Project description:Expression profiling of breast cancer tumours, comparing 10 year survivors to deceased patients Background It is of great significance to find better markers to correctly distinguish between high-risk and low-risk breast cancer patients since the majority of breast cancer cases are at present being overtreated. Methods 46 tumours from node-negative breast cancer patients were studied with gene expression microarrays. A t-test was carried out in order to find a set of genes where the expression might predict clinical outcome. Two classifiers were used to evaluate the gene lists on the different data sets, a correlation-based classifier and a VFI (Voting Features Interval) classifier. We then evaluated the predictive accuracy of this expression signature on tumour sets from two similar studies on lymph-node negative patients which had developed gene expression signatures superior to current methods in classifying node-negative breast tumours. These two signatures were also tested on our material. Results A list of 51 genes whose expression profiles could predict clinical outcome with high accuracy in our material (96% or 89% accuracy in cross-validation, depending on type of classifier) was developed. When tested on two independent data sets, the expression signature based on the 51 identified genes had good predictive qualities in one of the data sets (74% accuracy), whereas their predictive value on the other data set were poor, presumably due to the fact that only 23 of the 51 genes were found in that material. We also found that previously developed expression signatures could predict clinical outcome well to moderately well in our material (72% and 61%, respectively). Conclusion The list of 51 genes derived in this study might have potential for clinical utility as a prognostic gene set, and may include candidate genes of potential relevance for clinical outcome in breast cancer. According to the predictions by this expression signature, 30 of the 46 patients should have had different adjuvant treatment than they did. Keywords: Expression Microarray, Lymph-node-negative Breast Cancer, Clinical Outcome, Classification

Project description:Expression profiling of breast cancer tumours, comparing 10 year survivors to deceased patients Background It is of great significance to find better markers to correctly distinguish between high-risk and low-risk breast cancer patients since the majority of breast cancer cases are at present being overtreated. Methods 46 tumours from node-negative breast cancer patients were studied with gene expression microarrays. A t-test was carried out in order to find a set of genes where the expression might predict clinical outcome. Two classifiers were used to evaluate the gene lists on the different data sets, a correlation-based classifier and a VFI (Voting Features Interval) classifier. We then evaluated the predictive accuracy of this expression signature on tumour sets from two similar studies on lymph-node negative patients which had developed gene expression signatures superior to current methods in classifying node-negative breast tumours. These two signatures were also tested on our material. Results A list of 51 genes whose expression profiles could predict clinical outcome with high accuracy in our material (96% or 89% accuracy in cross-validation, depending on type of classifier) was developed. When tested on two independent data sets, the expression signature based on the 51 identified genes had good predictive qualities in one of the data sets (74% accuracy), whereas their predictive value on the other data set were poor, presumably due to the fact that only 23 of the 51 genes were found in that material. We also found that previously developed expression signatures could predict clinical outcome well to moderately well in our material (72% and 61%, respectively). Conclusion The list of 51 genes derived in this study might have potential for clinical utility as a prognostic gene set, and may include candidate genes of potential relevance for clinical outcome in breast cancer. According to the predictions by this expression signature, 30 of the 46 patients should have had different adjuvant treatment than they did. Keywords: Expression Microarray, Lymph-node-negative Breast Cancer, Clinical Outcome, Classification 23 fresh frozen primary node negative breast cancer tumours from 10 year survivors were compared to 23 tumours from deceased patients in order to find genes where the expression differed between the two groups Microarrays were produced at the Swegene DNA Microarray Resource Center, Department of Oncology, Lund University, Sweden (http://swegene.onk.lu.se). Frozen tumours were homogenized together with TRIzol Reagent (Invitrogen, Carlsbad, CA, USA). From the cell-suspension total RNA was extracted using RNeasy mini kit (Qiagen, Valencia, CA, USA) according to the manufacturer’s protocol. The quality of the RNA was evaluated with the Agilent 2100 BioAnalyser (Agilent Technologies, Palo Alto, CA, USA). Specimens where the 28S/16S ratio was lower than 1.0 or the RIN-value was lower than 6.7 were excluded from the study. For each sample, Cy3-dCTP (Amersham Biosciences, Buckinghamshire, UK) labelled probes were synthesized from 5 µg of the total tumour RNA and Cy5-dCTP (Amersham Biosciences) labelled reference were synthesized from 5 µg of commercial reference RNA (Universal Human Reference RNA, Stratagene, La Jolla, CA, USA) by reverse transcription. The probes were purified using ChipShot™ labelling cleanup system (Promega, Madison, WI, USA). The hybridizations were carried out using Pronto! Micro Array reagent systems (Corning Inc., Corning, NY, USA). For each sample, labelled tumour cDNA and reference cDNA were co-precipitated and hybridised to the microarray slide. The microarray slides were scanned with Agilent microarray scanner G2565AA (Agilent Technologies) and image analysis was performed using the Genepix 6.0.0.45 software (Axon Instruments, Union City, CA, USA).

