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

Supplementary Figure A2-1 from High Expression of Lymphocyte-Associated Genes in Node-Negative HER2+ Breast Cancers Correlates with Lower Recurrence Rates

Assessment of a possible time shift in gene expression between parasite lines. We examined the data to assess whether some genes displayed a shifted timing of expression between parasite strains. By comparing peaks of expression for each gene in each pairwise comparison, we determined the number of genes whose peak of expression was shifted of a determined number of hours. 0 corresponds to a shift of less than 1 h, one corresponds to a shift of at least 1 h but less than two hours, etc. Therefore, two genes cannot be more than 24 h apart of each other in either direction. We observed that more than 70 % of all genes peaked within the same 8-hour window in the pfmdr1 dataset (Table S5A) while about 80 % of the genes peaked within the same 5-hour window in the pfcrt dataset (Table S5B). (XLSX 44 kb)

BACKGROUND:The acquisition of multidrug resistance by Plasmodium falciparum underscores the need to understand the underlying molecular mechanisms so as to counter their impact on malaria control. For the many antimalarials whose mode of action relates to inhibition of heme detoxification inside infected erythrocytes, the digestive vacuole transporters PfCRT and PfMDR1 constitute primary resistance determinants.RESULTS:Using gene expression microarrays over the course of the parasite intra-erythrocytic developmental cycle, we compared the transcriptomic profiles between P. falciparum strains displaying mutant or wild-type pfcrt or varying in pfcrt or pfmdr1 expression levels. To account for differences in the time of sampling, we developed a computational method termed Hypergeometric Analysis of Time Series, which combines Fast Fourier Transform with a modified Gene Set Enrichment Analysis. Our analysis revealed coordinated changes in genes involved in protein catabolism, translation initiation and DNA/RNA metabolism. We also observed differential expression of genes with a role in transport or coding for components of the digestive vacuole. Interestingly, a global comparison of all profiled transcriptomes uncovered a tight correlation between the transcript levels of pfcrt and pfmdr1, extending to dozens of other genes, suggesting an intricate regulatory balance in order to maintain optimal physiological processes.CONCLUSIONS:This study provides insight into the mechanisms by which P. falciparum adjusts to the acquisition of mutations or gene amplification in key transporter loci that mediate drug resistance. Our results implicate several biological pathways that may be differentially regulated to compensate for impaired transporter function and alterations in parasite vacuole physiology.

List of genes with significant differential expression between 7G8 and 7G8 pfcrt_CTL in a stage-specific manner. Genes with significantly higher expression displayed absolute fold changes >1.5 and normalized fold changes values two standard deviations above the median normalized fold change value. Genes with significantly lower expression had absolute fold change values lower than 0.6 and a normalized fold change three standard deviations below the median value. Fold change for each gene at the designated stage is indicated. (XLSX 55 kb)

Genes (A-D) and gene sets (E-H) differentially expressed in the pfmdr1 data set. For each pairwise comparison, we listed genes with significant differential expression, i.e. with a fold change greater than 1.5 and a normalized fold change two standard deviations above the median values and genes with a fold change lower than 0.6 and a normalized fold change three standard deviations below the median value, which generally represented the quasi-totality of genes with a fold change below 0.6. Normalization was performed using the average fold change computed across a background pool of 11 transcriptome data sets as a scaling factor. This normalization allowed us to identify and filter out most of the hypervariable genes (e.g. var, rifin, stevor, phist, and hyp). Antigenically variant genes not detected by automatic filtering were manually removed. We listed the gene sets with significant differential expression (based on the mean rank of enrichment score >0.9 (significantly enriched) or <0.1 (significantly impoverished)) that are found in both FCB vs. FCB pfmdr1_3â ˛KD and FCB vs. 106/1 pairwise comparisons. (XLSX 65 kb)

A central problem in biology is to identify gene function. One approach is to infer function in large supergenomic networks of interactions and ancestral relationships among genes; however, their analysis can be computationally prohibitive. We show here that these biological networks are compressible. They can be shrunk dramatically by eliminating redundant evolutionary relationships, and this process is efficient because in these networks the number of compressible elements rises linearly rather than exponentially as in other complex networks. Compression enables global network analysis to computationally harness hundreds of interconnected genomes and to produce functional predictions. As a demonstration, we show that the essential, but functionally uncharacterized Plasmodium falciparum antigen EXP1 is a membrane glutathione S-transferase. EXP1 efficiently degrades cytotoxic hematin, is potently inhibited by artesunate, and is associated with artesunate metabolism and susceptibility in drug-pressured malaria parasites. These data implicate EXP1 in the mode of action of a frontline antimalarial drug.

