Multi-cohort analysis of host immune response identifies conserved protective and detrimental modules associated with severity across viruses.

Viral infections induce a conserved host response distinct from bacterial infections. We hypothesized that the conserved response is associated with disease severity and is distinct between patients with different outcomes. To test this, we integrated 4,780 blood transcriptome profiles from patients aged 0 to 90 years infected with one of 16 viruses, including SARS-CoV-2, Ebola, chikungunya, and influenza, across 34 cohorts from 18 countries, and single-cell RNA sequencing profiles of 702,970 immune cells from 289 samples across three cohorts. Severe viral infection was associated with increased hematopoiesis, myelopoiesis, and myeloid-derived suppressor cells. We identified protective and detrimental gene modules that defined distinct trajectories associated with mild versus severe outcomes. The interferon response was decoupled from the protective host response in patients with severe outcomes. These findings were consistent, irrespective of age and virus, and provide insights to accelerate the development of diagnostics and host-directed therapies to improve global pandemic preparedness.

Validation of Inflammopathic, Adaptive, and Coagulopathic Sepsis Endotypes in Coronavirus Disease 2019.

OBJECTIVES: Complex critical syndromes like sepsis and coronavirus disease 2019 may be composed of underling "endotypes," which may respond differently to treatment. The aim of this study was to test whether a previously defined bacterial sepsis endotypes classifier recapitulates the same clinical and immunological endotypes in coronavirus disease 2019. DESIGN: Prospective single-center observational cohort study. SETTING: Patients were enrolled in Athens, Greece, and blood was shipped to Inflammatix (Burlingame, CA) for analysis. PATIENTS: Adult patients within 24 hours of hospital admission with coronavirus disease 2019 confirmed by polymerase chain reaction and chest radiography. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We studied 97 patients with coronavirus disease 2019, of which 50 went on to severe respiratory failure (SRF) and 16 died. We applied a previously defined 33-messenger RNA classifier to assign endotype (Inflammopathic, Adaptive, or Coagulopathic) to each patient. We tested endotype status against other clinical parameters including laboratory values, severity scores, and outcomes. Patients were assigned as Inflammopathic (29%), Adaptive (44%), or Coagulopathic (27%), similar to our prior study in bacterial sepsis. Adaptive patients had lower rates of SRF and no deaths. Coagulopathic and Inflammopathic endotypes had 42% and 18% mortality rates, respectively. The Coagulopathic group showed highest d-dimers, and the Inflammopathic group showed highest C-reactive protein and interleukin-6 levels. CONCLUSIONS: Our predefined 33-messenger RNA endotypes classifier recapitulated immune phenotypes in viral sepsis (coronavirus disease 2019) despite its prior training and validation only in bacterial sepsis. Further work should focus on continued validation of the endotypes and their interaction with immunomodulatory therapy.

Nonclinical cardiovascular safety evaluation of romosozumab, an inhibitor of sclerostin for the treatment of osteoporosis in postmenopausal women at high risk of fracture.

Romosozumab (EVENITY™ [romosozumab-aqqg in the US]) is a humanized monoclonal antibody that inhibits sclerostin and has been approved in several countries for the treatment of osteoporosis in postmenopausal women at high risk of fracture. Sclerostin is expressed in bone and aortic vascular smooth muscle (AVSM). Its function in AVSM is unclear but it has been proposed to inhibit vascular calcification, atheroprogression, and inflammation. An increased incidence of positively adjudicated serious cardiovascular adverse events driven by an increase in myocardial infarction and stroke was observed in romosozumab-treated subjects in a clinical trial comparing alendronate with romosozumab (ARCH; NCT01631214) but not in a placebo-controlled trial (FRAME; NCT01575834). To investigate the effects of sclerostin inhibition with sclerostin antibody on the cardiovascular system, a comprehensive nonclinical toxicology package with additional cardiovascular studies was conducted. Although pharmacodynamic effects were observed in the bone, there were no functional, morphological, or transcriptional effects on the cardiovascular system in animal models in the presence or absence of atherosclerosis. These nonclinical studies did not identify evidence that proves the association between sclerostin inhibition and adverse cardiovascular function, increased cardiovascular calcification, and atheroprogression.

