Annuska M. Glas

Bio: Annuska M. Glas is an academic researcher from Netherlands Cancer Institute. The author has contributed to research in topics: Breast cancer & MammaPrint. The author has an hindex of 39, co-authored 103 publications receiving 13475 citations.

A Gene-Expression Signature as a Predictor of Survival in Breast Cancer

Abstract: Background A more accurate means of prognostication in breast cancer will improve the selection of patients for adjuvant systemic therapy. Methods Using microarray analysis to evaluate our previously established 70-gene prognosis profile, we classified a series of 295 consecutive patients with primary breast carcinomas as having a gene-expression signature associated with either a poor prognosis or a good prognosis. All patients had stage I or II breast cancer and were younger than 53 years old; 151 had lymph-node–negative disease, and 144 had lymph-node–positive disease. We evaluated the predictive power of the prognosis profile using univariable and multivariable statistical analyses. Results Among the 295 patients, 180 had a poor-prognosis signature and 115 had a good-prognosis signature, and the mean (±SE) overall 10-year survival rates were 54.6±4.4 percent and 94.5±2.6 percent, respectively. At 10 years, the probability of remaining free of distant metastases was 50.6±4.5 percent in the group with a...

70-Gene Signature as an Aid to Treatment Decisions in Early-Stage Breast Cancer

Abstract: BackgroundThe 70-gene signature test (MammaPrint) has been shown to improve prediction of clinical outcome in women with early-stage breast cancer. We sought to provide prospective evidence of the clinical utility of the addition of the 70-gene signature to standard clinical–pathological criteria in selecting patients for adjuvant chemotherapy. MethodsIn this randomized, phase 3 study, we enrolled 6693 women with early-stage breast cancer and determined their genomic risk (using the 70-gene signature) and their clinical risk (using a modified version of Adjuvant! Online). Women at low clinical and genomic risk did not receive chemotherapy, whereas those at high clinical and genomic risk did receive such therapy. In patients with discordant risk results, either the genomic risk or the clinical risk was used to determine the use of chemotherapy. The primary goal was to assess whether, among patients with high-risk clinical features and a low-risk gene-expression profile who did not receive chemotherapy, the...

Validation and Clinical Utility of a 70-Gene Prognostic Signature for Women With Node-Negative Breast Cancer

Abstract: Background: A 70-gene signature was previously shown to have prognostic value in patients with node-negative breast cancer. Our goal was to validate the signature in an independent group of patients. Methods: Patients (n = 307, with 137 events after a median follow-up of 13.6 years) from fi ve European centers were divided into high- and low-risk groups based on the gene signature classifi cation and on clinical risk classifi cations. Patients were assigned to the gene signature low-risk group if their 5-year distant metastasis – free survival probability as estimated by the gene signature was greater than 90%. Patients were assigned to the clinicopathologic low-risk group if their 10-year survival probability, as estimated by Adjuvant! software, was greater than 88% (for estrogen receptor [ER] – positive patients) or 92% (for ERnegative patients). Hazard ratios (HRs) were estimated to compare time to distant metastases, disease-free survival, and overall survival in high- versus low-risk groups. Results: The 70-gene signature outperformed the clinicopathologic risk assessment in predicting all endpoints. For time to distant metastases, the gene signature yielded HR = 2.32 (95% confi dence interval [CI] = 1.35 to 4.00) without adjustment for clinical risk and hazard ratios ranging from 2.13 to 2.15 after adjustment for various estimates of clinical risk; clinicopathologic risk using Adjuvant! software yielded an unadjusted HR = 1.68 (95% CI = 0.92 to 3.07). For overall survival, the gene signature yielded an unadjusted HR = 2.79 (95% CI = 1.60 to 4.87) and adjusted hazard ratios ranging from 2.63 to 2.89; clinicopathologic risk yielded an unadjusted HR = 1.67 (95% CI = 0.93 to 2.98). For patients in the gene signature high-risk group, 10-year overall survival was 0.69 for patients in both the low – and high – clinical risk groups; for patients in the gene signature low-risk group, the 10-year survival rates were 0.88 and 0.89, respectively. Conclusions: The 70-gene signature adds independent prognostic information to clinicopathologic risk assessment for patients with early breast cancer. [J Natl Cancer Inst 2006;98: 1183 – 92 ]

