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Supplementary Figure A6-2 from High Expression of Lymphocyte-Associated Genes in Node-Negative HER2+ Breast Cancers Correlates with Lower Recurrence Rates

Supplementary Figure A6-1 from High Expression of Lymphocyte-Associated Genes in Node-Negative HER2+ Breast Cancers Correlates with Lower Recurrence Rates

Supplementary Table 1 from High Expression of Lymphocyte-Associated Genes in Node-Negative HER2+ Breast Cancers Correlates with Lower Recurrence Rates

Gene expression analysis has identified biologically relevant subclasses of breast cancer. However, most classification schemes do not robustly cluster all HER2+ breast cancers, in part due to limitations and bias of clustering techniques used. In this article, we propose an alternative approach that first separates the HER2+ tumors using a gene amplification signal for Her2/neu amplicon genes and then applies consensus ensemble clustering separately to the HER2+ and HER2− clusters to look for further substructure. We applied this procedure to a microarray data set of 286 early-stage breast cancers treated only with surgery and radiation and identified two basal and four luminal subtypes in the HER2− tumors, as well as two novel and robust HER2+ subtypes. HER2+ subtypes had median distant metastasis-free survival of 99 months [95% confidence interval (95% CI), 83–118 months] and 33 months (95% CI, 11–54 months), respectively, and recurrence rates of 11% and 58%, respectively. The low recurrence subtype had a strong relative overexpression of lymphocyte-associated genes and was also associated with a prominent lymphocytic infiltration on histologic analysis. These data suggest that early-stage HER2+ cancers associated with lymphocytic infiltration are a biologically distinct subtype with an improved natural history. [Cancer Res 2007;67(22):10669–76]

Supplementary Table 3a from High Expression of Lymphocyte-Associated Genes in Node-Negative HER2+ Breast Cancers Correlates with Lower Recurrence Rates

Supplementary Appendix 1 from High Expression of Lymphocyte-Associated Genes in Node-Negative HER2+ Breast Cancers Correlates with Lower Recurrence Rates

Supplementary Table 2 from High Expression of Lymphocyte-Associated Genes in Node-Negative HER2+ Breast Cancers Correlates with Lower Recurrence Rates

Biomarker development for prediction of patient response to therapy is one of the goals of molecular profiling of human tissues. Due to the large number of transcripts, relatively limited number of samples, and high variability of data, identification of predictive biomarkers is a challenge for data analysis. Furthermore, many genes may be responsible for drug response differences, but often only a few are sufficient for accurate prediction. Here we present an analysis approach, the Convergent Random Forest (CRF) method, for the identification of highly predictive biomarkers. The aim is to select from genome-wide expression data a small number of non-redundant biomarkers that could be developed into a simple and robust diagnostic tool. Our method combines the Random Forest classifier and gene expression clustering to rank and select a small number of predictive genes. We evaluated the CRF approach by analyzing four different data sets. The first set contains transcript profiles of whole blood from rheumatoid arthritis patients, collected before anti-TNF treatment, and their subsequent response to the therapy. In this set, CRF identified 8 transcripts predicting response to therapy with 89% accuracy. We also applied the CRF to the analysis of three previously published expression data sets. For all sets, we have compared the CRF and recursive support vector machines (RSVM) approaches to feature selection and classification. In all cases the CRF selects much smaller number of features, five to eight genes, while achieving similar or better performance on both training and independent testing sets of data. For both methods performance estimates using cross-validation is similar to performance on independent samples. The method has been implemented in R and is available from the authors upon request: Jadwiga.Bienkowska@biogenidec.com.

We describe a new method based on principal component analysis and robust consensus ensemble clustering to identify and elucidate the subtypes of breast cancer disease. The method was applied to microarray gene expression data using micro-dissection of samples from 36 breast cancer patients with at least two of three pathological stages of disease. Controls were normal breast epithelial cells from 3 disease free patients. Our method identified an optimum set of genes and strong, stable clusters which correlated well with clinical classification into Luminal, Basal and Her2+ subtypes based on ER, PR and Her2 status. It also revealed a hierarchical portrait of disease progression through various grades and stages and identified genes and functional pathways for each stage, grade and disease subtype. We found that gene expression heterogeneity across subtypes is much greater than the heterogeneity of progression from DCIS to IDC within a subtype, suggesting that the disease subtypes are distinct disease processes. The averaging over data perturbations and clustering methods is critical in the robust identification of subtypes and gene markers for grade and progression.