Dose-related differences in the pharmacodynamic and toxicologic response to a novel hyperglycosylated analog of recombinant human erythropoietin in Sprague-Dawley rats with similarly high hematocrit.

We recently reported results that erythropoiesis-stimulating agent (ESA)-related thrombotic toxicities in preclinical species were not solely dependent on a high hematocrit (HCT) but also associated with increased ESA dose level, dose frequency, and dosing duration. In this article, we conclude that sequelae of an increased magnitude of ESA-stimulated erythropoiesis potentially contributed to thrombosis in the highest ESA dose groups. The results were obtained from two investigative studies we conducted in Sprague-Dawley rats administered a low (no thrombotic toxicities) or high (with thrombotic toxicities) dose level of a hyperglycosylated analog of recombinant human erythropoietin (AMG 114), 3 times weekly for up to 9 days or for 1 month. Despite similarly increased HCT at both dose levels, animals in the high-dose group had an increased magnitude of erythropoiesis measured by spleen weights, splenic erythropoiesis, and circulating reticulocytes. Resulting prothrombotic risk factors identified predominantly or uniquely in the high-dose group were higher numbers of immature reticulocytes and nucleated red blood cells in circulation, severe functional iron deficiency, and increased intravascular destruction of iron-deficient reticulocyte/red blood cells. No thrombotic events were detected in rats dosed up to 9 days suggesting a sustained high HCT is a requisite cofactor for development of ESA-related thrombotic toxicities.

Cytokines associated with increased erythropoiesis in Sprague-Dawley rats administered a novel hyperglycosylated analog of recombinant human erythropoietin.

We previously reported an increased incidence of thrombotic toxicities in Sprague-Dawley rats administered the highest dose level of a hyperglycosylated analog of recombinant human erythropoietin (AMG 114) for 1 month as not solely dependent on high hematocrit (HCT). Thereafter, we identified increased erythropoiesis as a prothrombotic risk factor increased in the AMG 114 high-dose group with thrombotic toxicities, compared to a low-dose group with no toxicities but similar HCT. Here, we identified pleiotropic cytokines as prothrombotic factors associated with AMG 114 dose level. Before a high HCT was achieved, rats in the AMG 114 high, but not the low-dose group, had imbalanced hemostasis (increased von Willebrand factor and prothrombin time, decreased antithrombin III) coexistent with cytokines implicated in thrombosis: monocyte chemotactic protein 1 (MCP-1), MCP-3, tissue inhibitor of metalloproteinases 1, macrophage inhibitory protein-2, oncostatin M, T-cell-specific protein, stem cell factor, vascular endothelial growth factor, and interleukin-11. While no unique pathway to erythropoiesis stimulating agent-related thrombosis was identified, cytokines associated with increased erythropoiesis contributed to a prothrombotic intravascular environment in the AMG 114 high-dose group, but not in lower dose groups with a similar high HCT.

A machine learning classifier using 33 host immune response mRNAs accurately distinguishes viral and non-viral acute respiratory illnesses in nasal swab samples.

BACKGROUND: Viral acute respiratory illnesses (viral ARIs) contribute significantly to human morbidity and mortality worldwide, but their successful treatment requires timely diagnosis of viral etiology, which is complicated by overlap in clinical presentation with the non-viral ARIs. Multiple pandemics in the twenty-first century to date have further highlighted the unmet need for effective monitoring of clinically relevant emerging viruses. Recent studies have identified conserved host response to viral infections in the blood. METHODS: We hypothesize that a similarly conserved host response in nasal samples can be utilized for diagnosis and to rule out viral infection in symptomatic patients when current diagnostic tests are negative. Using a multi-cohort analysis framework, we analyzed 1555 nasal samples across 10 independent cohorts dividing them into training and validation. RESULTS: Using six of the datasets for training, we identified 119 genes that are consistently differentially expressed in viral ARI patients (N = 236) compared to healthy controls (N = 146) and further down-selected 33 genes for classifier development. The resulting locked logistic regression-based classifier using the 33-mRNAs had AUC of 0.94 and 0.89 in the six training and four validation datasets, respectively. Furthermore, we found that although trained on healthy controls only, in the four validation datasets, the 33-mRNA classifier distinguished viral ARI from both healthy or non-viral ARI samples with > 80% specificity and sensitivity, irrespective of age, viral type, and viral load. Single-cell RNA-sequencing data showed that the 33-mRNA signature is dominated by macrophages and neutrophils in nasal samples. CONCLUSION: This proof-of-concept signature has potential to be adapted as a clinical point-of-care test ('RespVerity') to improve the diagnosis of viral ARIs.

