bluuebunny/crossref_metadata_2025_split huggingface dataset:
A comprehensive analysis of nine prognostic signatures reveals the high classification instability in breast cancer.
Authors: Reyal F , Vliet MH , Horlings HH , Armstrong N , de Visser KE , Kok M , Teschendorff AE , Mook S , Van't Veer L , Caldas C , Salmon RJ , Van de Vijver MJ , Wessels LF 
Published: December 2010

Abstract
Abstract #2026
To determine the prognosis of breast cancer patients, clinical and pathological factors are currently employed. Gene expression micro-arrays offer new opportunities to determine individual prognosis. Publications have raised concerns about micro-arrays studies who have the potential to preclude their use in clinical routine. To improve the understanding of gene-expression classifiers we addressed the following issues: 1) Is the performance similar between independent classifiers? 2) Is proliferation a common biological theme that represents various signatures? 3) Are there other enriched pathways among signatures with prognostic ability?&#x2028; Methods:&#x2028; On 6 public datasets we applied the 76-gene signature; the Molecular subtypes; the Chromosomal Instability Signature; the Wound Signature; the Invasiveness Gene Signature; the Molecular Prognosis Index; and the Genomic Grade Index. Survival, predictive accuracy and overlap analyses were performed. We created enlarged signatures by including all probes with significant correlation to at least one of the genes in the original signatures. We gathered a collection of gene sets from four databases (GO, KEGG, Reactome, MSDB). For each signature, we evaluated whether specific gene sets (modules) are overrepresented. We tested the prognosis ability of each of them.&#x2028; Results:&#x2028; The survival and predictive accuracy analyses gave similar results for each of the 9 signatures. They all added significant information to a multivariate model including standard pathological and clinical criteria. Nevertheless, we showed that none of these signatures were able to identify good and poor prognosis patients when applied to samples with intrinsically poor prognosis features (Positive Lymph Node, Negative Estrogen Receptor, High Grade). Conversely they identified good and poor prognosis patients when applied to samples with intrinsically good prognosis features (Negative LN, Positive ER Low Grade). The overlap analysis showed a low agreement between the signatures. 50% of the samples had almost one discordant classification result out of the 9 classifiers tested. The intersection of the signatures revealed a set of proliferation genes. The signatures were build on 10 different gene ontology modules with prognostic ability.&#x2028; Conclusion:&#x2028; This study underlines the need of large prospective validation studies of gene expression signatures. Further computational intelligence and system biology studies would be held to determine the best way to use these classifiers in clinical routine.
Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 2026.
Molecular characteristics of matched brain metastasis (BM) versus the primary breast cancer (PBC).
Authors: Duchnowska R , Jassem J , Shen C , Thorat M , Li L , Morimiya A , Zhao Q , Biernat W , Mandat T , Staszkiewicz R , Och W , Szostak W , Gugala K , Trojanowski T , Czartoryska-Arlukowicz B , Szczylik C , Nakshatri H , Steeg P , Sledge G , Badve S 
Published: December 2010

Abstract
Abstract #2028
Background: Brain is increasingly a site of relapse in breast cancer patients, however the molecular patho-physiology of this process is not well understood. Using a novel assay allowing high-throughput analysis of gene expression from formalin-fixed paraffin-embedded (FFPET) tumor samples, we compared molecular characteristics of BM with those of the PBC in a series of breast cancer patients who underwent excision of brain metastasis.&#x2028; Methods: In each patient we performed molecular analysis of paired archived FFPET specimens from both the PBC and excised BM. Of the 37 pairs of PBC/BM samples, 24 pairs had sufficient pathology material for molecular analysis. Patient characteristics: mean age at PBC diagnosis was 46.7 years (range 27-67 years); 7 (29%) ER+, 17 (71%) ER-, 12 (50%) HER2-positive(3+). We performed cDNA-mediated annealing, selection, extension and ligation (DASL) assay (Illumina Corp) for expression of 502 known cancer genes, using 200 ng RNA. Statistical analysis for microarrays (SAM) accounting for the pairing of the primary and metastasized tumors was used to identify differentially expressed genes while controlling the false discovery rate (FDR &lt;0.01). BeadStudio™ Absolute correlation clustering was used to cluster samples based on their expression profiles.&#x2028; Results: In only 7 (29%) cases matched PBC and BM pair clustered together on unsupervised hierarchical clustering, the pairs in remaining cases clustered apart. Comparison of the two groups (PBC and BM) showed that 41 genes were up regulated in BM, including proliferation genes (CDC2, CDC25a, CCNA2 and E2F family), anti-apoptotic (BIRC5), DNA repair (RAD51, -54b, XRCC2, BRCA2, BARD1, TOP1), angiogenesis (VEGF), and development of drug resistance (ABCB1, -G2). In contrast, the 43 genes that were down regulated in BM included those associated with invasion (MMP2, -3, -14), cellular motility, and epithelial to mesenchymal transformation (CDH11). Additional analysis to validate these trends and to identify potential therapeutic targets is underway.&#x2028; Conclusions: Although some BM retain remarkable similarity to the PBC, majority exhibit considerable deviation in their gene expression profile. These “adaptive” changes include greater resistance to drug therapy, increased DNA repair, a reversal back to the epithelial phenotype and decreased capacity for cell motility and invasion.
Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 2028.
An exonic expression profile for breast cancer diagnosis of fine needle aspiration biopsies.
Authors: Delaloge S , Scott V , Michiels S , Dessen P , Suciu V , Spielmann M , Vielh P , André F 
Published: December 2010

Abstract
Abstract #2029
Background: Fine needle aspiration (FNA) for breast cancer diagnosis, a simple, quick and cheap procedure, is limited by a significant number of inconclusive exams. In the present study, we have analyzed whether the expression of a set of exonic probes could accurately diagnose breast cancer of FNA biopsies.&#x2028; Patients and methods: cDNA from 165 breast lesions (120 cancers), obtained by FNA in 2006-2007, and for which RIN score was &gt; 6, was hybridized on splice arrayTM in two separate series (training/validation)(data set Andre et al SABCS 2008). We focused on 703 680 evidenced probe sets. The actual diagnosis of breast cancer was established by pathological examination at surgery. Associations between exonic probes and diagnosis were assessed by correlation coefficients (using t-tests) and a nearest centroid prediction rule was determined on the training set of 94 samples (70 cancers) in order to classify lesions as malignant or benign. Using the validation set of 71 samples (50 cancers), the performance of the prediction rule was evaluated and the proportion of explained variation (PEV) estimated in a logistic regression model (including only variables significant by a likelihood ratio test).&#x2028; &#x2028; Results: In a leave-one-out cross-validation analysis, the nearest prediction rule based on the expression of 1228 exonic probes (all probes with p&lt;10e-9) accurately classified all samples from the training set. In the validation set, 68 out of 71 samples were accurately classified (96%) by this exonic expression profile of 1228 probes. Sensitivity and specificity were 96% (exact 95% CI: 86-100%) and 95% (76-100%). The cytological exam failed to provide a definitive diagnosis in 5 out of 71 patients. Four out of these 5 lesions were accurately classified by the nearest prediction rule. The PEV obtained by a clinical model (age and BI-RADS classification) was 75% (bootstrap standard error +/-8%) and the PEV by the exonic profile alone 81% (+/- 10%) in the validation set. When the clinical model and the exonic profile were combined, the PEV was 87% (+/-7%).&#x2028; Conclusion: We have developed a profile based on the expression of 1228 exonic probes for breast cancer diagnosis using FNA samples. This exonic profile could be a complementary tool to clinical variables and to the cytologic exam to decide about surgery in post-screening programs.
Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 2029.
A reduced qRT-PCR-based gene expression signature with prognostic value in early breast cancer.