Gene Expression Signature to Improve Prognosis Prediction of Stage II and III Colorectal Cancer

Abstract: Purpose This study aims to develop a robust gene expression classifier that can predict disease relapse in patients with early-stage colorectal cancer (CRC). Patients and Methods Fresh frozen tumor tissue from 188 patients with stage I to IV CRC undergoing surgery was analyzed using Agilent 44K oligonucleotide arrays. Median follow-up time was 65.1 months, and the majority of patients (83.6%) did not receive adjuvant chemotherapy. A nearest mean classifier was developed using a cross-validation procedure to score all genes for their association with 5-year distant metastasis–free survival. Results An optimal set of 18 genes was identified and used to construct a prognostic classifier (ColoPrint). The signature was validated on an independent set of 206 samples from patients with stage I, II, and III CRC. The signature classified 60% of patients as low risk and 40% as high risk. Five-year relapse-free survival rates were 87.6% (95% CI, 81.5% to 93.7%) and 67.2% (95% CI, 55.4% to 79.0%) for low- and high-ri...

Converting a breast cancer microarray signature into a high-throughput diagnostic test.

Abstract: Background: A 70-gene tumor expression profile was established as a powerful predictor of disease outcome in young breast cancer patients. This profile, however, was generated on microarrays containing 25,000 60-mer oligonucleotides that are not designed for processing of many samples on a routine basis. Results: To facilitate its use in a diagnostic setting, the 70-gene prognosis profile was translated into a customized microarray (MammaPrint) containing a reduced set of 1,900 probes suitable for high throughput processing. RNA of 162 patient samples from two previous studies was subjected to hybridization to this custom array to validate the prognostic value. Classification results obtained from the original analysis were then compared to those generated using the algorithms based on the custom microarray and showed an extremely high correlation of prognosis prediction between the original data and those generated using the custom mini-array (p < 0.0001). Conclusion: In this report we demonstrate for the first time that microarray technology can be used as a reliable diagnostic tool. The data clearly demonstrate the reproducibility and robustness of the small custom-made microarray. The array is therefore an excellent tool to predict outcome of disease in breast cancer patients.

WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues

Abstract: WHO CLASSIFICATION OF TUMOURS OF HAEMATOPOIETIC AND LYMPHOID TISSUES , WHO CLASSIFICATION OF TUMOURS OF HAEMATOPOIETIC AND LYMPHOID TISSUES , کتابخانه مرکزی دانشگاه علوم پزشکی تهران

A Multigene Assay to Predict Recurrence of Tamoxifen-Treated, Node-Negative Breast Cancer

Abstract: background The likelihood of distant recurrence in patients with breast cancer who have no involved lymph nodes and estrogen-receptor–positive tumors is poorly defined by clinical and histopathological measures. methods We tested whether the results of a reverse-transcriptase–polymerase-chain-reaction (RT-PCR) assay of 21 prospectively selected genes in paraffin-embedded tumor tissue would correlate with the likelihood of distant recurrence in patients with node-negative, tamoxifen-treated breast cancer who were enrolled in the National Surgical Adjuvant Breast and Bowel Project clinical trial B-14. The levels of expression of 16 cancerrelated genes and 5 reference genes were used in a prospectively defined algorithm to calculate a recurrence score and to determine a risk group (low, intermediate, or high) for each patient. results Adequate RT-PCR profiles were obtained in 668 of 675 tumor blocks. The proportions of patients categorized as having a low, intermediate, or high risk by the RT-PCR assay were 51, 22, and 27 percent, respectively. The Kaplan–Meier estimates of the rates of distant recurrence at 10 years in the low-risk, intermediate-risk, and high-risk groups were 6.8 percent (95 percent confidence interval, 4.0 to 9.6), 14.3 percent (95 percent confidence interval, 8.3 to 20.3), and 30.5 percent (95 percent confidence interval, 23.6 to 37.4). The rate in the low-risk group was significantly lower than that in the high-risk group (P<0.001). In a multivariate Cox model, the recurrence score provided significant predictive power that was independent of age and tumor size (P<0.001). The recurrence score was also predictive of overall survival (P<0.001) and could be used as a continuous function to predict distant recurrence in individual patients. conclusions The recurrence score has been validated as quantifying the likelihood of distant recurrence in tamoxifen-treated patients with node-negative, estrogen-receptor–positive breast cancer.

