pAKT pathway activation is associated with PIK3CA mutations and good prognosis in luminal breast cancer in contrast to p-mTOR pathway activation

Numerous studies have focused on the PI3K/AKT/mTOR pathway in estrogen receptor positive (ER) breast cancer (BC), as a linear signal transduction pathway and reported its association with worse clinical outcomes. We developed gene signatures that reflect the level of expression of phosphorylated-Serine473-AKT (pAKT) and phosphorylated-Serine2448-mTOR (p-mTOR) separately, capturing their corresponding level of pathway activation. Our analysis revealed that the pAKT pathway activation was associated with luminal A BC while the p-mTOR pathway activation was more associated with luminal B BC (Kruskal-Wallis test p < 10^-10). pAKT pathway activation was significantly associated with better outcomes (multivariable HR, 0.79; 95%CI, 0.74-0.85; p = 2.5 × 10^-10) and PIK3CA mutations (p = 0.0001) whereas p-mTOR pathway activation showed worse outcomes (multivariable HR,1.1; 95%CI, 1.1-1.2; p = 9.9 × 10^-4) and associated with p53 mutations (p = 0.04). in conclusion, our data show that pAKT and p-mTOR pathway activation have differing impact on prognosis and suggest that they are not linearly connected in luminal breast cancers.

CDK4 phosphorylation status and a linked gene expression profile predict sensitivity to palbociclib

Cyclin D-CDK4/6 are the first CDK complexes to be activated in the G1 phase in response to oncogenic pathways. The specific CDK4/6 inhibitor PD0332991 (palbociclib) was recently approved by the FDA and EMA for treatment of advanced ER-positive breast tumors. Unfortunately, no reliable predictive tools are available for identifying potentially responsive or insensitive tumors. We had shown that the activating T172 phosphorylation of CDK4 is the central rate-limiting event that initiates the cell cycle decision and signals the presence of active CDK4. Here, we report that the profile of post-translational modification including T172 phosphorylation of CDK4 differs among breast tumors and associates with their subtypes and risk. A gene expression signature faithfully predicted CDK4 modification profiles in tumors and cell lines. Moreover, in breast cancer cell lines, the CDK4 T172 phosphorylation best correlated with sensitivity to PD0332991. This gene expression signature identifies tumors that are unlikely to respond to CDK4/6 inhibitors and could help to select a subset of patients with HER2-positive and basal-like tumors for clinical studies on this class of drugs.

p-STAT3 in luminal breast cancer: Integrated RNA-protein pooled analysis and results from the BIG 2-98 phase III trial

In the present study, in order to investigate the role of signal transducer and activator of transcription 3 (STAT3) in estrogen receptor (ER)-positive breast cancer prognosis, we evaluated the phosphorylated STAT3 (p-STAT3) status and investigated its effect on the outcome in a pooled analysis and in a large prospective adjuvant trial. By using the TCGA repository, we developed gene signatures that reflected the level of p-STAT3. Using pooled analysis of the expression data from luminal breast cancer patients, we assessed the effects of the p-STAT3 expression signature on prognosis. We further validated the p-STAT3 prognostic effect using immunohistochemistry (IHC) and immunofluorescence staining of p-STAT3 tissue microarrays from a large randomised prospective trial. Our analysis demonstrated that p-STAT3 expression was elevated in luminal A-type breast cancer (Kruskal-Wallis test, P<10e-10) and was significantly associated with a good prognosis (log-rank, P<10e-10). Notably, the p-STAT3 expression signature identified patients with a good prognosis irrespective of the luminal subtype (log-rank: luminal A, P=0.026; luminal B, P=0.006). p-STAT3 staining by IHC in the stroma or tumour was detected in 174 out of 610 ER-positive samples (28.5%) from the BIG 2-98 randomised trial. With a median follow-up of 10.1 years, p-STAT3 was associated with a reduced risk of recurrence in ER-positive/HER2-negative breast cancer (Cox univariate HR, 0.66; 95% CI, 0.44-0.98; P=0.04). On the whole, our data indicate that p-STAT3 is associated with an improved outcome in ER-positive breast cancer.

CXCL13-producing TFH cells link immune suppression and adaptive memory in human breast cancer

T follicular helper cells (TFH cells) are important regulators of antigen-specific B cell responses. The B cell chemoattractant CXCL13 has recently been linked with TFH cell infiltration and improved survival in human cancer. Although human TFH cells can produce CXCL13, their immune functions are currently unknown. This study presents data from human breast cancer, advocating a role for tumor-infiltrating CXCL13-producing (CXCR5-) TFH cells, here named TFHX13 cells, in promoting local memory B cell differentiation. TFHX13 cells potentially trigger tertiary lymphoid structure formation and thereby generate germinal center B cell responses at the tumor site. Follicular DCs are not potent CXCL13 producers in breast tumor tissues. We used the TFH cell markers PD-1 and ICOS to identify distinct effector and regulatory CD4+ T cell subpopulations in breast tumors. TFHX13 cells are an important component of the PD-1hiICOSint effector subpopulation and coexpanded with PD-1intICOShiFOXP3hi Tregs. IL2 deprivation induces CXCL13 expression in vitro with a synergistic effect from TGFβ1, providing insight into TFHX13 cell differentiation in response to Treg accumulation, similar to conventional TFH cell responses. Our data suggest that human TFHX13 cell differentiation may be a key factor in converting Treg-mediated immune suppression to de novo activation of adaptive antitumor humoral responses in the chronic inflammatory breast cancer microenvironment.

HER2-Overexpressing Breast Cancers Amplify FGFR Signaling upon Acquisition of Resistance to Dual Therapeutic Blockade of HER2

Purpose: Dual blockade of HER2 with trastuzumab and lapatinib or pertuzumab has been shown to be superior to single-agent trastuzumab. However, a significant fraction of HER2-overexpressing (HER2⁺) breast cancers escape from these drug combinations. In this study, we sought to discover the mechanisms of acquired resistance to the combination of lapatinib + trastuzumab.Experimental Design: HER2⁺ BT474 xenografts were treated with lapatinib + trastuzumab long-term until resistance developed. Potential mechanisms of acquired resistance were evaluated in lapatinib + trastuzumab-resistant (LTR) tumors by targeted capture next-generation sequencing. In vitro experiments were performed to corroborate these findings, and a novel drug combination was tested against LTR xenografts. Gene expression and copy-number analyses were performed to corroborate our findings in clinical samples.Results: LTR tumors exhibited an increase in FGF3/4/19 copy number, together with an increase in FGFR phosphorylation, marked stromal changes in the tumor microenvironment, and reduced tumor uptake of lapatinib. Stimulation of BT474 cells with FGF4 promoted resistance to lapatinib + trastuzumab in vitro Treatment with FGFR tyrosine kinase inhibitors reversed these changes and overcame resistance to lapatinib + trastuzumab. High expression of FGFR1 correlated with a statistically shorter progression-free survival in patients with HER2⁺ early breast cancer treated with adjuvant trastuzumab. Finally, FGFR1 and/or FGF3 gene amplification correlated with a lower pathologic complete response in patients with HER2⁺ early breast cancer treated with neoadjuvant anti-HER2 therapy.Conclusions: Amplification of FGFR signaling promotes resistance to HER2 inhibition, which can be diminished by the combination of HER2 and FGFR inhibitors. Clin Cancer Res; 23(15); 4323-34. ©2017 AACR.

Low Dose Radiation Causes Skin Cancer in Mice and Has a Differential Effect on Distinct Epidermal Stem Cells

The carcinogenic effect of ionizing radiation has been evaluated based on limited populations accidently exposed to high dose radiation. In contrast, insufficient data are available on the effect of low dose radiation (LDR), such as radiation deriving from medical investigations and interventions, as well as occupational exposure that concern a large fraction of western populations. Using mouse skin epidermis as a model, we showed that LDR results in DNA damage in sebaceous gland (SG) and bulge epidermal stem cells (SCs). While the first commit apoptosis upon low dose irradiation, the latter survive. Bulge SC survival coincides with higher HIF-1α expression and a metabolic switch upon LDR. Knocking down HIF-1α sensitizes bulge SCs to LDR-induced apoptosis, while upregulation of HIF-1α in the epidermis, including SG SCs, rescues cell death. Most importantly, we show that LDR results in cancer formation with full penetrance in the radiation-sensitive Patched1 heterozygous mice. Overall, our results demonstrate for the first time that LDR can be a potent carcinogen in individuals predisposed to cancer. Stem Cells 2017;35:1355-1364.