A robust gene expression signature for NASH in liver expression data.

Non-Alcoholic Fatty Liver Disease (NAFLD) is a progressive liver disease that affects up to 30% of worldwide population, of which up to 25% progress to Non-Alcoholic SteatoHepatitis (NASH), a severe form of the disease that involves inflammation and predisposes the patient to liver cirrhosis. Despite its epidemic proportions, there is no reliable diagnostics that generalizes to global patient population for distinguishing NASH from NAFLD. We performed a comprehensive multicohort analysis of publicly available transcriptome data of liver biopsies from Healthy Controls (HC), NAFLD and NASH patients. Altogether we analyzed 812 samples from 12 different datasets across 7 countries, encompassing real world patient heterogeneity. We used 7 datasets for discovery and 5 datasets were held-out for independent validation. Altogether we identified 130 genes significantly differentially expressed in NASH versus a mixed group of NAFLD and HC. We show that our signature is not driven by one particular group (NAFLD or HC) and reflects true biological signal. Using a forward search we were able to downselect to a parsimonious set of 19 mRNA signature with mean AUROC of 0.98 in discovery and 0.79 in independent validation. Methods for consistent diagnosis of NASH relative to NAFLD are urgently needed. We showed that gene expression data combined with advanced statistical methodology holds the potential to serve basis for development of such diagnostic tests for the unmet clinical need.

A Multi-mRNA Prognostic Signature for Anti-TNFα Therapy Response in Patients with Inflammatory Bowel Disease.

BACKGROUND: Anti-TNF-alpha (anti-TNFα) therapies have transformed the care and management of inflammatory bowel disease (IBD). However, they are expensive and ineffective in greater than 50% of patients, and they increase the risk of infections, liver issues, arthritis, and lymphoma. With 1.6 million Americans suffering from IBD and global prevalence on the rise, there is a critical unmet need in the use of anti-TNFα therapies: a test for the likelihood of therapy response. Here, as a proof-of-concept, we present a multi-mRNA signature for predicting response to anti-TNFα treatment to improve the efficacy and cost-to-benefit ratio of these biologics. METHODS: We surveyed public data repositories and curated four transcriptomic datasets (n = 136) from colonic and ileal mucosal biopsies of IBD patients (pretreatment) who were subjected to anti-TNFα therapy and subsequently adjudicated for response. We applied a multicohort analysis with a leave-one-study-out (LOSO) approach, MetaIntegrator, to identify significant differentially expressed (DE) genes between responders and non-responders and then used a greedy forward search to identify a parsimonious gene signature. We then calculated an anti-TNFα response (ATR) score based on this parsimonious gene signature to predict responder status and assessed discriminatory performance via an area-under-receiver operating-characteristic curve (AUROC). RESULTS: We identified 324 significant DE genes between responders and non-responders. The greedy forward search yielded seven genes that robustly distinguish anti-TNFα responders from non-responders, with an AUROC of 0.88 (95% CI: 0.70-1). The Youden index yielded a mean sensitivity of 91%, mean specificity of 76%, and mean accuracy of 86%. CONCLUSIONS: Our findings suggest that there is a robust transcriptomic signature for predicting anti-TNFα response in mucosal biopsies from IBD patients prior to treatment initiation. This seven-gene signature should be further investigated for its potential to be translated into a predictive test for clinical use.

The Optimization and Biological Significance of a 29-Host-Immune-mRNA Panel for the Diagnosis of Acute Infections and Sepsis.

