A Composite Gene Expression Signature Optimizes Prediction of Colorectal Cancer Metastasis and Outcome.

PURPOSE: We previously found that an epithelial-to-mesenchymal transition (EMT)-based gene expression signature was highly correlated with the first principal component (PC1) of 326 colorectal cancer tumors and was prognostic. This study was designed to improve these signatures for better prediction of metastasis and outcome. EXPERIMENTAL DESIGN: A total of 468 colorectal cancer tumors including all stages (I-IV) and metastatic lesions were used to develop a new prognostic score (ΔPC1.EMT) by subtracting the EMT signature score from its correlated PC1 signature score. The score was validated on six other independent datasets with a total of 3,697 tumors. RESULTS: ΔPC1.EMT was found to be far more predictive of metastasis and outcome than its parent scores. It performed well in stages I to III, among microsatellite instability subtypes, and across multiple mutation-based subclasses, demonstrating a refined capacity to predict distant metastatic potential even in tumors with a "good" prognosis. For example, in the PETACC-3 clinical trial dataset, it predicted worse overall survival in an adjusted multivariable model for stage III patients (HR standardized by interquartile range [IQR] = 1.50; 95% confidence interval, 1.25-1.81; P = 0.000016, N = 644). The improved performance of ΔPC1.EMT was related to its propensity to identify epithelial-like subpopulations as well as mesenchymal-like subpopulations. Biologically, the signature was correlated positively with RAS signaling but negatively with mitochondrial metabolism. ΔPC1.EMT was a "best of assessed" prognostic score when compared with 10 other known prognostic signatures. CONCLUSIONS: The study developed a prognostic signature score with a propensity to detect non-EMT features, including epithelial cancer stem cell-related properties, thereby improving its potential to predict metastasis and poorer outcome in stage I-III patients.

Semiquantitative Computed Tomography Characteristics for Lung Adenocarcinoma and Their Association With Lung Cancer Survival.

UNLABELLED: In this study we developed 25 computed tomography descriptors among 117 patients with lung adenocarcinoma to semiquantitatively assess their association with overall survival. Pleural attachment was significantly associated with an increased risk of death and texture was most important for distinguishing histological subtypes. This approach has the potential to support automated analyses and develop decision-support clinical tools. BACKGROUND: Computed tomography (CT) characteristics derived from noninvasive images that represent the entire tumor might have diagnostic and prognostic value. The purpose of this study was to assess the association of a standardized set of semiquantitative CT characteristics of lung adenocarcinoma with overall survival. PATIENTS AND METHODS: An initial set of CT descriptors was developed to semiquantitatively assess lung adenocarcinoma in patients (n = 117) who underwent resection. Survival analyses were used to determine the association between each characteristic and overall survival. Principle component analysis (PCA) was used to determine characteristics that might differentiate histological subtypes. RESULTS: Characteristics significantly associated with overall survival included pleural attachment (P < .001), air bronchogram (P = .03), and lymphadenopathy (P = .02). Multivariate analyses revealed pleural attachment was significantly associated with an increased risk of death overall (hazard ratio [HR], 3.21; 95% confidence interval [CI], 1.53-6.70) and among patients with lepidic predominant adenocarcinomas (HR, 5.85; 95% CI, 1.75-19.59), and lymphadenopathy was significantly associated with an increased risk of death among patients with adenocarcinomas without a predominant lepidic component (HR, 3.07; 95% CI, 1.09-8.70). A PCA model showed that texture (ground-glass opacity component) was most important for separating the 2 subtypes. CONCLUSION: A subset of the semiquantitative characteristics described herein has prognostic importance and provides the ability to distinguish between different histological subtypes of lung adenocarcinoma.

Tolerance associated gene expression following allogeneic hematopoietic cell transplantation.

Biologic markers of immune tolerance may facilitate tailoring of immune suppression duration after allogeneic hematopoietic cell transplantation (HCT). In a cross-sectional study, peripheral blood samples were obtained from tolerant (n = 15, median 38.5 months post-HCT) and non-tolerant (n = 17, median 39.5 post-HCT) HCT recipients and healthy control subjects (n = 10) for analysis of immune cell subsets and differential gene expression. There were no significant differences in immune subsets across groups. We identified 281 probe sets unique to the tolerant (TOL) group and 122 for non-tolerant (non-TOL). These were enriched for process networks including NK cell cytotoxicity, antigen presentation, lymphocyte proliferation, and cell cycle and apoptosis. Differential gene expression was enriched for CD56, CD66, and CD14 human lineage-specific gene expression. Differential expression of 20 probe sets between groups was sufficient to develop a classifier with > 90% accuracy, correctly classifying 14/15 TOL cases and 15/17 non-TOL cases. These data suggest that differential gene expression can be utilized to accurately classify tolerant patients following HCT. Prospective investigation of immune tolerance biologic markers is warranted.