The Prognostic Role of Androgen Receptor in Patients with Early-Stage Breast Cancer: A Meta-analysis of Clinical and Gene Expression Data

Purpose: Androgen receptor (AR) expression has been observed in about 70% of patients with breast cancer, but its prognostic role remains uncertain.Experimental Design: To assess the prognostic role of AR expression in early-stage breast cancer, we performed a meta-analysis of studies that evaluated the impact of AR at the protein and gene expression level on disease-free survival (DFS) and/or overall survival (OS). Eligible studies were identified by systematic review of electronic databases using the MeSH-terms "breast neoplasm" and "androgen receptor" and were selected after a qualitative assessment based on the REMARK criteria. A pooled gene expression analysis of 35 publicly available microarray data sets was also performed from patients with early-stage breast cancer with available gene expression and clinical outcome data.Results: Twenty-two of 33 eligible studies for the clinical meta-analysis, including 10,004 patients, were considered as evaluable for the current study after the qualitative assessment. AR positivity defined by IHC was associated with improved DFS in all patients with breast cancer [multivariate (M) analysis, HR 0.46; 95% confidence interval (CI) 0.37-0.58, P < 0.001] and better OS [M-HR 0.53; 95% CI, 0.38-0.73, P < 0.001]. Thirty-five datasets including 7,220 patients were eligible for the pooled gene expression analysis. High AR mRNA levels were found to confer positive prognosis overall in terms of DFS (HR 0.82; 95% CI 0.72-0.92;P = 0.0007) and OS (HR 0.84; 95% CI, 0.75-0.94; P = 0.02) only in univariate analysis.Conclusions: Our analysis, conducted among more than 17,000 women with early-stage breast cancer included in clinical and gene expression analysis, demonstrates that AR positivity is associated with favorable clinical outcome. Clin Cancer Res; 23(11); 2702-12. ©2016 AACR.

Portraying breast cancers with long noncoding RNAs

Evidence is emerging that long noncoding RNAs (lncRNAs) may play a role in cancer development, but this role is not yet clear. We performed a genome-wide transcriptional survey to explore the lncRNA landscape across 995 breast tissue samples. We identified 215 lncRNAs whose genes are aberrantly expressed in breast tumors, as compared to normal samples. Unsupervised hierarchical clustering of breast tumors on the basis of their lncRNAs revealed four breast cancer subgroups that correlate tightly with PAM50-defined mRNA-based subtypes. Using multivariate analysis, we identified no less than 210 lncRNAs prognostic of clinical outcome. By analyzing the coexpression of lncRNA genes and protein-coding genes, we inferred potential functions of the 215 dysregulated lncRNAs. We then associated subtype-specific lncRNAs with key molecular processes involved in cancer. A correlation was observed, on the one hand, between luminal A-specific lncRNAs and the activation of phosphatidylinositol 3-kinase, fibroblast growth factor, and transforming growth factor-β pathways and, on the other hand, between basal-like-specific lncRNAs and the activation of epidermal growth factor receptor (EGFR)-dependent pathways and of the epithelial-to-mesenchymal transition. Finally, we showed that a specific lncRNA, which we called CYTOR, plays a role in breast cancer. We confirmed its predicted functions, showing that it regulates genes involved in the EGFR/mammalian target of rapamycin pathway and is required for cell proliferation, cell migration, and cytoskeleton organization. Overall, our work provides the most comprehensive analyses for lncRNA in breast cancers. Our findings suggest a wide range of biological functions associated with lncRNAs in breast cancer and provide a foundation for functional investigations that could lead to new therapeutic approaches.

Integrative proteomic and gene expression analysis identify potential biomarkers for adjuvant trastuzumab resistance: analysis from the Fin-her phase III randomized trial

Trastuzumab is a remarkably effective therapy for patients with human epidermal growth factor receptor 2 (HER2) - positive breast cancer (BC). However, not all women with high levels of HER2 benefit from trastuzumab.

By integrating mRNA and protein expression data from Reverse-Phase Protein Array Analysis (RPPA) in HER2-positive BC, we developed gene expression metagenes that reflect pathway activation levels. Next we assessed the ability of these metagenes to predict resistance to adjuvant trastuzumab using gene expression data from two independent datasets.

10 metagenes passed external validation (false discovery rate [fdr] < 0.05) and showed biological relevance with their pathway of origin. These metagenes were further screened for their association with trastuzumab resistance. An association with trastuzumab resistance was observed and validated only for the AnnexinA1 metagene (ANXA1). In the randomised phase III Fin-her study, tumours with low levels of the ANXA1 metagene showed a benefit from trastuzumab (multivariate: hazard ratio [HR] for distant recurrence = 0.16[95%CI 0.05–0.5]; p = 0.002; fdr = 0.03), while high expression levels of the ANXA1 metagene were associated with a lack of benefit to trastuzmab (HR = 1.29[95%CI 0.55–3.02]; p = 0.56). The association of ANXA1 with trastuzumab resistance was successfully validated in an independent series of subjects who had received trastuzumab with chemotherapy (Log Rank; p = 0.01).

In conclusion, in HER2-positive BC, some proteins are associated with distinct gene expression profiles. Our findings identify the ANXA1metagene as a novel biomarker for trastuzumab resistance.

The transcription factor ERG recruits CCR4-NOT to control mRNA decay and mitotic progression

Control of mRNA levels, a fundamental aspect in the regulation of gene expression, is achieved through a balance between mRNA synthesis and decay. E26-related gene (Erg) proteins are canonical transcription factors whose previously described functions are confined to the control of mRNA synthesis. Here, we report that ERG also regulates gene expression by affecting mRNA stability and identify the molecular mechanisms underlying this function in human cells. ERG is recruited to mRNAs via interaction with the RNA-binding protein RBPMS, and it promotes mRNA decay by binding CNOT2, a component of the CCR4-NOT deadenylation complex. Transcriptome-wide mRNA stability analysis revealed that ERG controls the degradation of a subset of mRNAs highly connected to Aurora signaling, whose decay during S phase is necessary for mitotic progression. Our data indicate that control of gene expression by mammalian transcription factors may follow a more complex scheme than previously anticipated, integrating mRNA synthesis and degradation.

Genomic Characterization of Primary Invasive Lobular Breast Cancer

Purpose

Invasive lobular breast cancer (ILBC) is the second most common histologic subtype after invasive ductal breast cancer (IDBC). Despite clinical and pathologic differences, ILBC is still treated as IDBC. We aimed to identify genomic alterations in ILBC with potential clinical implications.

Methods

From an initial 630 ILBC primary tumors, we interrogated oncogenic substitutions and insertions and deletions of 360 cancer genes and genome-wide copy number aberrations in 413 and 170 ILBC samples, respectively, and correlated those findings with clinicopathologic and outcome features.

Results

Besides the high mutation frequency of CDH1 in 65% of tumors, alterations in one of the three key genes of the phosphatidylinositol 3-kinase pathway, PIK3CA, PTEN, and AKT1, were present in more than one-half of the cases. HER2 and HER3 were mutated in 5.1% and 3.6% of the tumors, with most of these mutations having a proven role in activating the human epidermal growth factor receptor/ERBB pathway. Mutations in FOXA1 and ESR1 copy number gains were detected in 9% and 25% of the samples. All these alterations were more frequent in ILBC than in IDBC. The histologic diversity of ILBC was associated with specific alterations, such as enrichment for HER2 mutations in the mixed, nonclassic, and ESR1 gains in the solid subtype. Survival analyses revealed that chromosome 1q and 11p gains showed independent prognostic value in ILBC and that HER2 and AKT1 mutations were associated with increased risk of early relapse.

Conclusion

This study demonstrates that we can now begin to individualize the treatment of ILBC, with HER2, HER3, and AKT1 mutations representing high-prevalence therapeutic targets and FOXA1 mutations and ESR1 gains deserving urgent dedicated clinical investigation, especially in the context of endocrine treatment.

Genomic aberrations in young and elderly breast cancer patients

BACKGROUND

Age at breast cancer diagnosis is a known prognostic factor. Previously, several groups including ours have shown that young age at diagnosis is associated with higher prevalence of basal-like tumors and aggressive tumor phenotypes. Yet the impact of age at diagnosis on the genomic landscape of breast cancer remains unclear. In this study, we examined the pattern of somatic mutations, chromosomal copy number variations (CNVs) and transcriptomic profiles in young and elderly breast cancer patients.

METHODS

Analyses were performed on The Cancer Genome Atlas (TCGA) dataset. Patients with metastatic disease at diagnosis, classified as normal-like by PAM50 or had missing clinical information were excluded. Young patients were defined as ≤45 years of age, while elderly patients were those ≥70 years of age at breast cancer diagnosis. The remaining patients were classified as "intermediate". We evaluated the association between age at diagnosis and somatic mutations, CNV and gene expression in a logistic regression model adjusting for tumor size, nodal status, histology and breast cancer subtype. All analyses were corrected for multiple testing using the Benjamini-Hochberg approach.

RESULTS

In this study, 125, 486 and 169 patients were ≤45, 46-69 and ≥70 years of age, respectively. Older patients had more somatic mutations (n = 44 versus 35 versus 31; P = 0.0009) and more CNVs, especially in ductal tumors (P = 0.02). Eleven mutations were independently associated with age at diagnosis, of which only GATA3 was associated with young age (15.2% versus 8.2% versus 9%; P = 0.003). Only two CNV events were independently associated with age, with more chr18p losses in older patients and more chr6q27 deletions in younger ones. Younger age at diagnosis was associated with higher expression of gene signatures related to proliferation, stem cell features and endocrine resistance.

CONCLUSIONS

Age adds a layer of biological complexity beyond breast cancer molecular subtypes, classic pathological and clinical variables, worthy of further consideration in future drug development as we seek to refine therapeutic strategies in the era of personalized medicine.

Constitutive phosphorylated STAT3-associated gene signature is predictive for trastuzumab resistance in primary HER2-positive breast cancer

BACKGROUND

The likelihood of recurrence in patients with breast cancer who have HER2-positive tumors is relatively high, although trastuzumab is a remarkably effective drug in this setting. Signal transducer and activator of transcription 3 protein (STAT3), a transcription factor that is persistently tyrosine-705 phosphorylated (pSTAT3) in response to numerous oncogenic signaling pathways, activates downstream proliferative and anti-apoptotic pathways. We hypothesized that pSTAT3 expression in HER2-positive breast cancer will confer trastuzumab resistance.

METHODS

We integrated reverse phase protein array (RPPA) and gene expression data from patients with HER2-positive breast cancer treated with trastuzumab in the adjuvant setting.

RESULTS

We show that a pSTAT3-associated gene signature (pSTAT3-GS) is able to predict pSTAT3 status in an independent dataset (TCGA; AUC = 0.77, P = 0.02). This suggests that STAT3 induces a characteristic set of gene expression changes in HER2-positive cancers. Tumors characterized as high pSTAT3-GS were associated with trastuzumab resistance (log rank P = 0.049). These results were confirmed using data from the prospective, randomized controlled FinHer study, where the effect was especially prominent in HER2-positive estrogen receptor (ER)-negative tumors (interaction test P = 0.02). Of interest, constitutively activated pSTAT3 tumors were associated with loss of PTEN, elevated IL6, and stromal reactivation.

CONCLUSIONS

This study provides compelling evidence for a link between pSTAT3 and trastuzumab resistance in HER2-positive primary breast cancers. Our results suggest that it may be valuable to add agents targeting the STAT3 pathway to trastuzumab for treatment of HER2-positive breast cancer.

Abstract 3891: Tackling the obstacles facing the implementation of a molecular screening program in an early drug development unit: The Jules Bordet Institute Program for Molecular Profiling of Metastatic Lesions - feasibility (Precision-f)

Introduction

The identification of pathways involved in carcinogenesis, the emergence of high-throughput technologies enabling tumor molecular analysis and the development of targeted therapies have led to the concept of precision medicine. Higher response rates and longer survivals have been achieved in recent genomic-driven clinical trials. However, genomic-driven cancer medicine is hindered by multiple obstacles. Our aim was to assess the feasibility of incorporating real-time targeted gene sequencing (TGS) of DNA derived from metastatic biopsies into daily clinical practice.

Experimental procedures

Precision-f (NCT01932489) is a pilot trial conducted at the Jules Bordet Institute. Patients with metastatic colorectal cancer (CRC), melanoma or non-small cell lung (NSCLC) cancer were enrolled. Two FFPE blocks and one fresh frozen sample embedded in OCT were collected from newly performed metastatic biopsies as well as one whole blood sample. The FFPE samples were checked for tumor cellularity and 5×5 μm sections were cut and sent to 2 laboratories. Targeted gene sequencing was performed on DNA extracted for the same sample using the Illumina TruSeq Amplicon Cancer Panel performed on a MiSeq Desktop Sequencer, and the Life Technologies Ion AmpliSeq Cancer Hotspot Panel performed on an Ion Personal Genome Machine. Results were reported to the institutional sequencing tumor board for discussion, annotation and treatment assignment. The main objectives were the evaluation of biopsy quality, turnaround time, the presence of “actionable” alterations, technology cross-validity and treatment assignments.

