Diversity of plant DNA in stool is linked to dietary quality, age, and household income.

Eating a varied diet is a central tenet of good nutrition. Here, we develop a molecular tool to quantify human dietary plant diversity by applying DNA metabarcoding with the chloroplast trnL-P6 marker to 1,029 fecal samples from 324 participants across two interventional feeding studies and three observational cohorts. The number of plant taxa per sample (plant metabarcoding richness or pMR) correlated with recorded intakes in interventional diets and with indices calculated from a food frequency questionnaire in typical diets (ρ = 0.40 to 0.63). In adolescents unable to collect validated dietary survey data, trnL metabarcoding detected 111 plant taxa, with 86 consumed by more than one individual and four (wheat, chocolate, corn, and potato family) consumed by >70% of individuals. Adolescent pMR was associated with age and household income, replicating prior epidemiologic findings. Overall, trnL metabarcoding promises an objective and accurate measure of the number and types of plants consumed that is applicable to diverse human populations.

A Multidimensional Bioinformatic Platform for the Study of Human Response to Surgery.

OBJECTIVE: To design and establish a prospective biospecimen repository that integrates multi-omics assays with clinical data to study mechanisms of controlled injury and healing. BACKGROUND: Elective surgery is an opportunity to understand both the systemic and focal responses accompanying controlled and well-characterized injury to the human body. The overarching goal of this ongoing project is to define stereotypical responses to surgical injury, with the translational purpose of identifying targetable pathways involved in healing and resilience, and variations indicative of aberrant peri-operative outcomes. METHODS: Clinical data from the electronic medical record combined with large-scale biological data sets derived from blood, urine, fecal matter, and tissue samples are collected prospectively through the peri-operative period on patients undergoing 14 surgeries chosen to represent a range of injury locations and intensities. Specimens are subjected to genomic, transcriptomic, proteomic, and metabolomic assays to describe their genetic, metabolic, immunologic, and microbiome profiles, providing a multidimensional landscape of the human response to injury. RESULTS: The highly multiplexed data generated includes changes in over 28,000 mRNA transcripts, 100 plasma metabolites, 200 urine metabolites, and 400 proteins over the longitudinal course of surgery and recovery. In our initial pilot dataset, we demonstrate the feasibility of collecting high quality multi-omic data at pre- and postoperative time points and are already seeing evidence of physiologic perturbation between timepoints. CONCLUSIONS: This repository allows for longitudinal, state-of-the-art geno-mic, transcriptomic, proteomic, metabolomic, immunologic, and clinical data collection and provides a rich and stable infrastructure on which to fuel further biomedical discovery.

Rationale and design of "Hearts & Parks": study protocol for a pragmatic randomized clinical trial of an integrated clinic-community intervention to treat pediatric obesity.

BACKGROUND: The prevalence of child and adolescent obesity and severe obesity continues to increase despite decades of policy and research aimed at prevention. Obesity strongly predicts cardiovascular and metabolic disease risk; both begin in childhood. Children who receive intensive behavioral interventions can reduce body mass index (BMI) and reverse disease risk. However, delivering these interventions with fidelity at scale remains a challenge. Clinic-community partnerships offer a promising strategy to provide high-quality clinical care and deliver behavioral treatment in local park and recreation settings. The Hearts & Parks study has three broad objectives: (1) evaluate the effectiveness of the clinic-community model for the treatment of child obesity, (2) define microbiome and metabolomic signatures of obesity and response to lifestyle change, and (3) inform the implementation of similar models in clinical systems. METHODS: Methods are designed for a pragmatic randomized, controlled clinical trial (n = 270) to test the effectiveness of an integrated clinic-community child obesity intervention as compared with usual care. We are powered to detect a difference in body mass index (BMI) between groups at 6 months, with follow up to 12 months. Secondary outcomes include changes in biomarkers for cardiovascular disease, psychosocial risk, and quality of life. Through collection of biospecimens (serum and stool), additional exploratory outcomes include microbiome and metabolomics biomarkers of response to lifestyle modification. DISCUSSION: We present the study design, enrollment strategy, and intervention details for a randomized clinical trial to measure the effectiveness of a clinic-community child obesity treatment intervention. This study will inform a critical area in child obesity and cardiovascular risk research-defining outcomes, implementation feasibility, and identifying potential molecular mechanisms of treatment response. CLINICAL TRIAL REGISTRATION: NCT03339440 .