Project description:BACKGROUND:Guidelines from the United Kingdom and the United States on risk stratification after polypectomy differ, as do recommended surveillance intervals. OBJECTIVE:To compare risk for advanced colorectal neoplasia at 1-year colonoscopy among patients cross-classified by U.S. and U.K. surveillance guidelines. DESIGN:Pooled analysis of 4 prospective studies between 1984 and 1998. SETTING:Academic and private clinics in the United States. PATIENTS:3226 postpolypectomy patients with 6- to 18-month follow-up colonoscopy. MEASUREMENTS:Rates of advanced neoplasia (an adenoma ?1 cm, high-grade dysplasia, >25% villous architecture, or invasive cancer) at 1 year, compared across U.S. and U.K. risk categories. RESULTS:Advanced neoplasia was detected 1 year after polypectomy in 3.8% (95% CI, 2.7% to 4.9%) of lower-risk patients and 11.2% (CI, 9.8% to 12.6%) of higher-risk patients by U.S. criteria. According to U.K. criteria, 4.4% (CI, 3.3% to 5.4%) of low-risk patients, 9.9% (CI, 8.3% to 11.5%) of intermediate-risk patients, and 18.7% (CI, 14.8% to 22.5%) of high-risk patients presented with advanced neoplasia; U.K. high-risk patients comprised 12.1% of all patients. All U.S. lower-risk patients were low-risk by U.K. criteria; however, more patients were classified as low-risk, because the U.K. guidelines do not consider histologic features. Higher-risk U.S. patients were distributed across the 3 U.K. categories. Among all patients with advanced neoplasia, 26.3% were reclassified by the U.K. criteria to a higher-risk category and 7.0% to a lower-risk category, with a net 19.0% benefiting from detection 2 years earlier. Overall, substitution of U.K. for U.S. guidelines resulted in an estimated 0.03 additional colonoscopy every 5 years per patient. LIMITATIONS:Patients were enrolled 15 to 20 years ago, and quality measures for colonoscopy were unavailable. Patients lacking follow-up colonoscopy or with surveillance colonoscopy after 6 to 18 months and those with cancer or insufficient baseline adenoma characteristics were excluded (2076 of 5302). CONCLUSION:Application of the U.K. guidelines in the United States could identify a subset of high-risk patients who may warrant a 1-year clearing colonoscopy without substantially increasing rates of colonoscopy. PRIMARY FUNDING SOURCE:European Union Public Health Programme.

Project description:Expression profiling of breast cancer tumours, comparing 10 year survivors to deceased patients Background It is of great significance to find better markers to correctly distinguish between high-risk and low-risk breast cancer patients since the majority of breast cancer cases are at present being overtreated. Methods 46 tumours from node-negative breast cancer patients were studied with gene expression microarrays. A t-test was carried out in order to find a set of genes where the expression might predict clinical outcome. Two classifiers were used to evaluate the gene lists on the different data sets, a correlation-based classifier and a VFI (Voting Features Interval) classifier. We then evaluated the predictive accuracy of this expression signature on tumour sets from two similar studies on lymph-node negative patients which had developed gene expression signatures superior to current methods in classifying node-negative breast tumours. These two signatures were also tested on our material. Results A list of 51 genes whose expression profiles could predict clinical outcome with high accuracy in our material (96% or 89% accuracy in cross-validation, depending on type of classifier) was developed. When tested on two independent data sets, the expression signature based on the 51 identified genes had good predictive qualities in one of the data sets (74% accuracy), whereas their predictive value on the other data set were poor, presumably due to the fact that only 23 of the 51 genes were found in that material. We also found that previously developed expression signatures could predict clinical outcome well to moderately well in our material (72% and 61%, respectively). Conclusion The list of 51 genes derived in this study might have potential for clinical utility as a prognostic gene set, and may include candidate genes of potential relevance for clinical outcome in breast cancer. According to the predictions by this expression signature, 30 of the 46 patients should have had different adjuvant treatment than they did. Keywords: Expression Microarray, Lymph-node-negative Breast Cancer, Clinical Outcome, Classification 23 fresh frozen primary node negative breast cancer tumours from 10 year survivors were compared to 23 tumours from deceased patients in order to find genes where the expression differed between the two groups Microarrays were produced at the Swegene DNA Microarray Resource Center, Department of Oncology, Lund University, Sweden (http://swegene.onk.lu.se). Frozen tumours were homogenized together with TRIzol Reagent (Invitrogen, Carlsbad, CA, USA). From the cell-suspension total RNA was extracted using RNeasy mini kit (Qiagen, Valencia, CA, USA) according to the manufacturer’s protocol. The quality of the RNA was evaluated with the Agilent 2100 BioAnalyser (Agilent Technologies, Palo Alto, CA, USA). Specimens where the 28S/16S ratio was lower than 1.0 or the RIN-value was lower than 6.7 were excluded from the study. For each sample, Cy3-dCTP (Amersham Biosciences, Buckinghamshire, UK) labelled probes were synthesized from 5 µg of the total tumour RNA and Cy5-dCTP (Amersham Biosciences) labelled reference were synthesized from 5 µg of commercial reference RNA (Universal Human Reference RNA, Stratagene, La Jolla, CA, USA) by reverse transcription. The probes were purified using ChipShot™ labelling cleanup system (Promega, Madison, WI, USA). The hybridizations were carried out using Pronto! Micro Array reagent systems (Corning Inc., Corning, NY, USA). For each sample, labelled tumour cDNA and reference cDNA were co-precipitated and hybridised to the microarray slide. The microarray slides were scanned with Agilent microarray scanner G2565AA (Agilent Technologies) and image analysis was performed using the Genepix 6.0.0.45 software (Axon Instruments, Union City, CA, USA).

Dataset Information

One versus Two Breast Density Measures to Predict 5- and 10-Year Breast Cancer Risk.

Publications

One versus Two Breast Density Measures to Predict 5- and 10-Year Breast Cancer Risk.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure