Gene-signature-derived IC50s/EC50s reflect the potency of causative upstream targets and downstream phenotypes.

Multiplexed gene-signature-based phenotypic assays are increasingly used for the identification and profiling of small molecule-tool compounds and drugs. Here we introduce a method (provided as R-package) for the quantification of the dose-response potency of a gene-signature as EC50 and IC50 values. Two signaling pathways were used as models to validate our methods: beta-adrenergic agonistic activity on cAMP generation (dedicated dataset generated for this study) and EGFR inhibitory effect on cancer cell viability. In both cases, potencies derived from multi-gene expression data were highly correlated with orthogonal potencies derived from cAMP and cell growth readouts, and superior to potencies derived from single individual genes. Based on our results we propose gene-signature potencies as a novel valid alternative for the quantitative prioritization, optimization and development of novel drugs.

DRUG-seq for miniaturized high-throughput transcriptome profiling in drug discovery.

Here we report Digital RNA with pertUrbation of Genes (DRUG-seq), a high-throughput platform for drug discovery. Pharmaceutical discovery relies on high-throughput screening, yet current platforms have limited readouts. RNA-seq is a powerful tool to investigate drug effects using transcriptome changes as a proxy, yet standard library construction is costly. DRUG-seq captures transcriptional changes detected in standard RNA-seq at 1/100th the cost. In proof-of-concept experiments profiling 433 compounds across 8 doses, transcription profiles generated from DRUG-seq successfully grouped compounds into functional clusters by mechanism of actions (MoAs) based on their intended targets. Perturbation differences reflected in transcriptome changes were detected for compounds engaging the same target, demonstrating the value of using DRUG-seq for understanding on and off-target activities. We demonstrate DRUG-seq captures common mechanisms, as well as differences between compound treatment and CRISPR on the same target. DRUG-seq provides a powerful tool for comprehensive transcriptome readout in a high-throughput screening environment.

Reverse translation of adverse event reports paves the way for de-risking preclinical off-targets.

The Food and Drug Administration Adverse Event Reporting System (FAERS) remains the primary source for post-marketing pharmacovigilance. The system is largely un-curated, unstandardized, and lacks a method for linking drugs to the chemical structures of their active ingredients, increasing noise and artefactual trends. To address these problems, we mapped drugs to their ingredients and used natural language processing to classify and correlate drug events. Our analysis exposed key idiosyncrasies in FAERS, for example reports of thalidomide causing a deadly ADR when used against myeloma, a likely result of the disease itself; multiplications of the same report, unjustifiably increasing its importance; correlation of reported ADRs with public events, regulatory announcements, and with publications. Comparing the pharmacological, pharmacokinetic, and clinical ADR profiles of methylphenidate, aripiprazole, and risperidone, and of kinase drugs targeting the VEGF receptor, demonstrates how underlying molecular mechanisms can emerge from ADR co-analysis. The precautions and methods we describe may enable investigators to avoid confounding chemistry-based associations and reporting biases in FAERS, and illustrate how comparative analysis of ADRs can reveal underlying mechanisms.

GH deficiency status combined with GH receptor polymorphism affects response to GH in children.