Genomic profiling in locally advanced and inflammatory breast cancer and its link to DCE-MRI and overall survival.

PURPOSE: We have previously reported that dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) perfusion patterns obtained from locally advanced breast cancer (LABC) patients prior to neoadjuvant therapy predicted pathologic clinical response. Genomic analyses were also independently conducted on the same patient population. This retrospective study was performed to test two hypotheses: (1) gene expression profiles are associated with DCE-MRI perfusion patterns, and (2) association between long-term overall survival data and gene expression profiles can lead to the identification of novel predictive biomarkers. METHODS: We utilised RNA microarray and DCE-MRI data from 47 LABC patients, including 13 inflammatory breast cancer (IBC) patients. Association between gene expression profile and DCE-MRI perfusion patterns (centrifugal and centripetal) was determined by Wilcoxon rank sum test. Association between gene expression level and survival was assessed using a Cox rank score test. Additional genomic analysis of the IBC subset was conducted, with a period of follow-up of up to 11 years. Associations between gene expression and overall survival were further assessed in The Cancer Genome Atlas Data Portal. RESULTS: Differences in gene expression profiles were seen between centrifugal and centripetal perfusion patterns in the sulphotransferase family, cytosolic, 1 A, phenol-preferring, members 1 and 2 (SULT1A1, SULT1A2), poly (ADP-ribose) polymerase, member 6 (PARP6), and metastasis tumour antigen1 (MTA1). In the IBC subset our analyses demonstrated that differential expression of 45 genes was associated with long-term survival. CONCLUSIONS: Here we have demonstrated an association between DCE-MRI perfusion patterns and gene expression profiles. In addition we have reported on candidate prognostic biomarkers in IBC patients, with some of the genes being significantly associated with survival in IBC and LABC.

Retraction: A genomic strategy to refine prognosis in early-stage non-small-cell lung cancer. N Engl J Med 2006;355:570-80.

To the Editor: We would like to retract our article, "A Genomic Strategy to Refine Prognosis in Early-Stage Non-Small-Cell Lung Cancer,"(1) which was published in the Journal on August 10, 2006. Using a sample set from a study by the American College of Surgeons Oncology Group (ACOSOG) and a collection of samples from a study by the Cancer and Leukemia Group B (CALGB), we have tried and failed to reproduce results supporting the validation of the lung metagene model described in the article. We deeply regret the effect of this action on the work of other investigators.

Genomic signatures to guide the use of chemotherapeutics.

Using in vitro drug sensitivity data coupled with Affymetrix microarray data, we developed gene expression signatures that predict sensitivity to individual chemotherapeutic drugs. Each signature was validated with response data from an independent set of cell line studies. We further show that many of these signatures can accurately predict clinical response in individuals treated with these drugs. Notably, signatures developed to predict response to individual agents, when combined, could also predict response to multidrug regimens. Finally, we integrated the chemotherapy response signatures with signatures of oncogenic pathway deregulation to identify new therapeutic strategies that make use of all available drugs. The development of gene expression profiles that can predict response to commonly used cytotoxic agents provides opportunities to better use these drugs, including using them in combination with existing targeted therapies.

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

The development of an oncogenic state is a complex process involving the accumulation of multiple independent mutations that lead to deregulation of cell signalling pathways central to the control of cell growth and cell fate. The ability to define cancer subtypes, recurrence of disease and response to specific therapies using DNA microarray-based gene expression signatures has been demonstrated in multiple studies. Various studies have also demonstrated the potential for using gene expression profiles for the analysis of oncogenic pathways. Here we show that gene expression signatures can be identified that reflect the activation status of several oncogenic pathways. When evaluated in several large collections of human cancers, these gene expression signatures identify patterns of pathway deregulation in tumours and clinically relevant associations with disease outcomes. Combining signature-based predictions across several pathways identifies coordinated patterns of pathway deregulation that distinguish between specific cancers and tumour subtypes. Clustering tumours based on pathway signatures further defines prognosis in respective patient subsets, demonstrating that patterns of oncogenic pathway deregulation underlie the development of the oncogenic phenotype and reflect the biology and outcome of specific cancers. Predictions of pathway deregulation in cancer cell lines are also shown to predict the sensitivity to therapeutic agents that target components of the pathway. Linking pathway deregulation with sensitivity to therapeutics that target components of the pathway provides an opportunity to make use of these oncogenic pathway signatures to guide the use of targeted therapeutics.