In response to the unmet need for timely accurate diagnosis and prognosis of acute infections and sepsis, host-immune-response-based tests are being developed to help clinicians make more informed decisions including prescribing antimicrobials, ordering additional diagnostics, and assigning level of care. One such test (InSep™, Inflammatix, Inc.) uses a 29-mRNA panel to determine the likelihood of bacterial infection, the separate likelihood of viral infection, and the risk of physiologic decompensation (severity of illness). The test, being implemented in a rapid point-of-care platform with a turnaround time of 30 min, enables accurate and rapid diagnostic use at the point of impact. In this report, we provide details on how the 29-biomarker signature was chosen and optimized, together with its molecular, immunological, and medical significance to better understand the pathophysiological relevance of altered gene expression in disease. We synthesize key results obtained from gene-level functional annotations, geneset-level enrichment analysis, pathway-level analysis, and gene-network-level upstream regulator analysis. Emerging findings are summarized as hallmarks on immune cell interaction, inflammatory mediators, cellular metabolism and homeostasis, immune receptors, intracellular signaling and antiviral response; and converging themes on neutrophil degranulation and activation involved in immune response, interferon, and other signaling pathways.

Transcriptomic similarities and differences in host response between SARS-CoV-2 and other viral infections.

The pandemic 2019 novel coronavirus disease (COVID-19) shares certain clinical characteristics with other acute viral infections. We studied the whole-blood transcriptomic host response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using RNAseq from 24 healthy controls and 62 prospectively enrolled patients with COVID-19. We then compared these data to non-COVID-19 viral infections, curated from 23 independent studies profiling 1,855 blood samples covering six viruses (influenza, respiratory syncytial virus (RSV), human rhinovirus (HRV), severe acute respiratory syndrome coronavirus 1 (SARS-CoV-1), Ebola, dengue). We show gene expression changes in COVID-19 versus non-COVID-19 viral infections are highly correlated (r = 0.74, p < 0.001). However, we also found 416 genes specific to COVID-19. Inspection of top genes revealed dynamic immune evasion and counter host responses specific to COVID-19. Statistical deconvolution of cell proportions maps many cell type proportions concordantly shifting. Discordantly increased in COVID-19 were CD56bright natural killer cells and M2 macrophages. The concordant and discordant responses mapped out here provide a window to explore the pathophysiology of the host response to SARS-CoV-2.

Universal Toxicity Gene Signatures for Early Identification of Drug-Induced Tissue Injuries in Rats.

A new safety testing paradigm that relies on gene expression biomarker panels was developed to easily and quickly identify drug-induced injuries across tissues in rats prior to drug candidate selection. Here, we describe the development, qualification, and implementation of gene expression signatures that diagnose tissue degeneration/necrosis for use in early rat safety studies. Approximately 400 differentially expressed genes were first identified that were consistently regulated across 4 prioritized tissues (liver, kidney, heart, and skeletal muscle), following injuries induced by known toxicants. Hundred of these "universal" genes were chosen for quantitative PCR, and the most consistent and robustly responding transcripts selected, resulting in a final 22-gene set from which unique sets of 12 genes were chosen as optimal for each tissue. The approach was extended across 4 additional tissues (pancreas, gastrointestinal tract, bladder, and testes) where toxicities are less common. Mathematical algorithms were generated to convert each tissue's 12-gene expression values to a single metric, scaled between 0 and 1, and a positive threshold set. For liver, kidney, heart, and skeletal muscle, this was established using a training set of 22 compounds and performance determined by testing a set of approximately 100 additional compounds, resulting in 74%-94% sensitivity and 94%-100% specificity for liver, kidney, and skeletal muscle, and 54%-62% sensitivity and 95%-98% specificity for heart. Similar performance was observed across a set of 15 studies for pancreas, gastrointestinal tract, bladder, and testes. Bundled together, we have incorporated these tissue signatures into a 4-day rat study, providing a rapid assessment of commonly seen compound liabilities to guide selection of lead candidates without the necessity to perform time-consuming histopathologic analyses.

A compound attributes-based predictive model for drug induced liver injury in humans.

Drug induced liver injury (DILI) is one of the key safety concerns in drug development. To assess the likelihood of drug candidates with potential adverse reactions of liver, we propose a compound attributes-based approach to predicting hepatobiliary disorders that are routinely reported to US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). Specifically, we developed a support vector machine (SVM) model with recursive feature extraction, based on physicochemical and structural properties of compounds as model input. Cross validation demonstrates that the predictive model has a robust performance with averaged 70% of both sensitivity and specificity over 500 trials. An independent validation was performed on public benchmark drugs and the results suggest potential utility of our model for identifying safety alerts. This in silico approach, upon further validation, would ultimately be implemented, together with other in vitro safety assays, for screening compounds early in drug development.

