The purpose of this study was to evaluate the activity of single-agent bevacizumab in patients with recurrent anaplastic glioma and assess correlative advanced imaging parameters. Patients with recurrent anaplastic glioma were treated with bevacizumab 10 mg/kg every 2 weeks. Complete patient evaluations were repeated every 4 weeks. Correlative dynamic contrast-enhanced MR and 18fluorodeoxyglucose PET imaging studies were obtained to evaluate physiologic changes in tumor and tumor vasculature at time points including baseline, 96 h after the first dose, and after the first 4 weeks of therapy. Median overall survival was 12 months (95% confidence interval [CI]: 6.08–22.8). Median progression-free survival was 2.93 months (95% CI: 2.01–4.93), and 6-month progression-free survival was 20.9% (95% CI: 10.3%–42.5%). Thirteen (43%) patients achieved a partial response. The most common grade ≥3 treatment-related toxicities were hypertension, hypophosphatemia, and thromboembolism. Single-agent bevacizumab produces significant radiographic response in patients with recurrent anaplastic glioma but did not meet the 6-month progression-free survival endpoint. Early change in enhancing tumor volume at 4 days after start of therapy was the most significant prognostic factor for overall and progression-free survival.

Affordable early screening in subjects with high risk of lung cancer has great potential to improve survival from this deadly disease. We measured gene expression from lung tissue and peripheral whole blood (PWB) from adenocarcinoma cases and controls to identify dysregulated lung cancer genes that could be tested in blood to improve identification of at-risk patients in the future. Genome-wide mRNA expression analysis was conducted in 153 subjects (73 adenocarcinoma cases, 80 controls) from the Environment And Genetics in Lung cancer Etiology (EAGLE) study using PWB and paired snap-frozen tumor and non-involved lung tissue samples. Analyses were conducted using unpaired t-tests, linear mixed effects and ANOVA models. The area under the receiver operating characteristic curve (AUC) was computed to assess the predictive accuracy of the identified biomarkers. We identified 50 dysregulated genes in stage I adenocarcinoma versus control PWB samples (False Discovery Rate ≤0.1, fold change ≥1.5 or ≤0.66). Among them, eight (TGFBR3, RUNX3, TRGC2, TRGV9, TARP, ACP1, VCAN, and TSTA3) differentiated paired tumor versus non-involved lung tissue samples in stage I cases, suggesting a similar pattern of lung cancer-related changes in PWB and lung tissue. These results were confirmed in two independent gene expression analyses in a blood-based case-control study (n=212) and a tumor-non tumor paired tissue study (n=54). The eight genes discriminated patients with lung cancer from healthy controls with high accuracy (AUC=0.81, 95% CI=0.74–0.87). Our finding suggests the use of gene expression from PWB for the identification of early detection markers of lung cancer in the future.

Noninvasive evaluation using MRI is the primary means to routinely assess children with diffuse intrinsic pontine gliomas (DIPGs). However, no standard MR sequence has correlated with outcome in these patients. In this study, patients with DIPGs were assessed to determine the combined prognostic value via dynamic susceptibility contrast (DSC) MRI, single-voxel spectroscopy (SVS), multivoxel MR spectroscopy (MRS), and T1-weighted post-gadolinium imaging. Eligible patients had clinical and radiographic findings consistent with a DIPG. Imaging studies were acquired on a 1.5T MRI at various time points during each patient's course. Data were evaluated using a Cox proportional hazard model, a time-dependent covariant Cox model, a Wald test, and a Kaplan–Meier analysis. Ninety-eight studies were performed on 34 patients of median age 5.5 years. Median survival from diagnosis was 468 days. At baseline imaging only, increased ratio of choline to n-acetylaspartate (Cho:NAA) on SVS and increased perfusion on DSC-MRI each predicted shorter survival (relative risk [RR] = 1.48, P = .015 and RR = 4.91, P = .0012, respectively). When analyzing all subsequent time points, increased maximum Cho:NAA on MRS (RR = 1.45, P = .042), increased Cho:NAA on SVS (RR = 1.69, P = .003), increased perfusion (RR = 4.68, P = .0016), and the presence of enhancement (RR = 5.69, P = .022) each predicted shorter survival. Kaplan–Meier analysis showed shorter survival associated with increased perfusion at baseline (P = .0004). Increased perfusion at any time point predicts a significantly shorter survival in children with DIPG. In addition, enhancement, increased Cho:NAA on SVS, and increased maximum Cho:NAA on chemical shift imaging are predictive of shorter survival over time. Routine baseline and subsequent imaging for children with DIPG should, at minimum, incorporate DSC-MRI and SVS.