Meta-analysis has shown a modest improvement in first-year growth response to recombinant human GH (r-hGH) for carriers of the exon 3-deleted GH receptor (GHRd3) polymorphism but with significant interstudy variability. The associations between GHRd3 and growth response to r-hGH over 3 years in relation to severity of GH deficiency (GHD) were investigated in patients from 14 countries. Treatment-naïve pre-pubertal children with GHD were enrolled from the PREDICT studies (NCT00256126 and NCT00699855), categorized by peak GH level (peak GH) during provocation test: ≤4 µg/l (severe GHD; n=45) and >4 to <10 µg/l mild GHD; n=49) and genotyped for the GHRd3 polymorphism (full length (fl/fl, fl/d3, d3/d3). Gene expression (GE) profiles were characterized at baseline. Changes in growth (height (cm) and SDS) over 3 years were measured. There was a dichotomous influence of GHRd3 polymorphism on response to r-hGH, dependent on peak GH level. GH peak level (higher vs lower) and GHRd3 (fl/fl vs d3 carriers) combined status was associated with height change over 3 years (P<0.05). GHRd3 carriers with lower peak GH had lower growth than subjects with fl/fl (median difference after 3 years -3.3 cm; -0.3 SDS). Conversely, GHRd3 carriers with higher peak GH had better growth (+2.7 cm; +0.2 SDS). Similar patterns were observed for GH-dependent biomarkers. GE profiles were significantly different between the groups, indicating that the interaction between GH status and GHRd3 carriage can be identified at a transcriptomic level. This study demonstrates that responses to r-hGH depend on the interaction between GHD severity and GHRd3 carriage.

Prognostic biomarkers of IFNb therapy in multiple sclerosis patients.

BACKGROUND: Interferon beta (IFNb) reduces relapse frequency and disability progression in patients with multiple sclerosis (MS). OBJECTIVES: Early identification of prognostic biomarkers of IFNb-treated patients will allow more effective management of MS. METHODS: The IMPROVE study evaluated subcutaneous IFNb versus placebo in 180 patients with relapsing-remitting MS. Magnetic resonance imaging scans, clinical assessments, and blood samples were obtained at baseline and every 4 weeks from every participant. Thirty-nine biomarkers (32 transcripts; seven proteins) were studied in 155 patients from IMPROVE. Therapeutic response was defined by absence of new combined unique lesions, relapses, and sustained increase in Expanded Disability Status Scale over 1 year. A machine learning approach was used to examine the association between biomarker expression and treatment response. RESULTS: While baseline levels of individual genes were relatively poor predictors, combinations of three genes were able to identify subjects with sub-optimal therapeutic responses. The triplet CASP2/IRF4/IRF6, previously identified in an independent dataset, was tested among other combinations. This triplet showed acceptable predictive accuracy (0.68) and specificity (0.88), but had relatively low sensitivity (0.22) resulting in an area under the curve (AUC) of 0.63. Other combinations of biomarkers resulted in AUC of up to 0.80 (e.g. CASP2/IL10/IL12Rb1). CONCLUSIONS: Baseline expression, or induction ratios, of specific gene combinations correlate with future therapeutic response to IFNb, and have the potential to be prognostically useful.

Src is activated by the nuclear receptor peroxisome proliferator-activated receptor ß/δ in ultraviolet radiation-induced skin cancer.

Although non-melanoma skin cancer (NMSC) is the most common human cancer and its incidence continues to rise worldwide, the mechanisms underlying its development remain incompletely understood. Here, we unveil a cascade of events involving peroxisome proliferator-activated receptor (PPAR) ß/δ and the oncogene Src, which promotes the development of ultraviolet (UV)-induced skin cancer in mice. UV-induced PPARß/δ activity, which directly stimulated Src expression, increased Src kinase activity and enhanced the EGFR/Erk1/2 signalling pathway, resulting in increased epithelial-to-mesenchymal transition (EMT) marker expression. Consistent with these observations, PPARß/δ-null mice developed fewer and smaller skin tumours, and a PPARß/δ antagonist prevented UV-dependent Src stimulation. Furthermore, the expression of PPARß/δ positively correlated with the expression of SRC and EMT markers in human skin squamous cell carcinoma (SCC), and critically, linear models applied to several human epithelial cancers revealed an interaction between PPARß/δ and SRC and TGFß1 transcriptional levels. Taken together, these observations motivate the future evaluation of PPARß/δ modulators to attenuate the development of several epithelial cancers.

A stroma-related gene signature predicts resistance to neoadjuvant chemotherapy in breast cancer.