Biomarkers of genome instability in normal mammalian genomes following drug-induced replication stress.

Genome instability is a hallmark of most human cancers and is exacerbated following replication stress. However, the effects that drugs/xenobiotics have in promoting genome instability including chromosomal structural rearrangements in normal cells are not currently assessed in the genetic toxicology battery. Here, we show that drug-induced replication stress leads to increased genome instability in vitro using proliferating primary human cells as well as in vivo in rat bone marrow (BM) and duodenum (DD). p53-binding protein 1 (53BP1, biomarker of DNA damage repair) nuclear bodies were increased in a dose-dependent manner in normal proliferating human mammary epithelial fibroblasts following treatment with compounds traditionally classified as either genotoxic (hydralazine) and nongenotoxic (low-dose aphidicolin, duvelisib, idelalisib, and amiodarone). Comparatively, no increases in 53BP1 nuclear bodies were observed in nonproliferating cells. Negative control compounds (mannitol, alosteron, diclofenac, and zonisamide) not associated with cancer risk did not induce 53BP1 nuclear bodies in any cell type. Finally, we studied the in vivo genomic consequences of drug-induced replication stress in rats treated with 10 mg/kg of cyclophosphamide for up to 14 days followed by polymerase chain reaction-free whole genome sequencing (30X coverage) of BM and DD cells. Cyclophosphamide induced chromosomal structural rearrangements at an average of 90 genes, including 40 interchromosomal/intrachromosomal translocations, within 2 days of treatment. Collectively, these data demonstrate that this drug-induced genome instability test (DiGIT) can reveal potential adverse effects of drugs not otherwise informed by standard genetic toxicology testing batteries. These efforts are aligned with the food and drug administration's (FDA's) predictive toxicology roadmap initiative.

Temporal pattern of skeletal muscle gene expression following endurance exercise in Alaskan sled dogs.

Muscle responses to exercise are complex and include acute responses to exercise-induced injury, as well as longer term adaptive training responses. Using Alaskan sled dogs as an experimental model, changes in muscle gene expression were analyzed to test the hypotheses that important regulatory elements of the muscle's adaptation to exercise could be identified based on the temporal pattern of gene expression. Dogs were randomly assigned to undertake a 160-km run (n=9), or to remain at rest (n=4). Biceps femoris muscle was obtained from the unexercised dogs and two dogs at each of 2, 6, and 12 h after the exercise, and from three dogs 24 h after exercise. RNA was extracted and microarray analysis used to define gene transcriptional changes. The changes in gene expression after exercise occurred in a temporal pattern. Overall, 569, 469, 316, and 223 transcripts were differentially expressed at 2, 6, 12, and 24 h postexercise, respectively, compared with unexercised dogs (based on Por=1.5). Increases in a number of known transcriptional regulators, including peroxisome proliferator-activated receptor-alpha, cAMP-responsive element modulator, and CCAAT enhancer binding protein-delta, and potential signaling molecules, including brain-derived neurotrophic factor, dermokine, and suprabasin, were observed 2 h after exercise. Biological functional analysis suggested changes in expression of genes with known functional relationships, including genes involved in muscle remodeling and growth, intermediary metabolism, and immune regulation. Sustained endurance exercise by Alaskan sled dogs induces coordinated changes in gene expression with a clear temporal pattern. RNA expression profiling has the potential to identify novel regulatory mechanisms and responses to exercise stimuli.

Genome-scale metabolic model of the rat liver predicts effects of diet restriction.

Mapping network analysis in cells and tissues can provide insights into metabolic adaptations to changes in external environment, pathological conditions, and nutrient deprivation. Here, we reconstructed a genome-scale metabolic network of the rat liver that will allow for exploration of systems-level physiology. The resulting in silico model (iRatLiver) contains 1,882 reactions, 1,448 metabolites, and 994 metabolic genes. We then used this model to characterize the response of the liver's energy metabolism to a controlled perturbation in diet. Transcriptomics data were collected from the livers of Sprague Dawley rats at 4 or 14 days of being subjected to 15%, 30%, or 60% diet restriction. These data were integrated with the iRatLiver model to generate condition-specific metabolic models, allowing us to explore network differences under each condition. We observed different pathway usage between early and late time points. Network analysis identified several highly connected "hub" genes (Pklr, Hadha, Tkt, Pgm1, Tpi1, and Eno3) that showed differing trends between early and late time points. Taken together, our results suggest that the liver's response varied with short- and long-term diet restriction. More broadly, we anticipate that the iRatLiver model can be exploited further to study metabolic changes in the liver under other conditions such as drug treatment, infection, and disease.