Results

Thirty-four patients were enrolled between December 2013 and August 2014: 13 NSCLC patients, 11 CRC patients, 10 melanoma patients. Successful molecular results were achieved from 32/34 biopsies (94%). The most frequent site of biopsy was the liver (10) followed by the lung (7), the skin (5) and lymph nodes (5). 27/34 (79%) samples had ≥ 20% tumor cells. 31/34 (91%) of samples had &gt; 10 ng of DNA for TGS. The median turnaround time for results reporting was 15 calendar days [8-22]. “Actionable” mutations were found for 66% (21/32) of patients, 76% (16/21) of which were treated with therapy according to the identified molecular alteration. Reasons for non-targeted therapy were: non-eligibility (2), unavailable drugs (2) and patient refusal (1). The results of the comparison of TGS data across the 2 platforms is ongoing and will be presented at the meeting.

Conclusion

The precision-f pilot trial has demonstrated the feasibility and clinical relevance of a molecular screening program in a clinical pharmacology unit. A Belgian national initiative will follow in order to enhance patient participation in genomic-driven clinical trials.

Citation Format: Philippe G. Aftimos, Marion Maetens, Catherine Sibille, Jean-François Laes, Sylvain Brohée, Thierry Berghmans, Joseph Kerger, Alain Hendlisz, Alexandre Irrthum, Olivier De Henau, Amélie Deleporte, Stylianos Drisis, Denis Larsimont, Jalal Vakili, Christos Sotiriou, Martine Piccart, Ahmad Awada. Tackling the obstacles facing the implementation of a molecular screening program in an early drug development unit: The Jules Bordet Institute Program for Molecular Profiling of Metastatic Lesions - feasibility (Precision-f). [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3891. doi:10.1158/1538-7445.AM2015-3891

Biology of breast cancer during pregnancy using genomic profiling

Breast cancer during pregnancy is rare and is associated with relatively poor prognosis. No information is available on its biological features at the genomic level. Using a dataset of 54 pregnant and 113 non-pregnant breast cancer patients, we evaluated the pattern of hot spot somatic mutations and did transcriptomic profiling using Sequenom and Affymetrix respectively. We performed gene set enrichment analysis to evaluate the pathways associated with diagnosis during pregnancy. We also evaluated the expression of selected cancer-related genes in pregnant and non-pregnant patients and correlated the results with changes occurring in the normal breast using a pregnant murine model. We finally investigated aberrations associated with disease-free survival (DFS). No significant differences in mutations were observed. Of the total number of patients, 18.6% of pregnant and 23% of non-pregnant patients had a PIK3CA mutation. Around 30% of tumors were basal, with no differences in the distribution of breast cancer molecular subtypes between pregnant and non-pregnant patients. Two pathways were enriched in tumors diagnosed during pregnancy: the G protein-coupled receptor pathway and the serotonin receptor pathway (FDR <0.0001). Tumors diagnosed during pregnancy had higher expression of PD1 (PDCD1; P=0.015), PDL1 (CD274; P=0.014), and gene sets related to SRC (P=0.004), IGF1 (P=0.032), and β-catenin (P=0.019). Their expression increased almost linearly throughout gestation when evaluated on the normal breast using a pregnant mouse model underscoring the potential effect of the breast microenvironment on tumor phenotype. No genes were associated with DFS in a multivariate model, which could be due to low statistical power. Diagnosis during pregnancy impacts the breast cancer transcriptome including potential cancer targets.

Converting the yeast arginine can1 permease to a lysine permease

Amino acid uptake in yeast cells is mediated by about 16 plasma membrane permeases, most of which belong to the amino acid-polyamine-organocation (APC) transporter family. These proteins display various substrate specificity ranges. For instance, the general amino acid permease Gap1 transports all amino acids, whereas Can1 and Lyp1 catalyze specific uptake of arginine and lysine, respectively. Although Can1 and Lyp1 have different narrow substrate specificities, they are close homologs. Here we investigated the molecular rules determining the substrate specificity of the H(+)-driven arginine-specific permease Can1. Using a Can1-Lyp1 sequence alignment as a guideline and a three-dimensional Can1 structural model based on the crystal structure of the bacterial APC family arginine/agmatine antiporter, we introduced amino acid substitutions liable to alter Can1 substrate specificity. We show that the single substitution T456S results in a Can1 variant transporting lysine in addition to arginine and that the combined substitutions T456S and S176N convert Can1 to a Lyp1-like permease. Replacement of a highly conserved glutamate in the Can1 binding site leads to variants (E184Q and E184A) incapable of any amino acid transport, pointing to a potential role for this glutamate in H(+) coupling. Measurements of the kinetic parameters of arginine and lysine uptake by the wild-type and mutant Can1 permeases, together with docking calculations for each amino acid in their binding site, suggest a model in which residues at positions 176 and 456 confer substrate selectivity at the ligand-binding stage and/or in the course of conformational changes required for transport.

