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.

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.

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.

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.

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.

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.

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.

Synchronous global assessment of gene and protein expression in colorectal cancer progression.

Well-established models of colorectal cancer progression are based on the idea that the disease evolves through a multistep process involving sequential genetic mutations, suggesting that progression through clinically defined stages should correlate with well-defined patterns of gene expression. The majority of studies to date, however, have assessed these processes one gene and one protein at a time. We report the first comprehensive assessment of both gene and protein expression performed in parallel across progressive stages of human colorectal neoplasia. Remarkably, despite the global nature of the gene expression assessment, very few genes could be linked with certainty to specific proteins through currently available annotations. Furthermore, the correlation of expression between identified genes and proteins was poor. Nevertheless, both produced expression signatures that differentiated normal mucosa and nonmalignant adenomas from each other and from the malignant carcinomas and both produced fairly consistent subclasses of the malignancies, suggesting that a molecular staging might be more appropriate provided that these profiles can be tied to clinical outcome. This is potentially important as clinical staging is widely used as a prognostic indicator and used in the decision to pursue adjuvant therapies.

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.

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.