PPARalpha siRNA-treated expression profiles uncover the causal sufficiency network for compound-induced liver hypertrophy.

Uncovering pathways underlying drug-induced toxicity is a fundamental objective in the field of toxicogenomics. Developing mechanism-based toxicity biomarkers requires the identification of such novel pathways and the order of their sufficiency in causing a phenotypic response. Genome-wide RNA interference (RNAi) phenotypic screening has emerged as an effective tool in unveiling the genes essential for specific cellular functions and biological activities. However, eliciting the relative contribution of and sufficiency relationships among the genes identified remains challenging. In the rodent, the most widely used animal model in preclinical studies, it is unrealistic to exhaustively examine all potential interactions by RNAi screening. Application of existing computational approaches to infer regulatory networks with biological outcomes in the rodent is limited by the requirements for a large number of targeted permutations. Therefore, we developed a two-step relay method that requires only one targeted perturbation for genome-wide de novo pathway discovery. Using expression profiles in response to small interfering RNAs (siRNAs) against the gene for peroxisome proliferator-activated receptor alpha (Ppara), our method unveiled the potential causal sufficiency order network for liver hypertrophy in the rodent. The validity of the inferred 16 causal transcripts or 15 known genes for PPARalpha-induced liver hypertrophy is supported by their ability to predict non-PPARalpha-induced liver hypertrophy with 84% sensitivity and 76% specificity. Simulation shows that the probability of achieving such predictive accuracy without the inferred causal relationship is exceedingly small (p < 0.005). Five of the most sufficient causal genes have been previously disrupted in mouse models; the resulting phenotypic changes in the liver support the inferred causal roles in liver hypertrophy. Our results demonstrate the feasibility of defining pathways mediating drug-induced toxicity from siRNA-treated expression profiles. When combined with phenotypic evaluation, our approach should help to unleash the full potential of siRNAs in systematically unveiling the molecular mechanism of biological events.

A gene-expression signature as a predictor of survival in breast cancer.

BACKGROUND: A more accurate means of prognostication in breast cancer will improve the selection of patients for adjuvant systemic therapy. METHODS: Using microarray analysis to evaluate our previously established 70-gene prognosis profile, we classified a series of 295 consecutive patients with primary breast carcinomas as having a gene-expression signature associated with either a poor prognosis or a good prognosis. All patients had stage I or II breast cancer and were younger than 53 years old; 151 had lymph-node-negative disease, and 144 had lymph-node-positive disease. We evaluated the predictive power of the prognosis profile using univariable and multivariable statistical analyses. RESULTS: Among the 295 patients, 180 had a poor-prognosis signature and 115 had a good-prognosis signature, and the mean (+/-SE) overall 10-year survival rates were 54.6+/-4.4 percent and 94.5+/-2.6 percent, respectively. At 10 years, the probability of remaining free of distant metastases was 50.6+/-4.5 percent in the group with a poor-prognosis signature and 85.2+/-4.3 percent in the group with a good-prognosis signature. The estimated hazard ratio for distant metastases in the group with a poor-prognosis signature, as compared with the group with the good-prognosis signature, was 5.1 (95 percent confidence interval, 2.9 to 9.0; P<0.001). This ratio remained significant when the groups were analyzed according to lymph-node status. Multivariable Cox regression analysis showed that the prognosis profile was a strong independent factor in predicting disease outcome. CONCLUSIONS: The gene-expression profile we studied is a more powerful predictor of the outcome of disease in young patients with breast cancer than standard systems based on clinical and histologic criteria.

A cell proliferation signature is a marker of extremely poor outcome in a subpopulation of breast cancer patients.

