This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Rhizobium leguminosarum genome

The genome sequence of the α-proteobacterial N2-fixing symbiont of legumes, Rhizobium leguminosarum, is described, revealing a 'core' and an 'accessory' component.

Most estrogen receptor α (ER)-positive breast cancers initially respond to antiestrogens, but many eventually become estrogen-independent and recur. We identified an estrogen-independent role for ER and the CDK4/Rb/E2F transcriptional axis in the hormone-independent growth of breast cancer cells. ER downregulation with fulvestrant or siRNA inhibited estrogen-independent growth. Chromatin immunoprecipitation identified ER genomic binding activity in estrogen-deprived cells and primary breast tumors treated with aromatase inhibitors. Gene expression profiling revealed an estrogen-independent, ER/E2F-directed transcriptional program. An E2F activation gene signature correlated with a lesser response to aromatase inhibitors in patients' tumors. siRNA screening showed that CDK4, an activator of E2F, is required for estrogen-independent cell growth. Long-term estrogen-deprived cells hyperactivate phosphatidylinositol 3-kinase (PI3K) independently of ER/E2F. Fulvestrant combined with the pan-PI3K inhibitor BKM120 induced regression of ER+ xenografts. These data support further development of ER downregulators and CDK4 inhibitors, and their combination with PI3K inhibitors for treatment of antiestrogen-resistant breast cancers.

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.

Purpose: Estrogen withdrawal by treatment with aromatase inhibitors is the most effective form of endocrine therapy for postmenopausal estrogen receptor-positive (ERþ) breast cancer. However, response to therapy varies markedly and understanding of the precise molecular effects of aromatase inhibitors and causes of resistance is limited. We aimed to identify in clinical breast cancer those genes and pathways most associated with resistance to aromatase inhibitors by examining the global transcriptional effects of AI treatment.Experimental Design: Baseline and 2-week posttreatment biopsies were obtained from 112 postmenopausal women with ERþ breast cancer receiving neoadjuvant anastrozole. Gene expression data were obtained from 81 baseline and 2-week paired samples. Pathway analysis identified (i) the most prevalent changes in expression and (ii) the pretreatment genes/pathways most related to poor antiproliferative response.Results: A total of 1,327 genes were differentially expressed after 2-week treatment (false discovery rate < 0.01). Proliferation-associated genes and classical estrogen-dependent genes were strongly downregulated whereas collagens and chemokines were upregulated. Pretreatment expression of an inflammatory signature correlated with antiproliferative response to anastrozole and this observation was validated in an independent study. Higher expression of immune-related genes such as SLAMF8 and TNF as well as lymphocytic infiltration were associated with poorer response (P < 0.001) and validated in an independent cohort.Conclusions: The molecular response to aromatase inhibitor treatment varies greatly between patients consistent with the variable clinical benefit from aromatase inhibitor treatment. Higher baseline expression of an inflammatory signature is associated with poor antiproliferative response and should be assessed further as a novel biomarker and potential target for aromatase inhibitor-treated patients.

Purpose Although most patients with estrogen receptor α (ER)-positive breast cancer initially respond to endocrine therapy, many ultimately develop resistance to antiestrogens. However, mechanisms of antiestrogen resistance and biomarkers predictive of such resistance are underdeveloped. Experimental Design We adapted four ER+ human breast cancer cell lines to grow in an estrogen-depleted medium. A gene signature of estrogen independence was developed by comparing expression profiles of long-term estrogen-deprived (LTED) cells to their parental counterparts. We evaluated the ability of the LTED signature to predict tumor response to neoadjuvant therapy with an aromatase inhibitor, and disease outcome following adjuvant tamoxifen. We utilized Gene Set Analysis (GSA) of LTED cell gene expression profiles and a loss-of-function approach to identify pathways causally associated with resistance to endocrine therapy. Results The LTED gene expression signature was predictive of high tumor cell proliferation following neoadjuvant therapy with anastrozole and letrozole, each in different patient cohorts. This signature was also predictive of poor recurrence-free survival in two studies of patients treated with adjuvant tamoxifen. Bioinformatic interrogation of expression profiles in LTED cells revealed a signature of MYC activation. The MYC activation signature and high MYC protein levels were both predictive of poor outcome following tamoxifen therapy. Finally, knockdown of MYC inhibited LTED cell growth. Conclusions A gene expression signature derived from ER+ breast cancer cells with acquired hormone independence predicted tumor response to aromatase inhibitors and associated with clinical markers of resistance to tamoxifen. In some cases, activation of the MYC pathway was associated with this resistance.