We identified significantly hypermethylated genes in clear cell renal cell carcinoma.We previously identified a set of under expressed genes in renal cell carcinoma tissue through transcriptional profiling and a robust computational screen. We selected 19 of these genes for hypermethylation analysis using a rigorous search for the best candidate regions, considering CpG islands and transcription factor binding sites. The genes were analyzed for hypermethylation in the DNA of 38 matched clear cell renal cell carcinoma and normal samples using matrix assisted laser desorption ionization time-of-flight mass spectrometry. The significance of hypermethylation was assessed using 3 statistical tests. We validated the down-regulation of significantly hypermethylated genes at the RNA and protein levels in a separate set of patients using reverse transcriptase-polymerase chain reaction, immunohistochemistry and Western blots.We found 7 significantly hypermethylated regions from 6 down-regulated genes, including SFRP1, which was previously shown to be hypermethylated in renal cell carcinoma and other cancer types.To our knowledge we report for the first time that another 5 genes (SCNN1B, SYT6, DACH1, and the tumor suppressors TFAP2A and MT1G) are hypermethylated in renal cell carcinoma. Robust computational screens and the high throughput methylation assay resulted in an enriched set of novel genes that are epigenetically altered in clear cell renal cell carcinoma. Overall the detection of hypermethylation in these highly down-regulated genes suggests that assaying for their methylation using cells from urine or blood could provide the basis for a viable diagnostic test.

Gene expression analysis has identified biologically relevant subclasses of breast cancer. However, most classification schemes do not robustly cluster all HER2+ breast cancers, in part due to limitations and bias of clustering techniques used. In this article, we propose an alternative approach that first separates the HER2+ tumors using a gene amplification signal for Her2/neu amplicon genes and then applies consensus ensemble clustering separately to the HER2+ and HER2− clusters to look for further substructure. We applied this procedure to a microarray data set of 286 early-stage breast cancers treated only with surgery and radiation and identified two basal and four luminal subtypes in the HER2− tumors, as well as two novel and robust HER2+ subtypes. HER2+ subtypes had median distant metastasis-free survival of 99 months [95% confidence interval (95% CI), 83–118 months] and 33 months (95% CI, 11–54 months), respectively, and recurrence rates of 11% and 58%, respectively. The low recurrence subtype had a strong relative overexpression of lymphocyte-associated genes and was also associated with a prominent lymphocytic infiltration on histologic analysis. These data suggest that early-stage HER2+ cancers associated with lymphocytic infiltration are a biologically distinct subtype with an improved natural history. [Cancer Res 2007;67(22):10669–76]

We develop a new technique to analyse microarray data which uses a combination of principal components analysis and consensus ensemble k-clustering to find robust clusters and gene markers in the data. We apply our method to a public microarray breast cancer dataset which has expression levels of genes in normal samples as well as in three pathological stages of disease; namely, atypical ductal hyperplasia or ADH, ductal carcinoma in situ or DCIS and invasive ductal carcinoma or IDC. Our method averages over clustering techniques and data perturbation to find stable, robust clusters and gene markers. We identify the clusters and their pathways with distinct subtypes of breast cancer (Luminal, Basal and Her2+). We confirm that the cancer phenotype develops early (in early hyperplasia or ADH stage) and find from our analysis that each subtype progresses from ADH to DCIS to IDC along its own specific pathway, as if each was a distinct disease.

High-throughput gene expression profiling can identify sets of genes that are differentially expressed between different phenotypes. Discovering marker genes is particularly important in diagnosis of a cancer phenotype. However, gene sets produced to date are too large to be economically viable diagnostics. We use a hybrid decision tree-discriminant analysis to identify small sets of genes, i.e. single genes and gene pairs, which separate normal samples from different stages of tumor samples. Half the samples are selected for training to form the probability distribution of expression values of each gene. The distributions for the tumor and normal phenotypes are then used to classify the test samples. The algorithm also identifies gene pairs by combining the probability distributions to construct a decision tree which is used to determine the class of test samples. After a series of training and testing sessions, genes and gene pairs that classify all samples correctly are recorded. The method was applied to a breast cancer data; and classifier genes that distinguish normal breast from different stages of breast tumor were identified. The genes were ranked according to their minimum Euclidean distance between the expression values in tumor and normal samples. The algorithm was able to pick known cancer related genes but also find genes that were not identified as differentially expressed by t-test with a 2 fold cut-off. Overall, the method generates possible diagnostic genes and gene pairs for a specific disease phenotype to pursue further biological interpretations in cancer biology.