Fibrosis and subsequent cytopenias are associated with basic fibroblast growth factor-deficient pluripotent mesenchymal stromal cells in large granular lymphocyte leukemia.

Cytopenias occur frequently in systemic lupus erythematosus, rheumatoid arthritis, Felty's syndrome, and large granular lymphocyte (LGL) leukemia, but the bone marrow microenvironment has not been systematically studied. In LGL leukemia (n = 24), retrospective analysis of bone marrow (BM) histopathology revealed severe fibrosis in 15 of 24 patients (63%) in association with the presence of cytopenias, occurrence of autoimmune diseases, and splenomegaly, but was undetectable in control cases with B cell malignancies (n = 11). Fibrosis severity correlated with T cell LGL cell numbers in the BM, but not in the periphery, suggesting deregulation is limited to the BM microenvironment. To identify fibrosis-initiating populations, primary mesenchymal stromal cultures (MSCs) from patients were characterized and found to display proliferation kinetics and overabundant collagen deposition, but displayed normal telomere lengths and osteoblastogenic, chondrogenic, and adipogenic differentiation potentials. To determine the effect of fibrosis on healthy hematopoietic progenitor cells (HPCs), bioartificial matrixes from rat tail or purified human collagen were found to suppress HPC differentiation and proliferation. The ability of patient MSCs to support healthy HSC proliferation was significantly impaired, but could be rescued with collagenase pretreatment. Clustering analysis confirmed the undifferentiated state of patient MSCs, and pathway analysis revealed an inverse relationship between cell division and profibrotic ontologies associated with reduced basic fibroblast growth factor production, which was confirmed by ELISA. Reconstitution with exogenous basic fibroblast growth factor normalized patient MSC proliferation, collagen deposition, and HPC supportive function, suggesting LGL BM infiltration and secondary accumulation of MSC-derived collagen is responsible for hematopoietic failure in autoimmune-associated cytopenias in LGL leukemia.

Development and application of a novel metric to assess effectiveness of biomedical data.

OBJECTIVE: Design a metric to assess the comparative effectiveness of biomedical data elements within a study that incorporates their statistical relatedness to a given outcome variable as well as a measurement of the quality of their underlying data. MATERIALS AND METHODS: The cohort consisted of 874 patients with adenocarcinoma of the lung, each with 47 clinical data elements. The p value for each element was calculated using the Cox proportional hazard univariable regression model with overall survival as the endpoint. An attribute or A-score was calculated by quantification of an element's four quality attributes; Completeness, Comprehensiveness, Consistency and Overall-cost. An effectiveness or E-score was obtained by calculating the conditional probabilities of the p-value and A-score within the given data set with their product equaling the effectiveness score (E-score). RESULTS: The E-score metric provided information about the utility of an element beyond an outcome-related p value ranking. E-scores for elements age-at-diagnosis, gender and tobacco-use showed utility above what their respective p values alone would indicate due to their relative ease of acquisition, that is, higher A-scores. Conversely, elements surgery-site, histologic-type and pathological-TNM stage were down-ranked in comparison to their p values based on lower A-scores caused by significantly higher acquisition costs. CONCLUSIONS: A novel metric termed E-score was developed which incorporates standard statistics with data quality metrics and was tested on elements from a large lung cohort. Results show that an element's underlying data quality is an important consideration in addition to p value correlation to outcome when determining the element's clinical or research utility in a study.

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.

A novel five gene signature derived from stem-like side population cells predicts overall and recurrence-free survival in NSCLC.

Gene expression profiling has been used to characterize prognosis in various cancers. Earlier studies had shown that side population cells isolated from Non-Small Cell Lung Cancer (NSCLC) cell lines exhibit cancer stem cell properties. In this study we apply a systems biology approach to gene expression profiling data from cancer stem like cells isolated from lung cancer cell lines to identify novel gene signatures that could predict prognosis. Microarray data from side population (SP) and main population (MP) cells isolated from 4 NSCLC lines (A549, H1650, H460, H1975) were used to examine gene expression profiles associated with stem like properties. Differentially expressed genes that were over or under-expressed at least two fold commonly in all 4 cell lines were identified. We found 354 were upregulated and 126 were downregulated in SP cells compared to MP cells; of these, 89 up and 62 downregulated genes (average 2 fold changes) were used for Principle Component Analysis (PCA) and MetaCore pathway analysis. The pathway analysis demonstrated representation of 4 up regulated genes (TOP2A, AURKB, BRRN1, CDK1) in chromosome condensation pathway and 1 down regulated gene FUS in chromosomal translocation. Microarray data was validated using qRT-PCR on the 5 selected genes and all showed robust correlation between microarray and qRT-PCR. Further, we analyzed two independent gene expression datasets that included 360 lung adenocarcinoma patients from NCI Director's Challenge Set for overall survival and 63 samples from Sungkyunkwan University (SKKU) for recurrence free survival. Kaplan-Meier and log-rank test analysis predicted poor survival of patients in both data sets. Our results suggest that genes involved in chromosome condensation are likely related with stem-like properties and might predict survival in lung adenocarcinoma. Our findings highlight a gene signature for effective identification of lung adenocarcinoma patients with poor prognosis and designing more aggressive therapies for such patients.