D-peaks: a visual tool to display ChIP-seq peaks along the genome

ChIP-sequencing is a method of choice to localize the positions of protein binding sites on DNA on a whole genomic scale. The deciphering of the sequencing data produced by this novel technique is challenging and it is achieved by their rigorous interpretation using dedicated tools and adapted visualization programs. Here, we present a bioinformatics tool (D-peaks) that adds several possibilities (including, user-friendliness, high-quality, relative position with respect to the genomic features) to the well-known visualization browsers or databases already existing. D-peaks is directly available through its web interface http://rsat.ulb.ac.be/dpeaks/ as well as a command line tool.

Using the NeAT toolbox to compare networks to networks, clusters to clusters, and network to clusters

In this chapter, we present and interpret some operations on biological networks that can easily performed with NeAT, a set of Web tools aimed at studying biological networks (or graphs) and classifications. These approaches are of particular interest for biologists and scientists who need to assess the reliability of new datasets (either experimental or predicted) by comparing them to established references. Firstly, we describe the steps that will allow a nonspecialist user to compare two networks to compute their union and the statistical significance of their intersection. Next, we show how to map functional classes (e.g., GO categories, sets of regulons or complexes) onto a biological network. A third protocol explains how to compare two sets of functional classes, e.g., to assess statistically the biological relevance of some computationally returned groups of genes (clustering). The metrics as well as the results obtained by following the different protocols are extensively described and explained. NeAT is available at the following URL: http://rsat.bigre.ulb.ac.be/rsat/index_neat.html.

Unraveling networks of co-regulated genes on the sole basis of genome sequences

With the growing number of available microbial genome sequences, regulatory signals can now be revealed as conserved motifs in promoters of orthologous genes (phylogenetic footprints). A next challenge is to unravel genome-scale regulatory networks. Using as sole input genome sequences, we predicted cis-regulatory elements for each gene of the yeast Saccharomyces cerevisiae by discovering over-represented motifs in the promoters of their orthologs in 19 Saccharomycetes species. We then linked all genes displaying similar motifs in their promoter regions and inferred a co-regulation network including 56,919 links between 3171 genes. Comparison with annotated regulons highlights the high predictive value of the method: a majority of the top-scoring predictions correspond to already known co-regulations. We also show that this inferred network is as accurate as a co-expression network built from hundreds of transcriptome microarray experiments. Furthermore, we experimentally validated 14 among 16 new functional links between orphan genes and known regulons. This approach can be readily applied to unravel gene regulatory networks from hundreds of microbial genomes for which no other information is available except the sequence. Long-term benefits can easily be perceived when considering the exponential increase of new genome sequences.

Biological knowledge bases using Wikis: combining the flexibility of Wikis with the structure of databases

SUMMARY

In recent years, the number of knowledge bases developed using Wiki technology has exploded. Unfortunately, next to their numerous advantages, classical Wikis present a critical limitation: the invaluable knowledge they gather is represented as free text, which hinders their computational exploitation. This is in sharp contrast with the current practice for biological databases where the data is made available in a structured way. Here, we present WikiOpener an extension for the classical MediaWiki engine that augments Wiki pages by allowing on-the-fly querying and formatting resources external to the Wiki. Those resources may provide data extracted from databases or DAS tracks, or even results returned by local or remote bioinformatics analysis tools. This also implies that structured data can be edited via dedicated forms. Hence, this generic resource combines the structure of biological databases with the flexibility of collaborative Wikis.

AVAILABILITY

The source code and its documentation are freely available on the MediaWiki website: http://www.mediawiki.org/wiki/Extension:WikiOpener.

Collaboratively charting the gene-to-phenotype network of human congenital heart defects

Background

How to efficiently integrate the daily practice of molecular biologists, geneticists, and clinicians with the emerging computational strategies from systems biology is still much of an open question.

Description

We built on the recent advances in Wiki-based technologies to develop a collaborative knowledge base and gene prioritization portal aimed at mapping genes and genomic regions, and untangling their relations with corresponding human phenotypes, congenital heart defects (CHDs). This portal is not only an evolving community repository of current knowledge on the genetic basis of CHDs, but also a collaborative environment for the study of candidate genes potentially implicated in CHDs - in particular by integrating recent strategies for the statistical prioritization of candidate genes. It thus serves and connects the broad community that is facing CHDs, ranging from the pediatric cardiologist and clinical geneticist to the basic investigator of cardiogenesis.