Objective

We sought to determine if there is a correlation between D'Amico risk stratification and degree of suspicion of prostate cancer on multi-parametric MRI, based on targeted biopsies obtained with our electromagnetically (EM) tracked MRI/ultrasound (US) fusion platform.

Methods

101 patients underwent 3 Tesla multi-parametric MR imaging of the prostate which consisted of T2, DCE, DWI, and spectroscopy images in patients with a suspicion for, or diagnosis of prostate cancer. All prostate MRI lesions were then identified and graded by the number of modalities positive: low (≤2), moderate (3) and high (4) suspicion. Patients and lesions were stratified by D'Amico risk stratification. The biopsy protocol included a standard 12 core biopsy followed by real-time MRI/US fusion-targeted biopsies of the suspicious MR lesions.

Results

90.1% of men were clinical T1c with a mean age of 62.7 ± 8.3 years and the median PSA was 5.8 ng/ml. 54.5% of the patients were positive for cancer on the protocol biopsy. A Chi-squared analysis resulted in a statistically significant correlation between the MR suspicion and D'Amico risk stratification for patients (p<0.0001). Within-cluster re-sampling technique determined that there was a statistically significant correlation between MR suspicion and D'Amico risk stratification for MR ‘targeted’ core biopsies and MR lesions (p<0.01)

Conclusion

Our data supports that with multi-parametric MR prostate imaging, one may be able to quantitatively assess the degree of risk associated with MR visible lesions within the prostate.

Background

Esophageal squamous cell carcinoma (ESCC), the predominant histological subtype of esophageal cancer, is characterized by high mortality. Previous work identified important mRNA expression differences between normal and tumor cells; however, to date there are limited ex vivo studies examining expression changes occurring during normal esophageal squamous cell differentiation versus those associated with tumorigenesis. In this study, we used a unique tissue microdissection strategy and microarrays to measure gene expression profiles associated with cell differentiation versus tumorigenesis in twelve cases of patient-matched normal basal squamous epithelial cells (NB), normal differentiated squamous epithelium (ND), and squamous cell cancer. Class comparison and pathway analysis were used to compare NB versus tumor in a search for unique therapeutic targets.

Results

As a first step towards this goal, gene expression profiles and pathways were evaluated. Overall, ND expression patterns were markedly different from NB and tumor; whereas, tumor and NB were more closely related. Tumor showed a general decrease in differentially expressed genes relative to NB as opposed to ND that exhibited the opposite trend. FSH and IgG networks were most highly dysregulated in normal differentiation and tumorigenesis, respectively. DNA repair pathways were generally elevated in NB and tumor relative to ND indicating involvement in both normal and pathological growth. PDGF signaling pathway and 12 individual genes unique to the tumor/NB comparison were identified as therapeutic targets, and 10 associated ESCC gene-drug pairs were identified. We further examined the protein expression level and the distribution patterns of four genes: ODC1, POSTN, ASPA and IGF2BP3. Ultimately, three genes (ODC1, POSTN, ASPA) were verified to be dysregulated in the same pattern at both the mRNA and protein levels.

Conclusions

These data reveal insight into genes and molecular pathways mediating ESCC development and provide information potentially useful in designing novel therapeutic interventions for this tumor type.

Background

The genus Ebolavirus includes five distinct viruses. Four of these viruses cause hemorrhagic fever in humans. Currently there are no licensed vaccines for any of them; however, several vaccines are under development. Ebola virus envelope glycoprotein (GP1,2) is highly immunogenic, but antibodies frequently arise against its least conserved mucin-like domain (MLD). We hypothesized that immunization with MLD-deleted GP1,2 (GPΔMLD) would induce cross-species immunity by making more conserved regions accessible to the immune system.

Methods

To test this hypothesis, mice were immunized with retrovirus-like particles (retroVLPs) bearing Ebola virus GPΔMLD, DNA plasmids (plasmo-retroVLP) that can produce such retroVLPs in vivo, or plasmo-retroVLP followed by retroVLPs.

Results

Cross-species neutralizing antibody and GP1,2-specific cellular immune responses were successfully induced.