Authors: Espinosa E , Gámez A , Sánchez I , Pinto [ , Hardisson D , Madero R , Zamora P , Redondo A , González Barón M , Fresno Vara J 
Published: December 2010

Abstract
Abstract #2030
Background: Gene profiling may improve prognostic accuracy in patients with early breast cancer, but this technology is not widely available. We used commercial assays for qRT-PCR to measure the expression of 83 genes previously found to determine breast cancer prognosis. The objective of our study was to develop a simple molecular signature predicting relapse in early breast cancer with positive hormonal receptors.&#x2028; Methods: 153 patients (age 29-82 years-old) with early breast cancer and a minimum follow-up of 5 years were included. All tumours were positive for hormonal receptors and 38% had positive lymph nodes; 64% of patients received adjuvant chemotherapy (with anthracycline if N+ or high-risk N0). RNA was isolated from tissue slices of formalin-fixed paraffin-embedded samples using the MasterPure RNA Purification Kit (Epicentre Biotech). qRT-PCR amplifications were performed with TaqMan Gene Expression Assays products in an ABI PRISM 7900 HT Sequence Detection System (Applied Biosystems). The reactions were carried out using the TaqMan Low Density Arrays (Applied Biosystems). A supervised analysis was done to identify a prognostic expression signature using BRB Array Tools software. Survival curves were derived from Kaplan-Meier estimates and compared by log-rank test. The association of gene expression and clinical variables with distant metastasis-free survival (DMFS) was analyzed by Cox regression models. SAS 9.1 software package was used for statistical analyses.&#x2028; Results: After a median follow-up of 92 months, 28% of patients relapsed. We defined an 8-gene prognostic signature. The distant metastasis-free survival at 5 years was calculated for this profile. DMFS was 97% -good risk- versus 60% -poor risk, HR 20.3 (95% CI 6.17-67.2), p&lt;0.001. This gene expression profile was highly informative in identifying patients with distant metastasis, even when corrected for traditional prognostic factors in Cox multivariate analysis (HR 20, 95% CI 5.9-67.4). Performance was similar to that of three validated gene profiles. The validity of this signature was verified in an independent cohort obtained at the GEO database.&#x2028; Conclusions: This study identifies a new molecular signature with prognostic utility that outperforms traditional histopathologic prognostic factors. Our 8-gene qRT-PCR assay is suitable for analyzing routine formalin-fixed paraffin-embedded samples in the clinical setting.
Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 2030.
Predicting response to RAD001 treatment in post-menopausal women with ER-positive breast cancer using the breast cancer DSA™ research tool (Almac).
Authors: Sabine VS , McCulla A , Wilson CL , Sims AH , McDyer F , McCluskey C , Halfpenny I , Oliver GR , Macaskill EJ , Renshaw L , Thomas JS , Dixon JM , Bartlett JM 
Published: December 2010

Abstract
Abstract #2032
Background: RAD001 (everolimus) is a rapamycin derivative that inhibits mTOR and its downstream substrates and has shown promising results in Phase I trials. The aim of this study was to assess the feasibility of predicting patient response to RAD001 treatment from pre-treatment biopsies.&#x2028; Materials & Methods: 32 post-menopausal, ER-positive early breast cancer patients received 5mg RAD001 pre-operative treatment daily for 14 days prior to primary surgery. 25 patients completed the regimen, 6 patients withdrew and 1 did not start treatment. Biopsies were taken at diagnosis and at surgery from the RAD001 treated patients. Pre-treatment samples were flash frozen prior to RNA isolation. Patient response status was classified based on the % of Ki-67 positive cells determined by immunohistochemistry in the post-treatment biopsy (Macaskill et al., 2006: Breast Cancer Research & Treatment: 100: S1: S286). RNA from the pre-treatment group was analysed using the Almac Diagnostics Breast Cancer DSATM Research Tool, which represents ∼60,000 normal breast and breast cancer transcripts and Illumina Human Ref.8 chip v2 containing ∼24,500 transcripts representing well-categorised and most commonly known genes, irrespective of disease or tissue. For each array platform, modulated genes were identified using statistically robust gene selection methods. In addition, the sequence content of the two platforms was compared by probe-level analysis using BLAST.&#x2028; Results & Discussion: Preliminary assessment of the expression differences between the non-responder and responder groups using the DSA™ Research Tool demonstrated 169 up-regulated and 93 down-regulated genes. This list is comprised of genes involved in chemokine activity, cell adhesion and transport. Cross-platform content comparison demonstrated that the Illumina bead arrays represented 41% of transcripts on the DSA™ Research Tool whilst 82% of the Illumina beadarray transcripts were represented on the DSA™ Research Tool. Genes of interest identified will be validated by quantitative real time PCR and the key pathways reported.&#x2028; These datasets have enabled us to begin the identification of tumours most suited to RAD001 treatment and may be used as a guide for the generation of a gene expression classifier for predicting patient response to RAD001.&#x2028; Acknowledgements: This work was supported by a grant from Breast Cancer Research Treatment, UK.
Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 2032.
Prediction of chemotherapy response in breast cancer by using a four-gene panel including osteopontin, activating leukocyte cell adhesion molecule (ALCAM), HER2 and estrogen receptor (ER).
Authors: Ihnen M , Müller V , Wirtz RM , Milde-Langosch K , Witzel I , Lisboa BW , Jänicke F 
Published: December 2010

Abstract
Abstract #2033
Background: Several prognostic factors such as TNM-stage, hormone receptor and HER2 status refer to therapeutical decisions towards adjuvant chemotherapy. Up to now it is still difficult to predict the response to chemotherapy for a single patient, which is a challenging problem in order to spare patients from cost-intensive ineffective treatment and adverse side effects. Therefore there is urgent need for additional predictive markers. Our previous findings implicated that tumor tissue expression of Activated Leucocyte Cell Adhesion Molecule (ALCAM/CD166) is associated with chemotherapy response. Osteopontin (OPN) has been described as prognostic marker in breast cancer and recently it was shown that high OPN levels in vitro mediate chemoresistance of breast cancer cells by inhibition of apoptosis. If these findings result in chemoresistance in vivo has not been investigated so far. Material and Methods: Primary breast cancer tissues from 100 patients treated with Taxane-free adjuvant standard chemotherapy regimen were collected at surgery prior to any additional therapy. The median follow-up time was 81 months. We analyzed ALCAM and OPN mRNA expression in the biological context of additional and predefined breast cancer markers, like estrogen receptor (ER), Her-2/neu (HER2), members of the urokinase plasminogen activator (uPA) system, vascular endothelial growth factor (VEGF) using oligonucleotide microarrays (Affymetrix HG-U133A). Hierarchical cluster analysis was performed to develop a gene algorithm predicting outcome after standard chemotherapy. Results: In contrast to ALCAM overexpression, which was associated with better outcome upon chemotherapy, OPN overexpression was associated with a significantly higher recurrence rate (p=0,027), but not with shorter overall survival. In addition, there were significant positive correlations of OPN with uPA, PAI-1, uPAR and VEGF-A mRNA expression. By cluster analysis based on the four markers ER, HER2, ALCAM and OPN, we identified a group of high-risk patients, which was characterized by low ER and HER2 expression, high OPN and low ALCAM levels, and with significantly shorter recurrence-free and overall survival (p&lt;0,001). Discussion: In our cohort of patients the combination of ALCAM, OPN, ER and HER2 expression levels turned out as valuable predictive marker for response to adjuvant Taxane-free chemotherapy. The ratio of high OPN and low ALCAM levels was particularly associated with chemoresistance and a poor prognosis in tumors having low ER and HER2 expression levels. We hypothesize that this simple four-gene panel could help to reassign an optimized therapy to the single patients.
Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 2033.
The predictive probability of four different breast cancer nomograms for non-sentinel axillary lymph node metastasis in positive sentinel lymph node biopsy.
