Integrative analyses identify modulators of response to neoadjuvant aromatase inhibitors in patients with early breast cancer

Introduction Aromatase inhibitors (AIs) are a vital component of estrogen receptor positive (ER+) breast cancer treatment. De novo and acquired resistance, however, is common. The aims of this study were to relate patterns of copy number aberrations to molecular and proliferative response to AIs, to study differences in the patterns of copy number aberrations between breast cancer samples pre- and post-AI neoadjuvant therapy, and to identify putative biomarkers for resistance to neoadjuvant AI therapy using an integrative analysis approach. Methods Samples from 84 patients derived from two neoadjuvant AI therapy trials were subjected to copy number profiling by microarray-based comparative genomic hybridisation (aCGH, n = 84), gene expression profiling (n = 47), matched pre- and post-AI aCGH (n = 19 pairs) and Ki67-based AI-response analysis (n = 39). Results Integrative analysis of these datasets identified a set of nine genes that, when amplified, were associated with a poor response to AIs, and were significantly overexpressed when amplified, including CHKA, LRP5 and SAPS3. Functional validation in vitro, using cell lines with and without amplification of these genes (SUM44, MDA-MB134-VI, T47D and MCF7) and a model of acquired AI-resistance (MCF7-LTED) identified CHKA as a gene that when amplified modulates estrogen receptor (ER)-driven proliferation, ER/estrogen response element (ERE) transactivation, expression of ER-regulated genes and phosphorylation of V-AKT murine thymoma viral oncogene homolog 1 (AKT1). Conclusions These data provide a rationale for investigation of the role of CHKA in further models of de novo and acquired resistance to AIs, and provide proof of concept that integrative genomic analyses can identify biologically relevant modulators of AI response

Identification of chemokine receptors as potential modulators of endocrine resistance in oestrogen receptor–positive breast cancers

Introduction Endocrine therapies target oestrogenic stimulation of breast cancer (BC) growth, but resistance remains problematic. Our aims in this study were (1) to identify genes most strongly associated with resistance to endocrine therapy by intersecting global gene transcription data from patients treated presurgically with the aromatase inhibitor anastrazole with those from MCF7 cells adapted to long-term oestrogen deprivation (LTED) (2) to assess the clinical value of selected genes in public clinical data sets and (3) to determine the impact of targeting these genes with novel agents. Methods Gene expression and Ki67 data were available from 69 postmenopausal women with oestrogen receptor–positive (ER+) early BC, at baseline and 2 weeks after anastrazole treatment, and from cell lines adapted to LTED. The functional consequences of target genes on proliferation, ER-mediated transcription and downstream cell signalling were assessed. Results By intersecting genes predictive of a poor change in Ki67 with those upregulated in LTED cells, we identified 32 genes strongly correlated with poor antiproliferative response that were associated with inflammation and/or immunity. In a panel of LTED cell lines, C-X-C chemokine receptor type 7 (CXCR7) and CXCR4 were upregulated compared to their wild types (wt), and CXCR7, but not CXCR4, was associated with reduced relapse-free survival in patients with ER+ BC. The CXCR4 small interfering RNA variant (siCXCR4) had no specific effect on the proliferation of wt-SUM44, wt-MCF7 and their LTED derivatives. In contrast, siCXCR7, as well as CCX733, a CXCR7 antagonist, specifically suppressed the proliferation of MCF7-LTED cells. siCXCR7 suppressed proteins associated with G1/S transition and inhibited ER transactivation in MCF7-LTED, but not wt-MCF7, by impeding association between ER and proline-, glutamic acid– and leucine-rich protein 1, an ER coactivator. Conclusions These data highlight CXCR7 as a potential therapeutic target warranting clinical investigation in endocrine-resistant BC

ESR1 Is Co-Expressed with Closely Adjacent Uncharacterised Genes Spanning a Breast Cancer Susceptibility Locus at 6q25.1