Characterizing the developmental pathways TTF-1, NKX2-8, and PAX9 in lung cancer.

We investigated the clinical implications of lung developmental transcription factors (TTF-1, NKX2-8, and PAX9) that we recently discovered as cooperating oncogenes activated by way of gene amplification at chromosome 14q13 in lung cancer. Using stable transfectants of human bronchial epithelial cells, RNA expression profiles (signatures) representing activation of the biological pathways defined by each of the 3 genes were determined and used to risk stratify a non-small-cell lung cancer (NSCLC) clinical data set consisting of 91 early stage tumors. Coactivation of the TTF-1 and NKX2-8 pathways identified a cluster of patients with poor survival, representing approximately 20% of patients with early stage NSCLC, whereas activation of individual pathways did not reveal significant prognostic power. Importantly, the poor prognosis associated with coactivation of TTF-1 and NKX2-8 was validated in 2 other independent clinical data sets. Furthermore, lung cancer cell lines showing coactivation of the TTF-1 and NKX2-8 pathways were shown to exhibit resistance to cisplatin, the standard of care for the treatment of NSCLC. This suggests that the cohort of patients with coactivation of TTF-1 and NKX2-8 pathways appears to be resistant to standard cisplatin therapy, suggesting the need for alternative therapies in this cohort of high-risk patients.

RNA splicing and aggregate gene expression differences in lung squamous cell carcinoma between patients of West African and European ancestry.

OBJECTIVES: Despite disparities in lung cancer incidence and mortality, the molecular landscape of lung cancer in patients of African ancestry remains underexplored, and race-related differences in RNA splicing remain unexplored. MATERIALS AND METHODS: We identified differentially spliced genes (DSGs) and differentially expressed genes (DEGs) in biobanked lung squamous cell carcinoma (LUSC) between patients of West African and European ancestry, using ancestral genotyping and Affymetrix Clariom D array. DSGs and DEGs were validated independently using the National Cancer Institute Genomic Data Commons. Associated biological processes, overlapping canonical pathways, enriched gene sets, and cancer relevance were identified using Gene Ontology Consortium, Ingenuity Pathway Analysis, Gene Set Enrichment Analysis, and CancerMine, respectively. Association with LUSC survival was conducted using The Cancer Genome Atlas. RESULTS: 4,829 DSGs and 267 DEGs were identified, including novel targets in NSCLC as well as genes identified previously to have relevance to NSCLC. RNA splicing events within 3 DSGs as well as 1 DEG were validated in the independent cohort. 853 DSGs and 29 DEGs have been implicated as potential drivers, oncogenes and/or tumor suppressor genes. Biological processes enriched among DSGs and DEGs included metabolic process, biological regulation, and multicellular organismal process and, among DSGs, ion transport. Overlapping canonical pathways among DSGs included neuronal signaling pathways and, among DEGs, cell metabolism involving biosynthesis. Gene sets enriched among DSGs included KRAS Signaling, UV Response, E2 F Targets, Glycolysis, and Coagulation. 355 RNA splicing events within DSGs and 18 DEGs show potential association with LUSC patient survival. CONCLUSION: These DSGs and DEGs, which show potential biological and clinical relevance, could have the ability to drive novel biomarker and therapeutic development to mitigate LUSC disparities.

The Pediatric Obesity Microbiome and Metabolism Study (POMMS): Methods, Baseline Data, and Early Insights.

OBJECTIVE: The purpose of this study was to establish a biorepository of clinical, metabolomic, and microbiome samples from adolescents with obesity as they undergo lifestyle modification. METHODS: A total of 223 adolescents aged 10 to 18 years with BMI ≥95th percentile were enrolled, along with 71 healthy weight participants. Clinical data, fasting serum, and fecal samples were collected at repeated intervals over 6 months. Herein, the study design, data collection methods, and interim analysis-including targeted serum metabolite measurements and fecal 16S ribosomal RNA gene amplicon sequencing among adolescents with obesity (n = 27) and healthy weight controls (n = 27)-are presented. RESULTS: Adolescents with obesity have higher serum alanine aminotransferase, C-reactive protein, and glycated hemoglobin, and they have lower high-density lipoprotein cholesterol when compared with healthy weight controls. Metabolomics revealed differences in branched-chain amino acid-related metabolites. Also observed was a differential abundance of specific microbial taxa and lower species diversity among adolescents with obesity when compared with the healthy weight group. CONCLUSIONS: The Pediatric Metabolism and Microbiome Study (POMMS) biorepository is available as a shared resource. Early findings suggest evidence of a metabolic signature of obesity unique to adolescents, along with confirmation of previously reported findings that describe metabolic and microbiome markers of obesity.