Repeated observation of breast tumor subtypes in independent gene expression data sets

Abstract: Characteristic patterns of gene expression measured by DNA microarrays have been used to classify tumors into clinically relevant subgroups. In this study, we have refined the previously defined subtypes of breast tumors that could be distinguished by their distinct patterns of gene expression. A total of 115 malignant breast tumors were analyzed by hierarchical clustering based on patterns of expression of 534 "intrinsic" genes and shown to subdivide into one basal-like, one ERBB2-overexpressing, two luminal-like, and one normal breast tissue-like subgroup. The genes used for classification were selected based on their similar expression levels between pairs of consecutive samples taken from the same tumor separated by 15 weeks of neoadjuvant treatment. Similar cluster analyses of two published, independent data sets representing different patient cohorts from different laboratories, uncovered some of the same breast cancer subtypes. In the one data set that included information on time to development of distant metastasis, subtypes were associated with significant differences in this clinical feature. By including a group of tumors from BRCA1 carriers in the analysis, we found that this genotype predisposes to the basal tumor subtype. Our results strongly support the idea that many of these breast tumor subtypes represent biologically distinct disease entities.

Inferring tumour purity and stromal and immune cell admixture from expression data

Abstract: Infiltrating stromal and immune cells form the major fraction of normal cells in tumour tissue and not only perturb the tumour signal in molecular studies but also have an important role in cancer biology. Here we describe 'Estimation of STromal and Immune cells in MAlignant Tumours using Expression data' (ESTIMATE)--a method that uses gene expression signatures to infer the fraction of stromal and immune cells in tumour samples. ESTIMATE scores correlate with DNA copy number-based tumour purity across samples from 11 different tumour types, profiled on Agilent, Affymetrix platforms or based on RNA sequencing and available through The Cancer Genome Atlas. The prediction accuracy is further corroborated using 3,809 transcriptional profiles available elsewhere in the public domain. The ESTIMATE method allows consideration of tumour-associated normal cells in genomic and transcriptomic studies. An R-library is available on https://sourceforge.net/projects/estimateproject/.

Long non-coding RNA HOTAIR reprograms chromatin state to promote cancer metastasis

Abstract: Large intervening non-coding RNAs (lincRNAs) are pervasively transcribed in the genome yet their potential involvement in human disease is not well understood. Recent studies of dosage compensation, imprinting, and homeotic gene expression suggest that individual lincRNAs can function as the interface between DNA and specific chromatin remodelling activities. Here we show that lincRNAs in the HOX loci become systematically dysregulated during breast cancer progression. The lincRNA termed HOTAIR is increased in expression in primary breast tumours and metastases, and HOTAIR expression level in primary tumours is a powerful predictor of eventual metastasis and death. Enforced expression of HOTAIR in epithelial cancer cells induced genome-wide re-targeting of Polycomb repressive complex 2 (PRC2) to an occupancy pattern more resembling embryonic fibroblasts, leading to altered histone H3 lysine 27 methylation, gene expression, and increased cancer invasiveness and metastasis in a manner dependent on PRC2. Conversely, loss of HOTAIR can inhibit cancer invasiveness, particularly in cells that possess excessive PRC2 activity. These findings indicate that lincRNAs have active roles in modulating the cancer epigenome and may be important targets for cancer diagnosis and therapy.