Conclusions

This study describes the first specialized portal to collaboratively annotate and analyze gene-phenotype networks. Of broad interest to the biological community, we argue that such portals will play a significant role in systems biology studies of numerous complex biological processes.

CHDWiki is accessible at http://www.esat.kuleuven.be/~bioiuser/chdwiki

NeAT: a toolbox for the analysis of biological networks, clusters, classes and pathways

The network analysis tools (NeAT) (http://rsat.ulb.ac.be/neat/) provide a user-friendly web access to a collection of modular tools for the analysis of networks (graphs) and clusters (e.g. microarray clusters, functional classes, etc.). A first set of tools supports basic operations on graphs (comparison between two graphs, neighborhood of a set of input nodes, path finding and graph randomization). Another set of programs makes the connection between networks and clusters (graph-based clustering, cliques discovery and mapping of clusters onto a network). The toolbox also includes programs for detecting significant intersections between clusters/classes (e.g. clusters of co-expression versus functional classes of genes). NeAT are designed to cope with large datasets and provide a flexible toolbox for analyzing biological networks stored in various databases (protein interactions, regulation and metabolism) or obtained from high-throughput experiments (two-hybrid, mass-spectrometry and microarrays). The web interface interconnects the programs in predefined analysis flows, enabling to address a series of questions about networks of interest. Each tool can also be used separately by entering custom data for a specific analysis. NeAT can also be used as web services (SOAP/WSDL interface), in order to design programmatic workflows and integrate them with other available resources.

RSAT: regulatory sequence analysis tools

The regulatory sequence analysis tools (RSAT, http://rsat.ulb.ac.be/rsat/) is a software suite that integrates a wide collection of modular tools for the detection of cis-regulatory elements in genome sequences. The suite includes programs for sequence retrieval, pattern discovery, phylogenetic footprint detection, pattern matching, genome scanning and feature map drawing. Random controls can be performed with random gene selections or by generating random sequences according to a variety of background models (Bernoulli, Markov). Beyond the original word-based pattern-discovery tools (oligo-analysis and dyad-analysis), we recently added a battery of tools for matrix-based detection of cis-acting elements, with some original features (adaptive background models, Markov-chain estimation of P-values) that do not exist in other matrix-based scanning tools. The web server offers an intuitive interface, where each program can be accessed either separately or connected to the other tools. In addition, the tools are now available as web services, enabling their integration in programmatic workflows. Genomes are regularly updated from various genome repositories (NCBI and EnsEMBL) and 682 organisms are currently supported. Since 1998, the tools have been used by several hundreds of researchers from all over the world. Several predictions made with RSAT were validated experimentally and published.

Evaluation of clustering algorithms for protein-protein interaction networks

Background

Protein interactions are crucial components of all cellular processes. Recently, high-throughput methods have been developed to obtain a global description of the interactome (the whole network of protein interactions for a given organism). In 2002, the yeast interactome was estimated to contain up to 80,000 potential interactions. This estimate is based on the integration of data sets obtained by various methods (mass spectrometry, two-hybrid methods, genetic studies). High-throughput methods are known, however, to yield a non-negligible rate of false positives, and to miss a fraction of existing interactions.

The interactome can be represented as a graph where nodes correspond with proteins and edges with pairwise interactions. In recent years clustering methods have been developed and applied in order to extract relevant modules from such graphs. These algorithms require the specification of parameters that may drastically affect the results. In this paper we present a comparative assessment of four algorithms: Markov Clustering (MCL), Restricted Neighborhood Search Clustering (RNSC), Super Paramagnetic Clustering (SPC), and Molecular Complex Detection (MCODE).

Results

A test graph was built on the basis of 220 complexes annotated in the MIPS database. To evaluate the robustness to false positives and false negatives, we derived 41 altered graphs by randomly removing edges from or adding edges to the test graph in various proportions.

Each clustering algorithm was applied to these graphs with various parameter settings, and the clusters were compared with the annotated complexes.

We analyzed the sensitivity of the algorithms to the parameters and determined their optimal parameter values.

We also evaluated their robustness to alterations of the test graph.

We then applied the four algorithms to six graphs obtained from high-throughput experiments and compared the resulting clusters with the annotated complexes.

Conclusion

This analysis shows that MCL is remarkably robust to graph alterations. In the tests of robustness, RNSC is more sensitive to edge deletion but less sensitive to the use of suboptimal parameter values. The other two algorithms are clearly weaker under most conditions.

The analysis of high-throughput data supports the superiority of MCL for the extraction of complexes from interaction networks.