A B S T R A C T PurposeTo determine whether plasma estradiol (E2) levels are related to gene expression in estrogen receptor (ER)-positive breast cancers in postmenopausal women. Materials and MethodsGenome-wide RNA profiles were obtained from pretreatment core-cut tumor biopsies from 104 postmenopausal patients with primary ER-positive breast cancer treated with neoadjuvant anastrozole. Pretreatment plasma E2 levels were determined by highly sensitive radioimmunoassay. Genes were identified for which expression was correlated with pretreatment plasma E2 levels. Validation was performed in an independent set of 73 ER-positive breast cancers. ResultsThe expression of many known estrogen-responsive genes and gene sets was highly significantly associated with plasma E2 levels (eg, TFF1/pS2, GREB1, PDZK1 and PGR; P Ͻ .005). Plasma E2 explained 27% of the average expression of these four average estrogen-responsive genes (ie, AvERG; r ϭ 0.51; P Ͻ .0001), and a standardized mean of plasma E2 levels and ER transcript levels explained 37% (r, 0.61). These observations were validated in an independent set of 73 ER-positive tumors. Exploratory analysis suggested that addition of the nuclear coregulators in a multivariable analysis with ER and E2 levels might additionally improve the relationship with the AvERG. Plasma E2 and the standardized mean of E2 and ER were both significantly correlated with 2-week Ki67, a surrogate marker of clinical outcome (r ϭ Ϫ0.179; P ϭ .05; and r ϭ Ϫ0.389; P ϭ .0005, respectively). ConclusionPlasma E2 levels are significantly associated with gene expression of ER-positive breast cancers and should be considered in future genomic studies of ER-positive breast cancer. The AvERG is a new experimental tool for the study of putative estrogenic stimuli of breast cancer.

Genomic profiling of tumours in patients in clinical trials enables rapid testing of multiple hypotheses to confirm which genomic events determine likely responder groups for targeted agents. A key challenge of this new capability is defining which specific genomic events should be classified as 'actionable' (that is, potentially responsive to a targeted therapy), especially when looking for early indications of patient subgroups likely to be responsive to new drugs. This Opinion article discusses some of the different approaches being taken in early clinical development to define actionable mutations, and describes our strategy to address this challenge in early-stage exploratory clinical trials.

This work centres on the genomic comparisons of two closely-related nitrogen-fixing symbiotic bacteria, Rhizobium leguminosarum biovar viciae 3841 and Rhizobium etli CFN42. These strains maintain a stable genomic core that is also common to other rhizobia species plus a very variable and significant accessory component. The chromosomes are highly syntenic, whereas plasmids are related by fewer syntenic blocks and have mosaic structures. The pairs of plasmids p42f-pRL12, p42e-pRL11 and p42b-pRL9 as well large parts of p42c with pRL10 are shown to be similar, whereas the symbiotic plasmids (p42d and pRL10) are structurally unrelated and seem to follow distinct evolutionary paths. Even though purifying selection is acting on the whole genome, the accessory component is evolving more rapidly. This component is constituted largely for proteins for transport of diverse metabolites and elements of external origin. The present analysis allows us to conclude that a heterogeneous and quickly diversifying group of plasmids co-exists in a common genomic framework.