Ovarian cancer tumor infiltrating T-regulatory (T(reg)) cells are associated with a metastatic phenotype.

OBJECTIVE: The objective of this study was to examine the clinicopathologic correlates of T-regulatory (T(reg)) cell infiltration in serous ovarian cancers and to define gene signatures associated with high T(reg)s. METHODS: Tumor infiltrating T(reg) and cytotoxic T-cells (CTLs) were quantitated in 232 primary serous ovarian cancers by immunostaining for FOXP3 and CD8. Expression microarray analysis was performed in a subset of 48 advanced cancers with the highest and lowest numbers of infiltrating T(reg)s and a genomic signature was developed using binary regression. ANOVA analysis was performed to assess the most differentially expressed genes and these genes were further assessed using Ingenuity Pathway Analysis (IPA) software. RESULTS: High T(reg) infiltration in ovarian cancers was associated with high grade (p<0.0001), advanced stage (p=0.004) and suboptimal debulking (p<0.04), but not with survival. In contrast, high tumor infiltrating CD8 CTL infiltration was associated with favorable survival (median survival 48.7 vs. 34.6 months, p=0.01). A microarray-based genomic signature for high tumor-infiltrating T(reg) cells had a 77% predictive accuracy using leave-one-out cross-validation. ANOVA of microarray data revealed the antigen presentation pathway as the most differentially expressed canonical pathway (p<0.00001) between cancers with high and low T(reg) cells. CONCLUSIONS: These data suggest that there may be an association between increased T(reg) cell infiltration in ovarian cancers and advanced stage. Increased T(reg) infiltration is characterized by a genomic signature enriched with several immunologic pathway genes. Therapeutic strategies that reduce tumor infiltrating T(reg) cells are under investigation and may prove useful in ovarian cancers with high numbers of these cells.

Microarray analysis of early stage serous ovarian cancers shows profiles predictive of favorable outcome.

PURPOSE: Although few women with advanced serous ovarian cancer are cured, detection of the disease at an early stage is associated with a much higher likelihood of survival. We previously used gene expression array analysis to distinguish subsets of advanced cancers based on disease outcome. In the present study, we report on gene expression of early-stage cancers and validate our prognostic model for advanced-stage cancers. EXPERIMENTAL DESIGN: Frozen specimens from 39 stage I/II, 42 stage III/IV, and 20 low malignant potential cancers were obtained from four different sites. A linear discriminant model was used to predict survival based upon array data. RESULTS: We validated the late-stage survival model and show that three of the most differentially expressed genes continue to be predictive of outcome. Most early-stage cancers (38 of 39 invasive, 15 of 20 low malignant potential) were classified as long-term survivors (median probabilities 0.97 and 0.86). MAL, the most differentially expressed gene, was further validated at the protein level and found to be an independent predictor of poor survival in an unselected group of advanced serous cancers (P = 0.0004). CONCLUSIONS: These data suggest that serous ovarian cancers detected at an early stage generally have a favorable underlying biology similar to advanced-stage cases that are long-term survivors. Conversely, most late-stage ovarian cancers seem to have a more virulent biology. This insight suggests that if screening approaches are to succeed it will be necessary to develop approaches that are able to detect these virulent cancers at an early stage.

Short-Chain Fatty Acid Production by Gut Microbiota from Children with Obesity Differs According to Prebiotic Choice and Bacterial Community Composition.