To better understand the relationship between tumor-host interactions and the efficacy of chemotherapy, we have developed an analytical approach to quantify several biological processes observed in gene expression data sets. We tested the approach on tumor biopsies from individuals with estrogen receptor-negative breast cancer treated with chemotherapy. We report that increased stromal gene expression predicts resistance to preoperative chemotherapy with 5-fluorouracil, epirubicin and cyclophosphamide (FEC) in subjects in the EORTC 10994/BIG 00-01 trial. The predictive value of the stromal signature was successfully validated in two independent cohorts of subjects who received chemotherapy but not in an untreated control group, indicating that the signature is predictive rather than prognostic. The genes in the signature are expressed in reactive stroma, according to reanalysis of data from microdissected breast tumor samples. These findings identify a previously undescribed resistance mechanism to FEC treatment and suggest that antistromal agents may offer new ways to overcome resistance to chemotherapy.

Omics meets hypothesis-driven research. Partnership for innovative discoveries in vascular biology and angiogenesis.

The emergence of omics technologies allowing the global analysis of a given biological or molecular system, rather than the study of its individual components, has revolutionized biomedical research, including cardiovascular medicine research in the past decade. These developments raised the prospect that classical, hypothesis-driven, single gene-based approaches may soon become obsolete. The experience accumulated so far, however, indicates that omic technologies only represent tools similar to those classically used by scientists in the past and nowadays, to make hypothesis and build models, with the main difference that they generate large amounts of unbiased information. Thus, omics and classical hypothesis-driven research are rather complementary approaches with the potential to effectively synergize to boost research in many fields, including cardiovascular medicine. In this article we discuss some general aspects of omics approaches, and review contributions in three areas of vascular biology, thrombosis and haemostasis, atherosclerosis and angiogenesis, in which omics approaches have already been applied (vasculomics).

CYR61 and alphaVbeta5 integrin cooperate to promote invasion and metastasis of tumors growing in preirradiated stroma.

Radiotherapy is widely used to treat human cancer. Patients locally recurring after radiotherapy, however, have increased risk of metastatic progression and poor prognosis. The clinical management of postradiation recurrences remains an unresolved issue. Tumors growing in preirradiated tissues have an increased fraction of hypoxic cells and are more metastatic, a condition known as tumor bed effect. The transcription factor hypoxia inducible factor (HIF)-1 promotes invasion and metastasis of hypoxic tumors, but its role in the tumor bed effect has not been reported. Here, we show that tumor cells derived from SCCVII and HCT116 tumors growing in a preirradiated bed, or selected in vitro through repeated cycles of severe hypoxia, retain invasive and metastatic capacities when returned to normoxia. HIF activity, although facilitating metastatic spreading of tumors growing in a preirradiated bed, is not essential. Through gene expression profiling and gain- and loss-of-function experiments, we identified the matricellular protein CYR61 and alphaVbeta5 integrin as proteins cooperating to mediate these effects. The anti-alphaV integrin monoclonal antibody 17E6 and the small molecular alphaVbeta3/alphaVbeta5 integrin inhibitor EMD121974 suppressed invasion and metastasis induced by CYR61 and attenuated metastasis of tumors growing within a preirradiated field. These results represent a conceptual advance to the understanding of the tumor bed effect and identify CYR61 and alphaVbeta5 integrin as proteins that cooperate to mediate metastasis. They also identify alphaV integrin inhibition as a potential therapeutic approach for preventing metastasis in patients at risk for postradiation recurrences.

Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures.