Approximately 80% of human breast carcinomas present as oestrogen receptor α-positive (ER+ve) disease, and ER status is a critical factor in treatment decision-making. Recently, single nucleotide polymorphisms (SNPs) in the region immediately upstream of the ER gene (ESR1) on 6q25.1 have been associated with breast cancer risk. Our investigation of factors associated with the level of expression of ESR1 in ER+ve tumours has revealed unexpected associations between genes in this region and ESR1 expression that are important to consider in studies of the genetic causes of breast cancer risk. RNA from tumour biopsies taken from 104 postmenopausal women before and after 2 weeks treatment with an aromatase (oestrogen synthase) inhibitor was analyzed on Illumina 48K microarrays. Multiple-testing corrected Spearman correlation revealed that three previously uncharacterized open reading frames (ORFs) located immediately upstream of ESR1, C6ORF96, C6ORF97, and C6ORF211 were highly correlated with ESR1 (Rs = 0.67, 0.64, and 0.55 respectively, FDR<1×10−7). Publicly available datasets confirmed this relationship in other groups of ER+ve tumours. DNA copy number changes did not account for the correlations. The correlations were maintained in cultured cells. An ERα antagonist did not affect the ORFs' expression or their correlation with ESR1, suggesting their transcriptional co-activation is not directly mediated by ERα. siRNA inhibition of C6ORF211 suppressed proliferation in MCF7 cells, and C6ORF211 positively correlated with a proliferation metagene in tumours. In contrast, C6ORF97 expression correlated negatively with the metagene and predicted for improved disease-free survival in a tamoxifen-treated published dataset, independently of ESR1. Our observations suggest that some of the biological effects previously attributed to ER could be mediated and/or modified by these co-expressed genes. The co-expression and function of these genes may be important influences on the recently identified relationship between SNPs in this region and breast cancer risk

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

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.Peer reviewe

Close and stable relationship between proliferation and a hypoxia metagene in aromatase inhibitor-treated ER-positive breast cancer.

PURPOSE: The majority of breast cancer patients who have estrogen receptor positive (ER(+)) tumors whose proliferation is reduced after estrogen deprivation by aromatase inhibitors (AI). This study investigates any link between proliferation and hypoxia, a major determinant of tumor biology, and defines the effect of estrogen deprivation on hypoxia-associated genes. METHODS: Genome-wide expression profiles were obtained from tumor biopsies from 81 ER(+) postmenopausal patients, before and after 2 weeks' anastrozole treatment. A hypoxia metagene was developed by identifying genes clustered with classical hypoxia-regulated genes, excluding those associated with proliferation. Proliferation was measured by Ki67 and a proliferation metagene derived from two published breast cancer data sets. RESULTS: Hypoxia and proliferation metagenes were associated at baseline (Pearson correlation coefficient, r = 0.67, P &lt; 10(-4)) and after 2 weeks (r = 0.71, P &lt; 10(-4)). Hypoxia metagene at baseline was associated with 2-week Ki67 (r = 0.43, P = 0.0002) and more weakly with poor 2-week Ki67 change consistent with a weak association with AI resistance. Hypoxia metagene was significantly downregulated with AI. This downregulation was significantly associated with change in the proliferation metagene and with Ki67 but, importantly, not with the substantial change in expression of classical estrogen-dependent genes. CONCLUSIONS: Hypoxia metagene is closely associated with proliferation before and after AI treatment. The downregulation of hypoxia metagene after AI therapy is most likely the result of changes in proliferation. There may be a weak effect of hypoxia metagene on de novo resistance to AIs. These findings are important to consider in coapplication of antiproliferative agents with antiangiogenic or antihypoxia agents

Effectiveness and molecular interactions of the clinically active mTORC1 inhibitor everolimus in combination with tamoxifen or letrozole in vitro and in vivo

Strategies to improve the efficacy of endocrine agents in breast cancer (BC) therapy and to delay the onset of resistance include concomitant targeting of the estrogen receptor alpha (ER) and the mammalian target of rapamycin complex 1 (mTORC1), which regulate cell-cycle progression and are supported by recent clinical results

Late Quaternary evolution of Lago Castor (Chile, 45.6°S) : timing of the deglaciation in northern Patagonia and evolution of the westerlies during the last 17 kyr.