Pediatric obesity remains a public health burden and continues to increase in prevalence. The gut microbiota plays a causal role in obesity and is a promising therapeutic target. Specifically, the microbial production of short-chain fatty acids (SCFA) from the fermentation of otherwise indigestible dietary carbohydrates may protect against pediatric obesity and metabolic syndrome. Still, it has not been demonstrated that therapies involving microbiota-targeting carbohydrates, known as prebiotics, will enhance gut bacterial SCFA production in children and adolescents with obesity (age, 10 to 18 years old). Here, we used an in vitro system to examine the SCFA production by fecal microbiota from 17 children with obesity when exposed to five different commercially available over-the-counter (OTC) prebiotic supplements. We found microbiota from all 17 patients actively metabolized most prebiotics. Still, supplements varied in their acidogenic potential. Significant interdonor variation also existed in SCFA production, which 16S rRNA sequencing supported as being associated with differences in the host microbiota composition. Last, we found that neither fecal SCFA concentration, microbiota SCFA production capacity, nor markers of obesity positively correlated with one another. Together, these in vitro findings suggest the hypothesis that OTC prebiotic supplements may be unequal in their ability to stimulate SCFA production in children and adolescents with obesity and that the most acidogenic prebiotic may differ across individuals.IMPORTANCE Pediatric obesity remains a major public health problem in the United States, where 17% of children and adolescents are obese, and rates of pediatric "severe obesity" are increasing. Children and adolescents with obesity face higher health risks, and noninvasive therapies for pediatric obesity often have limited success. The human gut microbiome has been implicated in adult obesity, and microbiota-directed therapies can aid weight loss in adults with obesity. However, less is known about the microbiome in pediatric obesity, and microbiota-directed therapies are understudied in children and adolescents. Our research has two important findings: (i) dietary prebiotics (fiber) result in the microbiota from adolescents with obesity producing more SCFA, and (ii) the effectiveness of each prebiotic is donor dependent. Together, these findings suggest that prebiotic supplements could help children and adolescents with obesity, but that these therapies may not be "one size fits all."

A genomic strategy to refine prognosis in early-stage non-small-cell lung cancer.

BACKGROUND: Clinical trials have indicated a benefit of adjuvant chemotherapy for patients with stage IB, II, or IIIA--but not stage IA--non-small-cell lung cancer (NSCLC). This classification scheme is probably an imprecise predictor of the prognosis of an individual patient. Indeed, approximately 25 percent of patients with stage IA disease have a recurrence after surgery, suggesting the need to identify patients in this subgroup for more effective therapy. METHODS: We identified gene-expression profiles that predicted the risk of recurrence in a cohort of 89 patients with early-stage NSCLC (the lung metagene model). We evaluated the predictor in two independent groups of 25 patients from the American College of Surgeons Oncology Group (ACOSOG) Z0030 study and 84 patients from the Cancer and Leukemia Group B (CALGB) 9761 study. RESULTS: The lung metagene model predicted recurrence for individual patients significantly better than did clinical prognostic factors and was consistent across all early stages of NSCLC. Applied to the cohorts from the ACOSOG Z0030 trial and the CALGB 9761 trial, the lung metagene model had an overall predictive accuracy of 72 percent and 79 percent, respectively. The predictor also identified a subgroup of patients with stage IA disease who were at high risk for recurrence and who might be best treated by adjuvant chemotherapy. CONCLUSIONS: The lung metagene model provides a potential mechanism to refine the estimation of a patient's risk of disease recurrence and, in principle, to alter decisions regarding the use of adjuvant chemotherapy in early-stage NSCLC.

Novel tumor sampling strategies to enable microarray gene expression signatures in breast cancer: a study to determine feasibility and reproducibility in the context of clinical care.

Feasibility and reproducibility of microarray biomarkers in clinical settings are doubted because of reliance on fresh frozen tissue. We sought to develop and validate a paradigm of frozen tissue collection from early breast tumors to enable use of microarray in oncology practice. Frozen core needle biopsies (CNBx) were collected from 150 clinical stage I patients during image-guided diagnostic biopsy and/or surgery. Histology and tumor content from frozen cores were compared to diagnostic specimens. Twenty-eight patients had microarray analysis to examine accuracy and reproducibility of predictive gene signatures developed for estrogen receptor (ER) and HER2. One hundred twenty-seven (85%) of 150 patients had at least one frozen core containing cancer suitable for microarray analysis. Larger tumor size, ex vivo biopsy, and use of a new specimen device increased the likelihood of obtaining adequate specimens. Sufficient quality RNA was obtained from 90% of tumor cores. Microarray signatures predicting ER and HER2 expression were developed in training sets of up to 363 surgical samples and were applied to microarray data obtained from core samples collected in clinical settings. In these samples, prediction of ER and HER2 expression achieved a sensitivity/specificity of 94%/100%, and 82%/72%, respectively. Predictions were reproducible in 83-100% of paired samples. Frozen CNBx can be readily obtained from most breast cancers without interfering with pathologic evaluation in routine clinical settings. Collection of tumor tissue at diagnostic biopsy and/or at surgery from lumpectomy specimens using image guidance resulted in sufficient samples for array analysis from over 90% of patients. Sampling of breast cancer for microarray data is reproducible and feasible in clinical practice and can yield signatures predictive of multiple breast cancer phenotypes.

