cell cycle, apoptosis, and metabolism. The c-Jun network hub was validated using a knockdown approach in 8 human cell lines representing lung, colon, and breast cancers. CONCLUSION We have developed a novel radiation-biomarker discovery platform using a systems biology modeling approach. We believe this platform will play a central role in the integration of biology into clinical radiation oncology practice.

Osteopontin induces multiple changes in gene expression that reflect the six "hallmarks of cancer" in a model of breast cancer progression.

Tumor progression is a multistep process, which enables cells to evolve from benign to malignant tumors. This progression has been suggested to depend on six essential characteristics identified as the “hallmarks of cancer,” which include: self‐sufficiency in growth signals, insensitivity to growth‐inhibitory signals, evasion of apoptosis, limitless replicative potential, sustained angiogenesis, and tissue invasion and metastasis. Osteopontin (OPN) is an integrin‐binding protein that has been shown to be associated with the progression of several cancer types, and to play an important functional role in various aspects of malignancy, particularly tissue invasion and metastasis. Here we studied genes regulated by OPN in a model of human breast cancer using oligonucleotide microarray technology by comparing the gene‐expression profiles of 21NT mammary carcinoma cells transfected to overexpress OPN versus mock‐transfected control cells. From over 12,000 human genes, we identified 99 known human genes differentially regulated by OPN whose expression changed by at least 1.5‐fold and showed statistically significant differences in mean expression levels between groups. Functional classification of these genes into the hallmarks of cancer categories showed that OPN can affect the expression of genes involved in all six categories in this model. Furthermore, we were able to validate the expression of 18/19 selected candidate genes by quantitative real‐time PCR, further supporting our microarray findings. This study provides the first evidence that OPN can lead to numerous gene expression changes that influence multiple aspects of tumor progression and malignant growth.

The Metabolomic Signature of Malignant Glioma Reflects Accelerated Anabolic Metabolism

Although considerable progress has been made toward understanding glioblastoma biology through large-scale genetic and protein expression analyses, little is known about the underlying metabolic alterations promoting their aggressive phenotype. We conducted global metabolomic profiling on patient-derived glioma specimens and identified specific metabolic programs differentiating low- and high-grade tumors, with the metabolic signature of glioblastoma reflecting accelerated anabolic metabolism. When coupled with transcriptional profiles, we identified the metabolic phenotype of the mesenchymal subtype to consist of accumulation of the glycolytic intermediate phosphoenolpyruvate and decreased pyruvate kinase activity. Unbiased hierarchical clustering of metabolomic profiles identified three subclasses, which we term energetic, anabolic, and phospholipid catabolism with prognostic relevance. These studies represent the first global metabolomic profiling of glioma, offering a previously undescribed window into their metabolic heterogeneity, and provide the requisite framework for strategies designed to target metabolism in this rapidly fatal malignancy.

LIN28B Polymorphisms Influence Susceptibility to Epithelial Ovarian Cancer

Defective microRNA (miRNA) biogenesis contributes to the development and progression of epithelial ovarian cancer (EOC). In this study, we examined the hypothesis that single nucleotide polymorphisms (SNP) in miRNA biogenesis genes may influence EOC risk. In an initial investigation, 318 SNPs in 18 genes were evaluated among 1,815 EOC cases and 1,900 controls, followed up by a replicative joint meta-analysis of data from an additional 2,172 cases and 3,052 controls. Of 23 SNPs from 9 genes associated with risk (empirical P < 0.05) in the initial investigation, the meta-analysis replicated 6 SNPs from the DROSHA, FMR1, LIN28, and LIN28B genes, including rs12194974 (G>A), an SNP in a putative transcription factor binding site in the LIN28B promoter region (summary OR = 0.90, 95% CI: 0.82-0.98; P = 0.015) which has been recently implicated in age of menarche and other phenotypes. Consistent with reports that LIN28B overexpression in EOC contributes to tumorigenesis by repressing tumor suppressor let-7 expression, we provide data from luciferase reporter assays and quantitative RT-PCR to suggest that the inverse association among rs12194974 A allele carriers may be because of reduced LIN28B expression. Our findings suggest that variants in LIN28B and possibly other miRNA biogenesis genes may influence EOC susceptibility.