INTRODUCTION: Breast cancer subtyping and prognosis have been studied extensively by gene expression profiling, resulting in disparate signatures with little overlap in their constituent genes. Although a previous study demonstrated a prognostic concordance among gene expression signatures, it was limited to only one dataset and did not fully elucidate how the different genes were related to one another nor did it examine the contribution of well-known biological processes of breast cancer tumorigenesis to their prognostic performance. METHOD: To address the above issues and to further validate these initial findings, we performed the largest meta-analysis of publicly available breast cancer gene expression and clinical data, which are comprised of 2,833 breast tumors. Gene coexpression modules of three key biological processes in breast cancer (namely, proliferation, estrogen receptor [ER], and HER2 signaling) were used to dissect the role of constituent genes of nine prognostic signatures. RESULTS: Using a meta-analytical approach, we consolidated the signatures associated with ER signaling, ERBB2 amplification, and proliferation. Previously published expression-based nomenclature of breast cancer 'intrinsic' subtypes can be mapped to the three modules, namely, the ER-/HER2- (basal-like), the HER2+ (HER2-like), and the low- and high-proliferation ER+/HER2- subtypes (luminal A and B). We showed that all nine prognostic signatures exhibited a similar prognostic performance in the entire dataset. Their prognostic abilities are due mostly to the detection of proliferation activity. Although ER- status (basal-like) and ERBB2+ expression status correspond to bad outcome, they seem to act through elevated expression of proliferation genes and thus contain only indirect information about prognosis. Clinical variables measuring the extent of tumor progression, such as tumor size and nodal status, still add independent prognostic information to proliferation genes. CONCLUSION: This meta-analysis unifies various results of previous gene expression studies in breast cancer. It reveals connections between traditional prognostic factors, expression-based subtyping, and prognostic signatures, highlighting the important role of proliferation in breast cancer prognosis.

Validation of gene signatures that predict the response of breast cancer to neoadjuvant chemotherapy: a substudy of the EORTC 10994/BIG 00-01 clinical trial.

BACKGROUND: We have previously described gene-expression signatures that predict growth inhibitory and cytotoxic effects of common chemotherapeutic drugs in vitro. The aim of this study was to confirm the validity of these gene-expression signatures in a large series of patients with oestrogen-receptor-negative breast tumours who were treated in a phase III neoadjuvant clinical trial. METHODS: This trial compares a non-taxane regimen (fluorouracil, epirubicin, and cyclophosphamide [FEC] for six cycles) with a taxane regimen (docetaxel for three cycles followed by epirubicin plus docetaxel [TET] for three cycles) in women with oestrogen-receptor-negative breast cancer. The primary endpoint of the study is the difference in progression-free survival based on TP53 status and will be reported later. Predicting response with gene signatures was a planned secondary endpoint of the trial and is reported here. Pathological complete response, defined as complete disappearance of the tumour with no more than a few scattered tumour cells detected by the pathologist in the resection specimen, was used to assess chemosensitivity. RNA was prepared from sections of frozen biopsies taken at diagnosis and hybridised to Affymetrix X3P microarrays. In-vitro single-agent drug sensitivity signatures were combined to obtain FEC and TET regimen-specific signatures. This study is registered on the clinical trials site of the US National Cancer Institute website http://www.clinicaltrials.gov/ct/show/NCT00017095. FINDINGS: Of 212 patients with oestrogen-receptor-negative tumours assessed, 87 patients were excluded. 125 oestrogen-receptor-negative tumours (55 that showed pathological complete responses) were tested: 66 in the FEC group (28 that showed pathological complete responses) and 59 in the TET group (27 that showed pathological complete responses). The regimen-specific signatures significantly predicted pathological complete response in patients treated with the appropriate regimen (p<0.0001). The FEC predictor had a sensitivity of 96% (27 of 28 patients [95% CI 82-99]), specificity of 66% (25 of 38 patients [50-79]), positive predictive value (PPV) of 68% (27 of 40 patients [52-80]), and negative predictive value (NPV) of 96% (25 of 26 patients [81-99]). The TET predictor had a sensitivity of 93% (25 of 27 patients [77-98]), specificity 69% (22 of 32 patients [51-82]), PPV of 71% (25 of 35 patients [55-84]), and NPV of 92% (22 of 24 patients [74-98]). Analysis of tumour size, grade, nodal status, age, and regimen-specific signatures showed that the genomic signatures were the only independent variables predicting pathological complete response at p<0.01. Selection of patients with these signatures would increase the proportion of patients with pathological complete responses from 44% to around 70% in the patients studied here. INTERPRETATION: We have validated the use of regimen-specific drug sensitivity signatures in the context of a multicentre randomised trial. The high NPV of both signatures may allow early selection of patients with breast cancer who should be considered for trials with new drugs.

Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis.

BACKGROUND: Histologic grade in breast cancer provides clinically important prognostic information. However, 30%-60% of tumors are classified as histologic grade 2. This grade is associated with an intermediate risk of recurrence and is thus not informative for clinical decision making. We examined whether histologic grade was associated with gene expression profiles of breast cancers and whether such profiles could be used to improve histologic grading. METHODS: We analyzed microarray data from 189 invasive breast carcinomas and from three published gene expression datasets from breast carcinomas. We identified differentially expressed genes in a training set of 64 estrogen receptor (ER)-positive tumor samples by comparing expression profiles between histologic grade 3 tumors and histologic grade 1 tumors and used the expression of these genes to define the gene expression grade index. Data from 597 independent tumors were used to evaluate the association between relapse-free survival and the gene expression grade index in a Kaplan-Meier analysis. All statistical tests were two-sided. RESULTS: We identified 97 genes in our training set that were associated with histologic grade; most of these genes were involved in cell cycle regulation and proliferation. In validation datasets, the gene expression grade index was strongly associated with histologic grade 1 and 3 status; however, among histologic grade 2 tumors, the index spanned the values for histologic grade 1-3 tumors. Among patients with histologic grade 2 tumors, a high gene expression grade index was associated with a higher risk of recurrence than a low gene expression grade index (hazard ratio = 3.61, 95% confidence interval = 2.25 to 5.78; P < .001, log-rank test). CONCLUSIONS: Gene expression grade index appeared to reclassify patients with histologic grade 2 tumors into two groups with high versus low risks of recurrence. This approach may improve the accuracy of tumor grading and thus its prognostic value.

Identification of molecular apocrine breast tumours by microarray analysis.

Previous microarray studies on breast cancer identified multiple tumour classes, of which the most prominent, named luminal and basal, differ in expression of the oestrogen receptor alpha gene (ER). We report here the identification of a group of breast tumours with increased androgen signalling and a 'molecular apocrine' gene expression profile. Tumour samples from 49 patients with large operable or locally advanced breast cancers were tested on Affymetrix U133A gene expression microarrays. Principal components analysis and hierarchical clustering split the tumours into three groups: basal, luminal and a group we call molecular apocrine. All of the molecular apocrine tumours have strong apocrine features on histological examination (P=0.0002). The molecular apocrine group is androgen receptor (AR) positive and contains all of the ER-negative tumours outside the basal group. Kolmogorov-Smirnov testing indicates that oestrogen signalling is most active in the luminal group, and androgen signalling is most active in the molecular apocrine group. ERBB2 amplification is commoner in the molecular apocrine than the other groups. Genes that best split the three groups were identified by Wilcoxon test. Correlation of the average expression profile of these genes in our data with the expression profile of individual tumours in four published breast cancer studies suggest that molecular apocrine tumours represent 8-14% of tumours in these studies. Our data show that it is possible with microarray data to divide mammary tumour cells into three groups based on steroid receptor activity: luminal (ER+ AR+), basal (ER- AR-) and molecular apocrine (ER- AR+).

Transcript profiling suggests that differential metabolic adaptation of mice to a high fat diet is associated with changes in liver to muscle lipid fluxes.