Assessing incomplete deprotection of microarray oligonucleotides in situ.

En masse analysis of gene structure and function by array technologies will have a lasting and profound effect on biology and medicine. This impact can be compromised by low quality of probes within arrays, which we show can be caused by incomplete removal of chemical protecting groups. To solve this quality control problem, we present a sensitive, specific and facile method to detect these groups in situ on arrays using monoclonal antibodies and existing instrumentation. Screening of microarrays with these monoclonal antibodies should guide the consideration given to data derived from these and should enhance the accuracy of the results obtained.

Gene expression signatures, clinicopathological features, and individualized therapy in breast cancer.

CONTEXT: Gene expression profiling may be useful for prognostic and therapeutic strategies in breast carcinoma. OBJECTIVES: To demonstrate the value in integrating genomic information with clinical and pathological risk factors, to refine prognosis, and to improve therapeutic strategies for early stage breast cancer. DESIGN, SETTING, AND PATIENTS: Retrospective study of patients with early stage breast carcinoma who were candidates for adjuvant chemotherapy; 964 clinically annotated breast tumor samples (573 in the initial discovery set and 391 in the validation cohort) with corresponding microarray data were used. All patients were assigned relapse risk scores based on their respective clinicopathological features. Signatures representing oncogenic pathway activation and tumor biology/microenvironment status were applied to these samples to obtain patterns of deregulation that correspond with relapse risk scores to refine prognosis with the clinicopathological prognostic model alone. Predictors of chemotherapeutic response were also applied to further characterize clinically relevant heterogeneity in early stage breast cancer. MAIN OUTCOME MEASURES: Gene expression signatures and clinicopathological variables in early stage breast cancer to determine a refined estimation of relapse-free survival and sensitivity to chemotherapy. RESULTS: In the initial data set of 573 patients, prognostically significant clusters representing patterns of oncogenic pathway activation and tumor biology/microenvironment states were identified within the low-risk (log-rank P = .004), intermediate-risk (log-rank P = .01), and high-risk (log-rank P = .003) model cohorts, representing clinically important genomic subphenotypes of breast cancer. As an example, in the low-risk cohort, of 6 prognostically significant clusters, patients in cluster 4 had an inferior relapse-free survival vs patients in cluster 1 (log-rank P = .004) and cluster 5 (log-rank P = .03). Median relapse-free survival for patients in cluster 4 was 16 months less than for patients in cluster 1 (95% CI, 7.5-24.5 months) and 19 months less than for patients in cluster 5 (95% CI, 10.5-27.5 months). Multivariate analyses confirmed the independent prognostic value of the genomic clusters (low risk, P = .05; high risk, P = .02). The reproducibility and validity of these patterns of pathway deregulation in predicting relapse risk was established using related but not identical clusters in the independent validation cohort. The prognostic clinicogenomic clusters also have unique sensitivity patterns to commonly used cytotoxic therapies. CONCLUSIONS: These results provide preliminary evidence that incorporation of gene expression signatures into clinical risk stratification can refine prognosis. Prospective studies are needed to determine the value of this approach for individualizing therapeutic strategies.

Gene expression signatures that predict radiation exposure in mice and humans.