SIRT1 negatively regulates the activities, functions, and protein levels of hMOF and TIP60.

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.

Necdin, a negative growth regulator, is a novel STAT3 target gene down-regulated in human cancer.

Cytokine and growth factor signaling pathways involving STAT3 are frequently constitutively activated in many human primary tumors, and are known for the transcriptional role they play in controlling cell growth and cell cycle progression. However, the extent of STAT3's reach on transcriptional control of the genome as a whole remains an important question. We predicted that this persistent STAT3 signaling affects a wide variety of cellular functions, many of which still remain to be characterized. We took a broad approach to identify novel STAT3 regulated genes by examining changes in the genome-wide gene expression profile by microarray, using cells expressing constitutively-activated STAT3. Using computational analysis, we were able to define the gene expression profiles of cells containing activated STAT3 and identify candidate target genes with a wide range of biological functions. Among these genes we identified Necdin, a negative growth regulator, as a novel STAT3 target gene, whose expression is down-regulated at the mRNA and protein levels when STAT3 is constitutively active. This repression is STAT3 dependent, since inhibition of STAT3 using siRNA restores Necdin expression. A STAT3 DNA-binding site was identified in the Necdin promoter and both EMSA and chromatin immunoprecipitation confirm binding of STAT3 to this region. Necdin expression has previously been shown to be down-regulated in a melanoma and a drug-resistant ovarian cancer cell line. Further analysis of Necdin expression demonstrated repression in a STAT3-dependent manner in human melanoma, prostate and breast cancer cell lines. These results suggest that STAT3 coordinates expression of genes involved in multiple metabolic and biosynthetic pathways, integrating signals that lead to global transcriptional changes and oncogenesis. STAT3 may exert its oncogenic effect by up-regulating transcription of genes involved in promoting growth and proliferation, but also by down-regulating expression of negative regulators of the same cellular processes, such as Necdin.

BAD phosphorylation determines ovarian cancer chemosensitivity and patient survival.

PURPOSE: Despite initial sensitivity to chemotherapy, ovarian cancers (OVCA) often develop drug resistance, which limits patient survival. Using specimens and/or genomic data from 289 patients and a panel of cancer cell lines, we explored genome-wide expression changes that underlie the evolution of OVCA chemoresistance and characterized the BCL2 antagonist of cell death (BAD) apoptosis pathway as a determinant of chemosensitivity and patient survival. EXPERIMENTAL DESIGN: Serial OVCA cell cisplatin treatments were performed in parallel with measurements of genome-wide expression changes. Pathway analysis was carried out on genes associated with increasing cisplatin resistance (EC(50)). BAD-pathway expression and BAD protein phosphorylation were evaluated in patient samples and cell lines as determinants of chemosensitivity and/or clinical outcome and as therapeutic targets. RESULTS: Induced in vitro OVCA cisplatin resistance was associated with BAD-pathway expression (P < 0.001). In OVCA cell lines and primary specimens, BAD protein phosphorylation was associated with platinum resistance (n = 147, P < 0.0001) and also with overall patient survival (n = 134, P = 0.0007). Targeted modulation of BAD-phosphorylation levels influenced cisplatin sensitivity. A 47-gene BAD-pathway score was associated with in vitro phosphorylated BAD levels and with survival in 142 patients with advanced-stage (III/IV) serous OVCA. Integration of BAD-phosphorylation or BAD-pathway score with OVCA surgical cytoreductive status was significantly associated with overall survival by log-rank test (P = 0.004 and P < 0.0001, respectively). CONCLUSION: The BAD apoptosis pathway influences OVCA chemosensitivity and overall survival, likely via modulation of BAD phosphorylation. The pathway has clinical relevance as a biomarker of therapeutic response, patient survival, and as a promising therapeutic target.