Conclusion

Our findings suggest that GPΔMLD presented through retroVLPs may provide a strategy for development of a vaccine against multiple ebolaviruses. Similar vaccination strategies may be adopted for other viruses whose envelope proteins contain highly variable regions that may mask more conserved domains from the immune system.

Patients with diffuse intrinsic pontine glioma (DIPG) face a grim prognosis with limited treatment options. Many patients will enroll on investigational trials though the role of chemotherapy or immunotherapy is unclear. Radiographic changes on conventional MRI are used to evaluate tumor response and progression, but are not predictive of outcome in these patients. More sensitive measures of tumor biology are needed to improve patient management. We evaluated changes in magnetic resonance spectroscopy (MRS) biomarkers in patients with DIPG. Thirty-eight patients were enrolled prospectively on an IRB-approved protocol, which included standard MRI, single voxel spectroscopy (SVS) and multi-slice multi-voxel spectroscopy (MRSI). Scans were performed at multiple time points during each patient’s clinical course, with a total of 142 scans. The prognostic values of Choline:N-acetylaspartate (Cho:NAA), Cho:Creatine (Cho:Cr) and the presence of lactate and lipids (+Lac/Lip) were evaluated. Cho:NAA and variance in Cho:NAA values among different voxels within a tumor were each predictive of shorter survival. This prospective study shows that MRS can be used to identify high-risk patients and monitor changes in tumor metabolism, which may reflect changes in tumor behavior.

Purpose

A novel platform was developed that fuses pre-biopsy magnetic resonance imaging with real-time transrectal ultrasound imaging to identify and biopsy lesions suspicious for prostate cancer. The cancer detection rates for the first 101 patients are reported.

Materials and Methods

This prospective, single institution study was approved by the institutional review board. Patients underwent 3.0 T multiparametric magnetic resonance imaging with endorectal coil, which included T2-weighted, spectroscopic, dynamic contrast enhanced and diffusion weighted magnetic resonance imaging sequences. Lesions suspicious for cancer were graded according to the number of sequences suspicious for cancer as low (2 or less), moderate (3) and high (4) suspicion. Patients underwent standard 12-core transrectal ultrasound biopsy and magnetic resonance imaging/ultrasound fusion guided biopsy with electromagnetic tracking of magnetic resonance imaging lesions. Chi-square and within cluster resampling analyses were used to correlate suspicion on magnetic resonance imaging and the incidence of cancer detected on biopsy.

Results

Mean patient age was 63 years old. Median prostate specific antigen at biopsy was 5.8 ng/ml and 90.1% of patients had a negative digital rectal examination. Of patients with low, moderate and high suspicion on magnetic resonance imaging 27.9%, 66.7% and 89.5% were diagnosed with cancer, respectively (p <0.0001). Magnetic resonance imaging/ultrasound fusion guided biopsy detected more cancer per core than standard 12-core transrectal ultrasound biopsy for all levels of suspicion on magnetic resonance imaging.

Conclusions

Prostate cancer localized on magnetic resonance imaging may be targeted using this novel magnetic resonance imaging/ultrasound fusion guided biopsy platform. Further research is needed to determine the role of this platform in cancer detection, active surveillance and focal therapy, and to determine which patients may benefit.

In many medical studies, patients are followed longitudinally and interest is on assessing the relationship between longitudinal measurements and time to an event. Recently, various authors have proposed joint modeling approaches for longitudinal and time-to-event data for a single longitudinal variable. These joint modeling approaches become intractable with even a few longitudinal variables. In this paper we propose a regression calibration approach for jointly modeling multiple longitudinal measurements and discrete time-to-event data. Ideally, a two-stage modeling approach could be applied in which the multiple longitudinal measurements are modeled in the first stage and the longitudinal model is related to the time-to-event data in the second stage. Biased parameter estimation due to informative dropout makes this direct two-stage modeling approach problematic. We propose a regression calibration approach which appropriately accounts for informative dropout. We approximate the conditional distribution of the multiple longitudinal measurements given the event time by modeling all pairwise combinations of the longitudinal measurements using a bivariate linear mixed model which conditions on the event time. Complete data are then simulated based on estimates from these pairwise conditional models, and regression calibration is used to estimate the relationship between longitudinal data and time-to-event data using the complete data. We show that this approach performs well in estimating the relationship between multivariate longitudinal measurements and the time-to-event data and in estimating the parameters of the multiple longitudinal process subject to informative dropout. We illustrate this methodology with simulations and with an analysis of primary biliary cirrhosis (PBC) data.

