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. 2006;7(3):R19.
doi: 10.1186/gb-2006-7-3-r19. Epub 2006 Mar 15.

Deciphering cellular states of innate tumor drug responses

Affiliations

Deciphering cellular states of innate tumor drug responses

Esther Graudens et al. Genome Biol. 2006.

Abstract

Background: The molecular mechanisms underlying innate tumor drug resistance, a major obstacle to successful cancer therapy, remain poorly understood. In colorectal cancer (CRC), molecular studies have focused on drug-selected tumor cell lines or individual candidate genes using samples derived from patients already treated with drugs, so that very little data are available prior to drug treatment.

Results: Transcriptional profiles of clinical samples collected from CRC patients prior to their exposure to a combined chemotherapy of folinic acid, 5-fluorouracil and irinotecan were established using microarrays. Vigilant experimental design, power simulations and robust statistics were used to restrain the rates of false negative and false positive hybridizations, allowing successful discrimination between drug resistance and sensitivity states with restricted sampling. A list of 679 genes was established that intrinsically differentiates, for the first time prior to drug exposure, subsequently diagnosed chemo-sensitive and resistant patients. Independent biological validation performed through quantitative PCR confirmed the expression pattern on two additional patients. Careful annotation of interconnected functional networks provided a unique representation of the cellular states underlying drug responses.

Conclusion: Molecular interaction networks are described that provide a solid foundation on which to anchor working hypotheses about mechanisms underlying in vivo innate tumor drug responses. These broad-spectrum cellular signatures represent a starting point from which by-pass chemotherapy schemes, targeting simultaneously several of the molecular mechanisms involved, may be developed for critical therapeutic intervention in CRC patients. The demonstrated power of this research strategy makes it generally applicable to other physiological and pathological situations.

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Figures

Figure 1
Figure 1
Range of biological variability of the gene expression dataset. Similarity dendrograms (Pearson correlation) resulting from hierarchical clustering of tumor samples from CRC patients based on the global gene expression matrix. Letters refer to the origin of the biopsies, colon tumors (T), liver metastases (M) or adjacent non-tumoral colons (N). Numbers refer to individual patients. For each sample, the color patch represents the primary drug response rate, either resistant (in red) or sensitive (in blue), subsequently diagnosed in patients at the presentation of the drugs. Color squares around the gene expression matrix display specific subgroups of clusters. The yellow square shows the cluster of N samples, while the gray one refers to the clusters of cancerous samples (T&M); red and blue squares specify predictive (resistant and sensitive) subgroups of cancerous samples.
Figure 2
Figure 2
Differential gene expression in cancerous samples. (a) Box plot of fold changes in expression levels from genes in list L863. A red line represents the average; a black line, the median. The 1st quartiles, 3rd quartiles, minimum and maximum values are indicated; the ratios are shown in absolute values. (b) Venn diagram representation of the co-occurring genes with differential expressions computed through statistical comparisons of subgroups of colon tumors (T), liver metastases (M) or both cancerous tissues together (T&M) (α = 0.01). The number of clones representing genes that were found significantly differentially expressed between chemo-sensitive and resistant states is indicated. (c) Clustering analysis on cancerous samples of the expression profiles of the genes in list L863. Genes (row) and samples (columns) are clustered independently using Pearson correlation. The top color patch represents primary drug responses, either chemo-sensitive (in blue) or resistant (in red). The top-ranked relevant gene clusters selected using t statistics with permutation-based adjustment (n = 10,000; α = 0.05) are indicated by color bars.
Figure 3
Figure 3
Independent biological validation by Q-PCR. Serial representation of Q-PCR relative expression (2-ΔΔCt; log10) of a series of genes (identified by symbols and HUGO nomenclature) that were shown to be statistically differentially expressed in both microarray and Q-PCR (indicated in bold in Table 2). Results are displayed as circles for the mean 2-ΔΔCt and 95% confidence intervals (α = 0.05) computed on a series of cancerous samples, which correspond to the sample set previously used in microarray analysis (compare Additional data file 10). The color patch refers to the primary drug responses, either chemo-sensitive (in blue) or resistant (in red) of the corresponding patients. Diamonds show the relative expression levels measured on a new tumor sample (T-P61) from an additional patient (P61). The results obtained for a second new patient are presented in Additional data file 7.
Figure 4
Figure 4
Term-ranking of GO categories. Representation of the 13 top-ranked functional categories (terms), using GO terms, that are enriched in differentially expressed genes (α = 0.05). Numbers of genes per category are indicated. The computed two-sided Fisher's exact scores are shown for each relevant biological theme (black) together with scores for up-regulated (red) or down-regulated (green) genes.
Figure 5
Figure 5
Directed acyclic graph representation of ontology terms. Association tree of the GO terms found significantly associated with in vivo innate drug responses. Bracketed numbers refer to the framework of the GO hierarchy and arrows indicate direct parent-child links. Relevant terms enriched in differentially expressed genes were computed using the GoMiner system [54]. Terms related to categories enriched in down-regulated genes, up-regulated genes or both down- and up-regulated genes are colored in green, red and orange, respectively, and the corresponding adjusted p values are indicated. Squares and triangles refer, respectively, to the terms relevant in the NODE547X and NODE519X gene modules.
Figure 6
Figure 6
Systems view of a cellular state anticipating in vivo innate drug resistance. Molecular interaction networks characteristic of a resistant cellular state, prior to drug exposure, showing an integrated view of networks from resistant cells arrested either in G1 phase or in S-G2 phases. The representation recapitulates the dynamics and dependencies of gene modulations with specific components of the cell machinery identified through statistical comparisons, GO annotations and literature mining. Biochemical interactions are specified; the symbols used are derived from the standard nomenclature proposed by CellDesigner v2.5: arrow, direct activation; dotted arrow, unknown interaction; arrow with a strike through it, indirect activation; bar, inhibition; dotted bar, unknown inhibition; dashed arrow, transcriptional activation; dashed bar, transcriptional inhibition; arrow with clear arrowhead, transport. Red and green colors refer to up- and down-regulation, respectively, in resistant versus sensitive states; other genes analyzed with microarrays and/or Q-PCR are indicated in gray, and those not investigated in this study are indicated in white.

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