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. EXPERIMENTAL DESIGN: Liquid chromatography coupled to multiple reaction monitoring (LC-MRM) mass spectrometry assays are developed using SDS-PAGE fractionated lysates from cancer cell lines. Pathway maps created using GeneGO Metacore provide the biological relationships between proteins and illustrate concepts for multiplexed analysis; each protein can be selected to examine assay development at the protein and peptide levels. RESULTS: The coupling of SDS-PAGE and multiple reaction monitoring mass spectrometry screening has been used to detect 876 peptides from 218 cancer-related proteins in model systems including colon, lung, melanoma, leukemias, and myeloma, which has led to the development of 95 quantitative assays including stable-isotope-labeled peptide standards. Methods are published online and peptide standards are made available to the research community. Protein expression measurements for heat shock proteins, including a comparison with ELISA and monitoring response to the HSP90 inhibitor, 17-(dimethylaminoethylamino)-17-demethoxygeldanamycin (17-DMAG), are used to illustrate the components of the QuAD and its potential utility. CONCLUSIONS AND CLINICAL RELEVANCE: This resource enables quantitative assessment of protein components of signaling pathways and biological processes and holds promise for systematic investigation of treatment responses in cancer.

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.

Hybrid model integrating immunohistochemistry and expression profiling for the classification of carcinomas of unknown primary site.

Identification of the site of origin for 'malignancy with unknown primary' remains a challenge for modern pathology. Correct diagnosis is critical to defining the most beneficial treatment for the patient. Standard pathological approaches combine morphology and immunohistochemical (IHC) studies to first subclassify cytokeratin-positive carcinomas into adenocarcinoma, squamous cell carcinoma, neuroendocrine carcinoma, and urothelial carcinoma. Subsequently, organ-specific IHC-markers, if available, are used to assign the tumor's primary site of origin. Previous gene expression classifiers have shown promise in tumor classification but cannot readily be integrated into standard practice because they ignore the algorithmic hierarchy used by pathologists. Here we present a novel hybrid approach integrating a hierarchy of gene expression classifiers into the algorithmic method used with IHC. In this method, a tumor is initially assigned to one of the carcinoma subclasses by the top tier classifier. Dependent on initial classification, one of three second-tier classifiers assign primary site resulting in both carcinoma subtype and primary site classification. First tier classifier accuracies were 89%, 88%, and 75% for cross-validation, independent, and institutional independent test sets, respectively. Second tier accuracies were 87%, 90%, and 87% for adenocarcinoma, squamous, and neuroendocrine carcinoma respectively. Therefore, we can successfully separate the four main subtypes of carcinoma and subsequently assign primary site by incorporation of gene expression-based classifiers into the standard algorithmic pathology approach.

Gene expression profiling of colorectal mucinous adenocarcinomas.

PURPOSE: Although mucinous adenocarcinomas represent 6% to 19% of all colorectal adenocarcinomas, little is known about the genome-wide alterations associated with this malignancy. We have sought to characterize both the gene expression profiles of mucinous adenocarcinomas and their clinicopathologic features. METHODS: Tumors from 171 patients with primary colorectal cancer were profiled using the Affymetrix HG-U133Plus 2.0 GeneChip with characterization of clinicopathologic data. Gene ontology software was used to identify altered biologic pathways. RESULTS: Twenty (11.7%) mucinous adenocarcinomas and 151 (89.3%) nonmucinous adenocarcinomas were identified. Mucinous adenocarcinomas were more likely to be diagnosed with lymph node (LN) metastases (75% vs 51%, P = .04) and at a more advanced stage (85% vs 54%, P = .006) but long-term survival (5-y survival 58.9% vs 58.7%, P = NS) was similar. Mucinous adenocarcinomas displayed 182 upregulated and 135 downregulated genes. The most upregulated genes included those involved in cellular differentiation and mucin metabolism (eg, AQP3 + 4.6, MUC5AC +4.2, MUC2 + 2.8). Altered biologic pathways included those associated with mucin substrate metabolism (P = .002 and .02), amino acid metabolism (P = .02), and the mitogen-activated protein kinase cascade (P = .02). DISCUSSION: Using gene expression profiling of mucinous adenocarcinomas, we have identified the differential upregulation of genes involved in differentiation and mucin metabolism, as well as specific biologic pathways. These findings suggest that mucinous adenocarcinomas represent a genetically distinct variant of colorectal adencarcinoma and have implications for the development of targeted therapies.

Biomarkers to discern transplantation tolerance after allogeneic hematopoietic cell transplantation.