Authors: Gur SA , Unal B , Johnson R , Ahrendt G , Bonaventura M , Soran A 
Published: December 2010

Abstract
Abstract #204
Introduction: Although completion axillary lymph node dissection (CALND) is the standard for evaluating axillary status after identification of a positive sentinel lymph node (SLN) in breast cancer (BC), almost 50% of SLN positive patients will have negative non-SLN and undergo a non-therapeutic axillary dissection. Accurate estimates of the likelihood of additional disease in the axilla can assist decision-making about further treatment. In order to predict non- sentinel lymph node metastases (NSLNM) in patients with a positive SLN biopsy, four different nomograms have been created. The Memorial Sloan Kettering Cancer Center (MSKCC) nomogram was created in 2003 and validated at sources outside that institution. The other nomograms were developed by the Tenon Hospital, the Cambridge University and the Stanford University group recently, but have not been validated yet. The aim of this study was to evaluate the accuracy of 4 different nomograms in our SLN positive BC patients. Methods: We identified 319 patients who had a positive SLN biopsy and CALND at Magee-Womens Hospital of the University of Pittsburgh Medical Center over an 8 year period. The BC nomograms developed by the MSKCC, the Tenon Hospital, the Cambridge University and the Stanford University were used to calculate the probability of NSLNM. Area Under (AUC) Receiver Operating Characteristics Curve (ROC) was calculated for each nomogram a value more than 0.70 was accepted as providing considerable discrimination. Results: One hundred-seven of 319 patients (33.5%) had positive axillary NSLNM. The mean predicted probability of positive NSLNM was 28.1 % (3-93), 67.1% (4.8-100) and, 39.1% (3.6-97.5) for the MSKCC, the Cambridge, and the Stanford nomograms, respectively. The mean point for the Tenon model was 4.45 (0-7). The AUC values were 0.70, 0.69, 0.69 and 0.64 for the MSKCC, the Tenon, the Cambridge and, the Stanford models, respectively. Conclusion: Breast nomograms can be used in the current practice to provide an accurate prediction for identifying low or high risk for NSLNM in BC. In our study, we found the MSKCC nomogram is more predictive than the other nomograms but the Cambridge model and the Tenon model reached borderline values for the good prediction. A model can provide more accurate results with its original group, but may be insufficient on the different groups. Nomograms developed at other institutions should be used with caution when counseling patients regarding the risk of additional nodal disease.
Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 204.
MicroRNA profiling of triple negative breast cancer: predicting outcome and targets.
Authors: Paranjape TS , Nallur SV , Keanie K , Martel M , Haffty BG , Tuck DP , Slack F , Weidhaas JB 
Published: December 2010

Abstract
Abstract #2040
Background: Breast tumors have been categorized on their molecular basis into ER- and/or PR positive, HER2 gene amplified and triple-negative tumors. Among these, triple-negative tumors represent the most aggressive subclass of breast tumors that are most prevalent in African American women. Currently, no effective targeted therapies or potential biomarkers have been identified to predict outcome within this group.&#x2028; MicroRNAs(miRNAs) are global regulatory RNAs misregulated in almost every cancer and may function as tumor suppressor genes or proto-oncogenes. Specific miRNAs are aberrantly expressed in breast tumor versus normal tissue. Although miRNA expression profiles for the ER positive and HER2 positive subtype have been reported, the triple negative subtype remains to be studied.&#x2028; Hypothesis: We hypothesize that identifying the miRNA signature of the triple negative tumors as a subclass as well as differences in this signature between triple negative patients with known outcome will lead to the identification of miRNAs predictive of outcome. For these studies we will use a large, well-annotated database of triple negative breast cancer patients with long-term follow-up. We will then test the identified miRNAs in a well-established cell-culture system to determine which miRNAs can act as potential therapeutic agents in triple negative breast tumors.&#x2028; Study Design: We have performed miRNA microarray analysis to identify miRNA expression patterns in over 40 triple negative breast cancers using both fresh frozen tumors and formalin-fixed paraffin embedded (FFPE) samples. We have used the Ambion miRNA Taqman Low Density Array for analysis of these samples. The potential of specific miRNAs to predict outcome is currently being determined using statistical analysis.&#x2028; Results: Our initial studies indicated that the triple negative subgroup has a significantly different miRNA signature than the HER2 positive subtype. We also additionally found unique miRNA expression patterns that may define triple negative cancers as a class. The triple negative tumors are known to have low levels of p53 and our microarray data showed that miR-34a which has been shown to be co-regulated by p53 and has similar expression levels as p53 in tissues, was significantly low in the triple negative compared to HER 2 positive breast cancer. We are currently evaluating the impact of altering mir-34 expression in these cell lines in vitro.
Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 2040.