International audienceEven though Patagonia is ideally located to study climate of the southern mid-latitudes, many questions on the late Quaternary climate evolution remain unresolved. The timing of maximum glacier extent is still uncertain in vast areas, and the postglacial evolution of the Southern Westerly Wind Belt (SWWB) remains highly debated. Here, we study the sedimentary infill of a glacigenic lake (Lago Castor; 45.6°S, 71.8°W) located at the leeside of the Andes in Chilean Patagonia to i) reconstruct the deglacial evolution of the eastern flank of the Patagonian Ice Sheet (PIS), and ii) discuss postglacial changes in wind strength at a critical location where westerly wind records are critically lacking. A dense grid of high-resolution reflection-seismic data was used to reconstruct the large-scale infill history of the lake, and a radiocarbon dated sediment core penetrating all lacustrine seismic units, was retrieved. Results indicate that the deglaciation of the lake basin and its catchment occurred no later than ∼28 cal kyr BP (i.e. an early LGM), but possibly even already after MIS 4. Afterwards, the Lago Castor area was covered by a large proglacial lake that drained – possibly through an outburst flood – when the PIS outlet glaciers retreated to a critical location. Subsequently, very dry conditions caused the lake to desiccate, as evidenced by an unconformity visible on the seismic profiles and in the sediment core. This dry period likely resulted from the increased orographic effect of the PIS-covered Andes, accompanied by weaker westerlies. From ∼20 kyr BP onwards, the combination of a shrinking PIS and a southward shift of the SWWB resulted in increased precipitation, which caused the lake level to rise. After ∼17 cal kyr BP, lake sedimentation was more directly influenced by the southern westerlies, with the formation of sediment drifts resulting from strong bottom current during periods of intense westerly winds. Our results suggest a progressive increase in wind strength at 46°S from 11.2 to 4.5 cal kyr BP, which supports the hypothesis that the SWWB broadened during the early and middle Holocene

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

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 &gt;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

The genome of Rhizobium leguminosarum has recognizable core and accessory components

Background: Rhizobium leguminosarum is an alpha-proteobacterial N-2-fixing symbiont of legumes that has been the subject of more than a thousand publications. Genes for the symbiotic interaction with plants are well studied, but the adaptations that allow survival and growth in the soil environment are poorly understood. We have sequenced the genome of R. leguminosarum biovar viciae strain 3841. Results: The 7.75 Mb genome comprises a circular chromosome and six circular plasmids, with 61% G+C overall. All three rRNA operons and 52 tRNA genes are on the chromosome; essential protein-encoding genes are largely chromosomal, but most functional classes occur on plasmids as well. Of the 7,263 protein-encoding genes, 2,056 had orthologs in each of three related genomes ( Agrobacterium tumefaciens, Sinorhizobium meliloti, and Mesorhizobium loti), and these genes were overrepresented in the chromosome and had above average G+C. Most supported the rRNA-based phylogeny, confirming A. tumefaciens to be the closest among these relatives, but 347 genes were incompatible with this phylogeny; these were scattered throughout the genome but were over-represented on the plasmids. An unexpectedly large number of genes were shared by all three rhizobia but were missing from A. tumefaciens. Conclusion: Overall, the genome can be considered to have two main components: a 'core', which is higher in G+C, is mostly chromosomal, is shared with related organisms, and has a consistent phylogeny; and an 'accessory' component, which is sporadic in distribution, lower in G+C, and located on the plasmids and chromosomal islands. The accessory genome has a different nucleotide composition from the core despite a long history of coexistence