Background: This study reviewed Twitter status updates mentioning "antibiotic(s)'' to determine overarching categories and explore evidence of misunderstanding or misuse of antibiotics.Methods: One thousand Twitter status updates mentioning antibiotic(s) were randomly selected for content analysis and categorization. To explore cases of potential misunderstanding or misuse, these status updates were mined for co-occurrence of the following terms: "cold + antibiotic(s),'' "extra + antibiotic(s),'' "flu + antibiotic(s),'' "leftover + antibiotic(s),'' and "share + antibiotic( s)'' and reviewed to confirm evidence of misuse or misunderstanding.Results: Of the 1000 status updates, 971 were categorized into 11 groups: general use (n = 289), advice/information (n = 157), side effects/negative reactions (n = 113), diagnosis (n = 102), resistance (n = 92), misunderstanding and/or misuse (n = 55), positive reactions (n = 48), animals (n = 46), other (n = 42), wanting/needing (n = 19), and cost (n = 8). Cases of misunderstanding or abuse were identified for the following combinations: "flu + antibiotic(s)'' (n = 345), "cold + antibiotic(s)'' (n = 302), "leftover + antibiotic( s)'' (n = 23), "share + antibiotic(s)'' (n = 10), and "extra + antibiotic(s)'' (n = 7).Conclusion: Social media sites offer means of health information sharing. Further study is warranted to explore how such networks may provide a venue to identify misuse or misunderstanding of antibiotics, promote positive behavior change, disseminate valid information, and explore how such tools can be used to gather real-time health data.

ABSTRACT Artemisinin-based combination therapies (ACTs) are highly effective for the treatment of Plasmodium falciparum malaria, yet their sustained efficacy is threatened by the potential spread of parasite resistance. Recent studies have provided evidence that artemisinins can inhibit the function of PfATP6, the P. falciparum ortholog of the ER calcium pump SERCA, when expressed in Xenopus laevis oocytes. Inhibition was significantly reduced in an L263E variant, which introduced the mammalian residue into a putative drug-binding pocket. To test the hypothesis that this single mutation could decrease P. falciparum susceptibility to artemisinins, we implemented an allelic-exchange strategy to replace the wild-type pfatp6 allele by a variant allele encoding L263E. Transfected P. falciparum clones were screened by PCR analysis for disruption of the endogenous locus and introduction of the mutant L263E allele under the transcriptional control of a calmodulin promoter. Expression of the mutant allele was demonstrated by reverse transcriptase (RT) PCR and verified by sequence analysis. Parasite clones expressing wild-type or L263E variant PfATP6 showed no significant difference in 50% inhibitory concentrations (IC 50 s) for artemisinin or its derivatives dihydroartemisinin and artesunate. Nonetheless, hierarchical clustering analysis revealed a trend toward reduced susceptibility that neared significance (artemisinin, P ≈ 0.1; dihydroartemisinin, P = 0.053 and P = 0.085; and artesunate, P = 0.082 and P = 0.162 for the D10 and 7G8 lines, respectively). Notable differences in the distribution of normalized IC 50 s provided evidence of decreased responsiveness to artemisinin and dihydroartemisinin ( P = 0.02 for the D10 and 7G8 lines), but not to artesunate in parasites expressing mutant PfATP6.

Abstract 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 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.

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]

Background: Clustering analysis of microarray data is often criticized for giving ambiguous results because of sensitivity to data perturbation or clustering techniques used. In this paper, we describe a new method based on principal component analysis and ensemble consensus clustering that avoids these

Background Clustering analysis of microarray data is often criticized for giving ambiguous results because of sensitivity to data perturbation or clustering techniques used. In this paper, we describe a new method based on principal component analysis and ensemble consensus clustering that avoids these problems. Results We illustrate the method on a public microarray dataset from 36 breast cancer patients of whom 31 were diagnosed with at least two of three pathological stages of disease (atypical ductal hyperplasia (ADH), ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC). Our method identifies an optimum set of genes and divides the samples into stable clusters which correlate with clinical classification into Luminal, Basal-like and Her2+ subtypes. Our analysis reveals a hierarchical portrait of breast cancer progression and identifies genes and pathways for each stage, grade and subtype. An intriguing observation is that the disease phenotype is distinguishable in ADH and progresses along distinct pathways for each subtype. The genetic signature for disease heterogeneity across subtypes is greater than the heterogeneity of progression from DCIS to IDC within a subtype, suggesting that the disease subtypes have distinct progression pathways. Our method identifies six disease subtype and one normal clusters. The first split separates the normal samples from the cancer samples. Next, the cancer cluster splits into low grade (pathological grades 1 and 2) and high grade (pathological grades 2 and 3) while the normal cluster is unchanged. Further, the low grade cluster splits into two subclusters and the high grade cluster into four. The final six disease clusters are mapped into one Luminal A, three Luminal B, one Basal-like and one Her2+. Conclusion We confirm that the cancer phenotype can be identified in early stage because the genes altered in this stage progressively alter further as the disease progresses through DCIS into IDC. We identify six subtypes of disease which have distinct genetic signatures and remain separated in the clustering hierarchy. Our findings suggest that the heterogeneity of disease across subtypes is higher than the heterogeneity of the disease progression within a subtype, indicating that the subtypes are in fact distinct diseases.

The high dimensionality of global transcription profiles, the expression level of 20,000 genes in a much small number of samples, presents challenges that affect the sensitivity and general applicability of analysis results. In principle, it would be better to describe the data in terms of a small number of metagenes, positive linear combinations of genes, which could reduce noise while still capturing the invariant biological features of the data. Here, we describe how to accomplish such a reduction in dimension by a metagene projection methodology, which can greatly reduce the number of features used to characterize microarray data. We show, in applications to the analysis of leukemia and lung cancer data sets, how this approach can help assess and interpret similarities and differences between independent data sets, enable cross-platform and cross-species analysis, improve clustering and class prediction, and provide a computational means to detect and remove sample contamination.