The dismal lethality of lung cancer is due to late stage at diagnosis and inherent therapeutic resistance. The incorporation of targeted therapies has modestly improved clinical outcomes, but the identification of new targets could further improve clinical outcomes by guiding stratification of poor-risk early stage patients and individualizing therapeutic choices. We hypothesized that a sequential, combined microarray approach would be valuable to identify and validate new targets in lung cancer. We profiled gene expression signatures during lung epithelial cell immortalization and transformation, and showed that genes involved in mitosis were progressively enhanced in carcinogenesis. 28 genes were validated by immunoblotting and 4 genes were further evaluated in non-small cell lung cancer tissue microarrays. Although CDK1 was highly expressed in tumor tissues, its loss from the cytoplasm unexpectedly predicted poor survival and conferred resistance to chemotherapy in multiple cell lines, especially microtubule-directed agents. An analysis of expression of CDK1 and CDK1-associated genes in the NCI60 cell line database confirmed the broad association of these genes with chemotherapeutic responsiveness. These results have implications for personalizing lung cancer therapy and highlight the potential of combined approaches for biomarker discovery.

In genetic family studies, ages at onset of diseases are routinely collected. Often one is interested in assessing the familial association of ages at onset of a certain disease type. However, when a competing risk is present and is related to the disease of interest, the usual measure of association by treating the competing event as an independent censoring event is biased. We propose a bivariate model that incorporates two types of association: one is between the first event time of paired members, and the other is between the failure types given the first event time. We consider flexible measures for both types of association, and estimate the corresponding association parameters by adopting the two-stage estimation of Shih and Louis (1995) and Nan et al. (2006). The proposed method is illustrated using the kinship data from the Washington Ashkenazi Study.

The development of multidrug resistance (MDR) to chemotherapy remains a major challenge in the treatment of cancer. Resistance exists against every effective anti-cancer drug and can develop by multiple mechanisms. These mechanisms can act individually or synergistically, leading to multidrug resistance (MDR), in which the cell becomes resistant to a variety of structurally and mechanistically unrelated drugs in addition to the drug initially administered. Although extensive work has been done to characterize MDR mechanisms in vitro, the translation of this knowledge to the clinic has not been successful. Therefore, identifying genes and mechanisms critical to the development of MDR in vivo and establishing a reliable method for analyzing highly homologous genes from small amounts of tissue is fundamental to achieving any significant enhancement in our understanding of multidrug resistance mechanisms and could lead to treatments designed to circumvent it. In this study, we use a previously established database that allows the identification of lead compounds in the early stages of drug discovery that are not ABC transporter substrates. We believe this can serve as a model for appraising the accuracy and sensitivity of current methods used to analyze the expression profiles of ABC transporters. We found two platforms to be superior methods for the analysis of expression profiles of highly homologous gene superfamilies. This study also led to an improved database by revealing previously unidentified substrates for ABCB1, ABCC1 and ABCG2, transporters that contribute to multidrug resistance.

The TGF-β pathway has tumor suppressor activity in many epithelial tissues. Since TGF-β is a potent inhibitor of epithelial cell proliferation, it has been widely assumed that this property underlies the tumor suppressor effect. Here we have used a xenograft model of breast cancer to show that endogenous TGF-β has the potential to suppress tumorigenesis through a novel mechanism, involving effects at two distinct levels in the hierarchy of cellular progeny that make up the epithelial component of the tumor. Firstly TGF-β reduces the size of the putative cancer stem or early progenitor cell population, and secondly it promotes differentiation of a more committed, but highly proliferative, progenitor cell population to an intrinsically less proliferative state. We further show that reduced expression of the type II TGF-β receptor correlates with loss of luminal differentiation in a clinical breast cancer cohort, suggesting that this mechanism may be clinically relevant. At a molecular level, the induction of differentiation by TGF-β involves down-regulation of Id1, and forced overexpression of Id1 can promote tumorigenesis despite persistence of the anti-proliferative effect of TGF-β. These data suggest new roles for the TGF-β pathway in regulating tumor cell dynamics that are independent of direct effects on proliferation.

Background

Tobacco smoking is responsible for over 90% of lung cancer cases, and yet the precise molecular alterations induced by smoking in lung that develop into cancer and impact survival have remained obscure.