SIRT1 Negatively Regulates the Activities, Functions, and Protein Levels of hMOF and TIP60

ABSTRACT SIRT1 is a NAD+-dependent histone H4K16 deacetylase that controls several different normal physiologic and disease processes. Like most histone deacetylases, SIRT1 also deacetylates nonhistone proteins. Here, we show that two members of the MYST (MOZ, Ybf2/Sas3, Sas2, and TIP60) acetyltransferase family, hMOF and TIP60, are SIRT1 substrates. SIRT1 deacetylation of the enzymatic domains of hMOF and TIP60 inhibits their acetyltransferase activity and promotes ubiquitination-dependent degradation of these proteins. Importantly, immediately following DNA damage, the binding of SIRT1 to hMOF and TIP60 is transiently interrupted, with corresponding hMOF/TIP60 hyperacetylation. Lysine-to-arginine mutations in SIRT1-targeted lysines on hMOF and TIP60 repress DNA double-strand break repair and inhibit the ability of hMOF/TIP60 to induce apoptosis in response to DNA double-strand break. Together, these findings uncover novel pathways in which SIRT1 dynamically interacts with and regulates hMOF and TIP60 through deacetylation and provide additional mechanistic insights by which SIRT1 regulates DNA damage response.

A database of reaction monitoring mass spectrometry assays for elucidating therapeutic response in cancer

Purpose: The Quantitative Assay Database (QuAD), http://proteome.moffitt.org/QUAD/, facilitates widespread implementation of quantitative mass spectrometry in cancer biology and clinical research through sharing of methods and reagents for monitoring protein expression and modification.

Deregulated expression of LRBA facilitates cancer cell growth.

LRBA expression is induced by mitogens in lymphoid and myeloid cells. The Drosophila LRBA orthologue rugose/DAKAP550 is involved in Notch, Ras and EGFR pathways. These findings suggest that LRBA could play a role in cell types that have increased proliferative and survival capacity. Here, we show by microarray and real-time PCR analyses that LRBA is overexpressed in several different cancers relative to their normal tissue controls. We also show that LRBA promoter activity and endogenous LRBA mRNA levels are reduced by p53 and increased by E2F1, indicating that mutations in the tumor suppressors p53 and Rb could contribute to the deregulation of LRBA. Furthermore, inhibition of LRBA expression by RNA interference, or inhibition of its function by a dominant-negative mutant, leads to significant growth inhibition of cancer cells, demonstrating that deregulated expression of LRBA contributes to the altered growth properties of a cancer cell. Finally, we show that the phosphorylation of EGFR is affected by the dominant-negative mutant, suggesting LRBA plays a role in the mammalian EGFR pathway. These findings demonstrate that LRBA facilitates cancer cell growth and thus LRBA may represent a novel molecular target for cancer therapy.

Elucidation of a protein signature discriminating six common types of adenocarcinoma

Pathologists are commonly facing the problem of attempting to identify the site of origin of a metastatic cancer when no primary tumor has been identified, yet few markers have been identified to date. Multitumor classifiers based on microarray based RNA expression have recently been described. Here we describe the first approximation of a tumor classifier based entirely on protein expression quantified by two‐dimensional gel electrophoresis (2DE). The 2DE was used to analyze the proteomic expression pattern of 77 similarly appearing (using histomorphology) adenocarcinomas encompassing 6 types or sites of origin: ovary, colon, kidney, breast, lung and stomach. Discriminating sets of proteins were identified and used to train an artificial neural network (ANN). A leave‐one‐out cross validation (LOOCV) method was used to test the ability of the constructed network to predict the single held out sample from each iteration with a maximum predictive accuracy of 87% and an average predictive accuracy of 82% over the range of proteins chosen for its construction. These findings demonstrate the use of proteomics to construct a highly accurate ANN‐based classifier for the detection of an individual tumor type, as well as distinguishing between 6 common tumor types in an unknown primary diagnosis setting.