Genetically homogenous C57Bl/6 mice display differential metabolic adaptation when fed a high fat diet for 9 months. Most become obese and diabetic, but a significant fraction remains lean and diabetic or lean and non-diabetic. Here, we performed microarray analysis of "metabolic" transcripts expressed in liver and hindlimb muscles to evaluate: (i) whether expressed transcript patterns could indicate changes in metabolic pathways associated with the different phenotypes, (ii) how these changes differed from the early metabolic adaptation to short term high fat feeding, and (iii) whether gene classifiers could be established that were characteristic of each metabolic phenotype. Our data indicate that obesity/diabetes was associated with preserved hepatic lipogenic gene expression and increased plasma levels of very low density lipoprotein and, in muscle, with an increase in lipoprotein lipase gene expression. This suggests increased muscle fatty acid uptake, which may favor insulin resistance. In contrast, the lean mice showed a strong reduction in the expression of hepatic lipogenic genes, in particular of Scd-1, a gene linked to sensitivity to diet-induced obesity; the lean and non-diabetic mice presented an additional increased expression of eNos in liver. After 1 week of high fat feeding the liver gene expression pattern was distinct from that seen at 9 months in any of the three mouse groups, thus indicating progressive establishment of the different phenotypes. Strikingly, development of the obese phenotype involved re-expression of Scd-1 and other lipogenic genes. Finally, gene classifiers could be established that were characteristic of each metabolic phenotype. Together, these data suggest that epigenetic mechanisms influence gene expression patterns and metabolic fates.

Classification of human astrocytic gliomas on the basis of gene expression: a correlated group of genes with angiogenic activity emerges as a strong predictor of subtypes.

The development of targeted treatment strategies adapted to individual patients requires identification of the different tumor classes according to their biology and prognosis. We focus here on the molecular aspects underlying these differences, in terms of sets of genes that control pathogenesis of the different subtypes of astrocytic glioma. By performing cDNA-array analysis of 53 patient biopsies, comprising low-grade astrocytoma, secondary glioblastoma (respective recurrent high-grade tumors), and newly diagnosed primary glioblastoma, we demonstrate that human gliomas can be differentiated according to their gene expression. We found that low-grade astrocytoma have the most specific and similar expression profiles, whereas primary glioblastoma exhibit much larger variation between tumors. Secondary glioblastoma display features of both other groups. We identified several sets of genes with relatively highly correlated expression within groups that: (a). can be associated with specific biological functions; and (b). effectively differentiate tumor class. One prominent gene cluster discriminating primary versus nonprimary glioblastoma comprises mostly genes involved in angiogenesis, including VEGF fms-related tyrosine kinase 1 but also IGFBP2, that has not yet been directly linked to angiogenesis. In situ hybridization demonstrating coexpression of IGFBP2 and VEGF in pseudopalisading cells surrounding tumor necrosis provided further evidence for a possible involvement of IGFBP2 in angiogenesis. The separating groups of genes were found by the unsupervised coupled two-way clustering method, and their classification power was validated by a supervised construction of a nearly perfect glioma classifier.

Inhibition of prostaglandin G/H synthase unveils a potent effect of platelet activating factor on the permeability of bovine aortic endothelial cells to albumin.

Platelet Activating Factor (PAF) is a very potent stimulant of various cell functions but little is known about the mechanisms responsible for its marked effect on endothelial permeability. An in vitro assay system was used to assess the direct effect of PAF on the permeability of a bovine aortic endothelial cell (BAEC) monolayer to albumin. PAF produced a small but not significant increase of the permeability of BAEC monolayer to albumin. However, pre-treatment of the monolayer with indomethacin (10 microM) resulted in a significant increase of BAEC permeability following PAF administration. This increase was concentration-dependent up to a maximal effect of 105% above basal value (for 0.1 microM PAF). Addition of the PAF antagonist SRI 63 441ZI (5 microM) abolished this effect. Exogenous administration of PGE2 (10(-7) M) inhibited the effect of PAF on the BAEC permeability suggesting that prostaglandins synthesized by the endothelium behave as a negative autoregulatory factor. Compound SRI 63 441ZI also partially inhibited bradykinin-induced permeability to albumin but did not significantly modify the activity of thrombin. These findings show that PAF can increase endothelial permeability to albumin when the synthesis of prostaglandins is inhibited. Our results also show that PAF might have an autocrine activity by mediating part of BK-induced permeability.