Recent translational research: microarray expression profiling of breast cancer--beyond classification and prognostic markers? - PubMed (original) (raw)

Recent translational research: microarray expression profiling of breast cancer--beyond classification and prognostic markers?

Cindy A Wilson et al. Breast Cancer Res. 2004.

Abstract

Genomic expression profiling has greatly improved our ability to subclassify human breast cancers according to shared molecular characteristics and clinical behavior. The logical next question is whether this technology will be similarly useful for identifying the dominant signaling pathways that drive tumor initiation and progression within each breast cancer subtype. A major challenge will be to integrate data generated from the experimental manipulation of model systems with expression profiles obtained from primary tumors. We highlight some recent progress and discuss several obstacles in the use of expression profiling to identify pathway signatures in human breast cancer.

PubMed Disclaimer

Figures

Figure 1

Figure 1

Cell-type origin model for the classification of human breast cancers. Illustration of the relationship between cell type and of the two main branches of the tumor subclassification schema. ER, estrogen receptor.

Figure 2

Figure 2

ERRB2 and GRB7 co-expression in microarray profiling data from primary breast cancers. The log ratios or log intensity values were downloaded for the ERBB2 and GRB7 probes from each of four publicly available microarray profiling data sets of primary breast cancers. (a) Log10 ratios generated using 60-mer oligonucleotide arrays for the 98 node-negative tumors (78 sporadic tumors and 20 BRCA1/BRCA2 mutant tumors) versus a pooled reference of all 78 sporadic breast cancer RNA from the van't Veer and colleagues data set [11]. (b) Log2 ratios generated using cDNA microarrays for 115 breast cancers and seven nonmalignant breast samples versus a universal reference RNA (mixed human cell lines) from the Sørlie and colleagues data set [4]. (c) Log2 ratios generated from cDNA microarrays for 99 unselected breast cancers versus a universal reference (mixed human cell lines) from the Sotiriou and colleagues data set [5]. (d) Log10 intensity values generated using Affymetrix oligonucleotide arrays for 49 breast cancers from the West and colleagues data set [3].

Figure 3

Figure 3

Breast cancer subgroups defined by estrogen receptor message (ESR1) expression, ERRB2 amplification and BRCA1/BRCA2 mutation status. (a) ESR1 values in the van't Veer and colleagues tumor samples [11]. The expression ratios for ESR1, ERRB2 and GRB7 along with the mutational status of BRCA1/BRCA2 were used to delineate the breast cancers into six subgroups. The ERBB2-positive tumors (purple) are grouped separately and arranged from highest to lowest ESR1 level. The samples containing BRCA1 or BRCA2 mutations (dark green) are also grouped separately and arranged from highest to lowest ESR1. The two BRCA2 samples are marked with asterisks. The remaining tumors are arranged by ESR1 level and colored as highly positive (black; ESR log ratio > 0.2), moderately positive (blue; ESR1 > 0 and ESR1 < 0.2), weakly positive (yellow; ESR1 < 0 and ESR1 > -0.5), and negative (red; ESR1 < -0.5). The dotted line represents an estimation of true estrogen receptor (ER) negativity by immunohistochemistry comparison. (b) High ERBB2 expression levels are the result of gene amplification. The samples are arranged in the same order and colored as in (a). Upper, ERRB2 intensity ratios; lower, co-amplified GRB7 intensity ratios.

Figure 4

Figure 4

Cell-type-specific cytokeratin expression in breast cancer subgroups. The log ratios for the cytokeratin probes for the van't Veer and colleagues tumor samples [11] are colored by group as in Fig. 3. (a) Co-expression of the luminal KRT8 and KRT18 is found in the estrogen receptor (ER)-positive tumors (black, blue and yellow) and in the ERRB2-positive tumors (purple). (b) Co-expression of the basal KRT5 and KRT14 is found in the ER-negative tumors (red) and in the BRCA1 mutant tumors (dark green).

Figure 5

Figure 5

Estrogen-modulated genes and their co-expression in estrogen receptor-positive primary breast cancers. The log ratios of the four experimentally confirmed, estrogen-induced genes (a) LIV-1, (b) TFF1, (c) PGR and (d) AREG were plotted against ESR1 from the van't Veer and colleagues data [11]. Data for genes shown to be repressed by estrogen treatment in MCF-7 cells [8] are also plotted against ESR1: (e) TGFβ2 and (f) NDRG1. The samples are colored by group, as in Figs 3 and 4. Only values for the five subgroups of sporadic tumors (n = 78) are plotted and used for the _R_2 calculations. The data for the BRCA1/BRCA2 subgroup (dark green) are not shown.

Similar articles

Cited by

References

    1. Perou CM, Sorlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, Pollack JR, Ross DT, Johnsen H, Akslen LA, et al. Molecular portraits of human breast tumours. Nature. 2000;406:747–752. doi: 10.1038/35021093. - DOI - PubMed
    1. Sørlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, Hastie T, Eisen MB, van de Rijn M, Jeffrey SS, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA. 2001;98:10869–10874. doi: 10.1073/pnas.191367098. - DOI - PMC - PubMed
    1. West M, Blanchette C, Dressman H, Huang E, Ishida S, Spang R, Zuzan H, Olson JA, Jr, Marks JR, Nevins JR. Predicting the clinical status of human breast cancer by using gene expression profiles. Proc Natl Acad Sci USA. 2001;98:11462–11467. doi: 10.1073/pnas.201162998. - DOI - PMC - PubMed
    1. Sørlie T, Tibshirani R, Parker J, Hastie T, Marron JS, Nobel A, Deng S, Johnsen H, Pesich R, Geisler S, et al. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci USA. 2003;100:8418–8423. doi: 10.1073/pnas.0932692100. - DOI - PMC - PubMed
    1. Sotiriou C, Neo SY, McShane LM, Korn EL, Long PM, Jazaeri A, Martiat P, Fox SB, Harris AL, Liu ET. Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proc Natl Acad Sci USA. 2003;100:10393–10398. doi: 10.1073/pnas.1732912100. - DOI - PMC - PubMed

MeSH terms

Substances

LinkOut - more resources