Methodology/Principal Findings

We performed gene expression analysis using HG-U133A Affymetrix chips on 135 fresh frozen tissue samples of adenocarcinoma and paired noninvolved lung tissue from current, former and never smokers, with biochemically validated smoking information. ANOVA analysis adjusted for potential confounders, multiple testing procedure, Gene Set Enrichment Analysis, and GO-functional classification were conducted for gene selection. Results were confirmed in independent adenocarcinoma and non-tumor tissues from two studies. We identified a gene expression signature characteristic of smoking that includes cell cycle genes, particularly those involved in the mitotic spindle formation (e.g., NEK2, TTK, PRC1). Expression of these genes strongly differentiated both smokers from non-smokers in lung tumors and early stage tumor tissue from non-tumor tissue (p<0.001 and fold-change >1.5, for each comparison), consistent with an important role for this pathway in lung carcinogenesis induced by smoking. These changes persisted many years after smoking cessation. NEK2 (p<0.001) and TTK (p = 0.002) expression in the noninvolved lung tissue was also associated with a 3-fold increased risk of mortality from lung adenocarcinoma in smokers.

Conclusions/Significance

Our work provides insight into the smoking-related mechanisms of lung neoplasia, and shows that the very mitotic genes known to be involved in cancer development are induced by smoking and affect survival. These genes are candidate targets for chemoprevention and treatment of lung cancer in smokers.

Background

Cancer patients have highly variable clinical outcomes owing to many factors, among which are genes that determine the likelihood of invasion and metastasis. This predisposition can be reflected in the gene expression pattern of the primary tumor, which may predict outcomes and guide the choice of treatment better than other clinical predictors.

Methodology/Principal Findings

We developed an mRNA expression-based model that can predict prognosis/outcomes of human breast cancer patients regardless of microarray platform and patient group. Our model was developed using genes differentially expressed in mouse plasma cell tumors growing in vivo versus those growing in vitro. The prediction system was validated using published data from three cohorts of patients for whom microarray and clinical data had been compiled. The model stratified patients into four independent survival groups (BEST, GOOD, BAD, and WORST: log-rank test p = 1.7×10−8).

Conclusions

Our model significantly improved the survival prediction over other expression-based models and permitted recognition of patients with different prognoses within the estrogen receptor-positive group and within a single pathological tumor class. Basing our predictor on a dataset that originated in a different species and a different cell type may have rendered it less sensitive to proliferation differences and endowed it with wide applicability.

Significance

Prognosis prediction for patients with breast cancer is currently based on histopathological typing and estrogen receptor positivity. Yet both assays define groups that are heterogeneous in survival. Gene expression profiling allows subdivision of these groups and recognition of patients whose tumors are very unlikely to be lethal and those with much grimmer outlooks, which can augment the predictive power of conventional tumor analysis and aid the clinician in choosing relaxed vs. aggressive therapy.

Background

Gene expression profiling by microarray analysis of cells enriched by laser capture microdissection (LCM) faces several technical challenges. Frozen sections yield higher quality RNA than paraffin-imbedded sections, but even with frozen sections, the staining methods used for histological identification of cells of interest could still damage the mRNA in the cells. To study the contribution of staining methods to degradation of results from gene expression profiling of LCM samples, we subjected pellets of the mouse plasma cell tumor cell line TEPC 1165 to direct RNA extraction and to parallel frozen sectioning for LCM and subsequent RNA extraction. We used microarray hybridization analysis to compare gene expression profiles of RNA from cell pellets with gene expression profiles of RNA from frozen sections that had been stained with hematoxylin and eosin (H&E), Nissl Stain (NS), and for immunofluorescence (IF) as well as with the plasma cell-revealing methyl green pyronin (MGP) stain. All RNAs were amplified with two rounds of T7-based in vitro transcription and analyzed by two-color expression analysis on 10-K cDNA microarrays.

Results

The MGP-stained samples showed the least introduction of mRNA loss, followed by H&E and immunofluorescence. Nissl staining was significantly more detrimental to gene expression profiles, presumably owing to an aqueous step in which RNA may have been damaged by endogenous or exogenous RNAases.

Conclusion

RNA damage can occur during the staining steps preparatory to laser capture microdissection, with the consequence of loss of representation of certain genes in microarray hybridization analysis. Inclusion of RNAase inhibitor in aqueous staining solutions appears to be important in protecting RNA from loss of gene transcripts.