Breast cancer comprises a group of distinct subtypes that despite having similar histologic appearances, have very different metastatic potentials. Being able to identify the biological driving force, even for a subset of patients, is crucially important given the large population of women diagnosed with breast cancer. Here, we show that within a subset of patients characterized by relatively high estrogen receptor expression for their age, the occurrence of metastases is strongly predicted by a homogeneous gene expression pattern almost entirely consisting of cell cycle genes (5-year odds ratio of metastasis, 24.0; 95% confidence interval, 6.0-95.5). Overexpression of this set of genes is clearly associated with an extremely poor outcome, with the 10-year metastasis-free probability being only 24% for the poor group, compared with 85% for the good group. In contrast, this gene expression pattern is much less correlated with the outcome in other patient subpopulations. The methods described here also illustrate the value of combining clinical variables, biological insight, and machine-learning to dissect biological complexity. Our work presented here may contribute a crucial step towards rational design of personalized treatment.

Development of an approach for ab initio estimation of compound-induced liver injury based on global gene transcriptional profiles.

Toxicity is a major cause of failure in drug development. A toxicogenomic approach may provide a powerful tool for better assessing the potential toxicity of drug candidates. Several approaches have been reported for predicting hepatotoxicity based on reference compounds with well-studied toxicity mechanisms. We developed a new approach for assessing compound-induced liver injury without prior knowledge of a compound's mechanism of toxicity. Using samples from rodents treated with 49 known liver toxins and 10 compounds without known liver toxicity, we derived a hepatotoxicity score as a single quantitative measurement for assessing the degree of induced liver damage. Combining the sensitivity of the hepatotoxicity score and the power of a machine learning algorithm, we then built a model to predict compound-induced liver injury based on 212 expression profiles. As estimated in an independent data set of 54 expression profiles, the built model predicted compound-induced liver damage with 90.9% sensitivity and 88.4% specificity. Our findings illustrate the feasibility of ab initio estimation of liver toxicity based on transcriptional profiles.

Time-dependent cellular and transcriptional changes in the osteoblast lineage associated with sclerostin antibody treatment in ovariectomized rats.

Inhibition of sclerostin with sclerostin antibody (Scl-Ab) has been shown to stimulate bone formation, decrease bone resorption, and increase bone mass in both animals and humans. To obtain insight into the temporal cellular and transcriptional changes in the osteoblast (OB) lineage associated with long-term Scl-Ab treatment, stereological and transcriptional analyses of the OB lineage were performed on lumbar vertebrae from aged ovariectomized rats. Animals were administered Scl-Ab 3 or 50mg/kg/wk or vehicle (VEH) for up to 26weeks (d183), followed by a treatment-free period (TFP). At 50mg/kg/wk, bone volume (BV/total volume [TV]) increased through d183 and declined during the TFP. Bone formation rate (BFR/bone surface [BS]) and total OB number increased through d29, then progressively declined, coincident with a decrease in total osteoprogenitor (OP) numbers from d29 through d183. Analysis of differentially expressed genes (DEGs) from microarray analysis of mRNA isolated from laser capture microdissection samples enriched for OB, lining cells, and osteocytes (OCy) revealed modules of genes that correlated with BFR/BS, BV/TV, and osteoblastic surface (Ob.S)/BS. Expression change of canonical Wnt target genes was similar in all three cell types at d8, including upregulation of Twist1 and Wisp1. At d29, the pattern of Wnt target gene expression changed in the OCy, with Twist1 returning to VEH level, sustained upregulation of Wisp1, and upregulation of several other Wnt targets that continued into the TFP. Predicted activation of pathways recognized to integrate with and regulate canonical Wnt signaling were also activated at d29 in the OCy. The most significantly affected pathways represented transcription factor signaling known to inhibit cell cycle progression (notably p53) and mitogenesis (notably c-Myc). These changes occurred at the time of peak BFR/BS and continued as BFR/BS declined during treatment, then trended toward VEH level in the TFP. Concurrent with this transcriptional switch was a reduction in OP numbers, an effect that would ultimately limit bone formation. This study confirms that the initial transcriptional response in response to Scl-Ab is activation of canonical Wnt signaling and the data demonstrate that there is induction of additional regulatory pathways in OCy with long-term treatment. The interactions between Wnt and p53/c-Myc signaling may be key in limiting OP populations, thus contributing to self-regulation of bone formation with continued Scl-Ab administration.