Although it is commonly accepted that allogeneic hematopoietic cell transplant (HCT) recipients develop transplantation tolerance and can quickly discontinue all immunosuppressive drugs, existing data does not support this concept. Most patients will require a prolonged duration of immunosuppression, lasting commonly several years. This has even greater importance, as the majority of transplants are now performed utilizing peripheral blood mobilized stem cells, which are associated with an increased risk of chronic graft-versus-host disease (cGVHD) and prolonged duration of immunosuppression. Despite these challenges, the approach to liberation from immunosuppression after HCT is empiric, and biomarkers of operational tolerance after HCT are lacking. Conversely, investigators in solid organ allografting have begun to examine tolerance associated gene expression in renal and hepatic allograft recipients. Significant challenges in the design and interpretation of these studies potentially limit comparisons. However, a relatively unified model is beginning to emerge, which largely recapitulates previously established mechanisms of immune tolerance. This evidence supports a state of immune quiescence with reduced expression of costimulation and immune response genes, and upregulation of cell cycle control genes. Data indirectly supports the importance of tumor growth factor (TGF)-beta, supports the role of CD4(+)CD25(+) regulatory T cells, and offers new insights into the role of natural killer (NK) cells. Distinct in hepatic allograft tolerance, emerging evidence highlights the importance of gammadeltaT cells, and selection of the Vgammadelta1+ subtype among the gammadeltaT cell population. The deficiencies in the current understanding of transplantation tolerance after HCT, as well as the inadequacies evident in the current empiric approach to immunosuppressive medication (IS) management after HCT make clear the rationale for investigation aimed at elucidating tolerance associated biomarkers after HCT.

Proliferative genes dominate malignancy-risk gene signature in histologically-normal breast tissue.

Historical data have indicated the potential for the histologically-normal breast to harbor pre-malignant changes at the molecular level. We postulated that a histologically-normal tissue with "tumor-like" gene expression pattern might harbor substantial risk for future cancer development. Genes associated with these high-risk tissues were considered to be "malignancy-risk genes". From a total of 90 breast cancer patients, we collected a set of 143 histologically-normal breast tissues derived from patients harboring breast cancer who underwent curative mastectomy, as well as a set of 42 invasive ductal carcinomas (IDC) of various histologic grades. All samples were assessed for global gene expression differences using microarray analysis. For the purpose of this study we defined normal breast tissue to include histologically normal and benign lesions. Here we report the discovery of a "malignancy-risk" gene signature that may portend risk of breast cancer development in benign, but molecularly-abnormal, breast tissue. Pathway analysis showed that the malignancy-risk signature had a dramatic enrichment for genes with proliferative function, but appears to be independent of ER, PR, and HER2 status. The signature was validated by RT-PCR, with a high correlation (Pearson correlation = 0.95 with P < 0.0001) with microarray data. These results suggest a predictive role for the malignancy-risk signature in normal breast tissue. Proliferative biology dominates the earliest stages of tumor development.

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.

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.

Electrocardiographic T-wave changes underlying acute cardiac and cerebral events.

T-wave inversions produced by myocardial infarction (MI) are classically narrow and symmetric. Electrocardiography T-wave changes including low-amplitude and abnormally inverted T waves may be the result of noncardiac path physiology. We present a series of cases that presented with different electrocardiography T-wave changes. The first case involved a 64-year-old woman who presented to the emergency department with diffuse splayed T-wave inversions and was found to have an MI in the context of an acute cerebrovascular accident. We contrasted this case with that of a 76-year-old man with hypercholesterolemia who presented with T-wave widening and a prolonged QT interval and was found to have a subarachnoid hemorrhage secondary to a basilar aneurysm and no MI. Several mechanisms have been suggested to explain the cardiac and cerebral injury, including microvascular spasm and increased levels of circulating catecholamines. Accurate interpretation of T-wave changes can assist the clinician toward a timely therapeutic intervention and accurate diagnosis.

Tissue-specific RMA models to incrementally normalize Affymetrix GeneChip data.

Gene expression classifiers have been used to predict diagnosis of disease, patient prognosis and patient response to therapy. Although there have been remarkable successes in this area, a particular goal of personalized medicine is the ability predict a response from a single gene expression microarray. One aspect of this problem is the normalization of microarrays. Affymetrix GeneChip microarrays are typically processed using model-based algorithms that require all of the data in order to adequately estimate the model. We experiment with the RMA normalization procedure in an incremental fashion, adding new chips to an existing normalization model. Focusing on tissue-specific normalization models, we generate datasets that have very small differences from a batch normalization. Through several large datasets of patient samples, we provide evidence that RMA models of normalization converge to a common model in homogenous samples. These results offer the promise of maintaining large data warehouses of patient microarray samples without the requirement of constant renormalization.