BACKGROUND: The capacity to assess environmental inputs to biological phenotypes is limited by methods that can accurately and quantitatively measure these contributions. One such example can be seen in the context of exposure to ionizing radiation. METHODS AND FINDINGS: We have made use of gene expression analysis of peripheral blood (PB) mononuclear cells to develop expression profiles that accurately reflect prior radiation exposure. We demonstrate that expression profiles can be developed that not only predict radiation exposure in mice but also distinguish the level of radiation exposure, ranging from 50 cGy to 1,000 cGy. Likewise, a molecular signature of radiation response developed solely from irradiated human patient samples can predict and distinguish irradiated human PB samples from nonirradiated samples with an accuracy of 90%, sensitivity of 85%, and specificity of 94%. We further demonstrate that a radiation profile developed in the mouse can correctly distinguish PB samples from irradiated and nonirradiated human patients with an accuracy of 77%, sensitivity of 82%, and specificity of 75%. Taken together, these data demonstrate that molecular profiles can be generated that are highly predictive of different levels of radiation exposure in mice and humans. CONCLUSIONS: We suggest that this approach, with additional refinement, could provide a method to assess the effects of various environmental inputs into biological phenotypes as well as providing a more practical application of a rapid molecular screening test for the diagnosis of radiation exposure.

Phase I trial of sequential low-dose 5-aza-2'-deoxycytidine plus high-dose intravenous bolus interleukin-2 in patients with melanoma or renal cell carcinoma.

PURPOSE: The silencing of gene expression through DNA methylation contributes to defects in antigen presentation and apoptosis in melanoma and renal cell cancer. To determine how a hypomethylating agent would modulate the toxicity and antitumor activity of immunotherapy, we initiated a phase I trial of 5-aza-2'-deoxycytidine (decitabine) plus high-dose interleukin 2 (IL-2). EXPERIMENTAL DESIGN: Patients received s.c. decitabine daily x 5 days on weeks 1 and 2 of a 12-week cycle. High-dose IL-2, consisting of two cycles of IL-2 600,000 IU/kg i.v. q8 hours x 14 doses separated by a 2-week break, was administered starting on week 3. Decitabine was escalated from 0.1 to 0.25 mg/kg. The hypomethylating activity of decitabine was assessed during cycle 1 by measuring hemoglobin F levels and changes in DNA methylation in peripheral blood mononuclear cells. RESULTS: Twenty-one patients with melanoma or renal cell cancer were enrolled. Decitabine did not alter the tolerability of IL-2 but caused grade 4 neutropenia in most patients. Grade 4 neutropenia lasting more than 7 days was the only dose-limiting toxicity, with a trend toward a higher incidence with increasing decitabine doses. Infection occurred in only one patient despite the high incidence of neutropenia, and granulocyte colony-stimulating factor use in several patients expedited neutrophil recovery. Decitabine augmented hemoglobin F levels and altered DNA methylation and gene expression in peripheral blood mononuclear cells in a dose-independent manner that overlapped with the administration of IL-2. Objective responses occurred in 31% of melanoma patients. CONCLUSIONS: Decitabine can be safely administered with high-dose IL-2 and may enhance the activity of IL-2 in melanoma.

Gene expression profiles of multiple breast cancer phenotypes and response to neoadjuvant chemotherapy.

PURPOSE: Breast cancer is a heterogeneous disease, and markers for disease subtypes and therapy response remain poorly defined. For that reason, we employed a prospective neoadjuvant study in locally advanced breast cancer to identify molecular signatures of gene expression correlating with known prognostic clinical phenotypes, such as inflammatory breast cancer or the presence of hypoxia. In addition, we defined molecular signatures that correlate with response to neoadjuvant chemotherapy. EXPERIMENTAL DESIGN: Tissue was collected under ultrasound guidance from patients with stage IIB/III breast cancer before four cycles of neoadjuvant liposomal doxorubicin paclitaxel chemotherapy combined with local whole breast hyperthermia. Gene expression analysis was done using Affymetrix U133 Plus 2.0 GeneChip arrays. RESULTS: Gene expression patterns were identified that defined the phenotypes of inflammatory breast cancer as well as tumor hypoxia. In addition, molecular signatures were identified that predicted the persistence of malignancy in the axillary lymph nodes after neoadjuvant chemotherapy. This persistent lymph node signature significantly correlated with disease-free survival in two separate large populations of breast cancer patients. CONCLUSIONS: Gene expression signatures have the capacity to identify clinically significant features of breast cancer and can predict which individual patients are likely to be resistant to neoadjuvant therapy, thus providing the opportunity to guide treatment decisions.