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Entropy measures quantify global splicing disorders in cancer.

Ritchie W, Granjeaud S, Puthier D, Gautheret D - PLoS Comput. Biol. (2008)

Bottom Line: Here, we examine instead the bulk of non-specific transcript isoforms and analyze their level of disorder using a measure of uncertainty called Shannon's entropy.Interestingly, the genes that present the highest entropy gain are enriched in splicing factors.We postulate that such splicing disorders may develop in part from splicing alteration in key splice factors, which in turn significantly impact multiple target genes.

View Article: PubMed Central - PubMed

Affiliation: Université de Méditerranée, INSERM ERM 206, Technologies Avancées pour le Génome et Clinique, Marseille, France.

ABSTRACT
Most mammalian genes are able to express several splice variants in a phenomenon known as alternative splicing. Serious alterations of alternative splicing occur in cancer tissues, leading to expression of multiple aberrant splice forms. Most studies of alternative splicing defects have focused on the identification of cancer-specific splice variants as potential therapeutic targets. Here, we examine instead the bulk of non-specific transcript isoforms and analyze their level of disorder using a measure of uncertainty called Shannon's entropy. We compare isoform expression entropy in normal and cancer tissues from the same anatomical site for different classes of transcript variations: alternative splicing, polyadenylation, and transcription initiation. Whereas alternative initiation and polyadenylation show no significant gain or loss of entropy between normal and cancer tissues, alternative splicing shows highly significant entropy gains for 13 of the 27 cancers studied. This entropy gain is characterized by a flattening in the expression profile of normal isoforms and is correlated to the level of estimated cellular proliferation in the cancer tissue. Interestingly, the genes that present the highest entropy gain are enriched in splicing factors. We provide here the first quantitative estimate of splicing disruption in cancer. The expression of normal splice variants is widely and significantly disrupted in at least half of the cancers studied. We postulate that such splicing disorders may develop in part from splicing alteration in key splice factors, which in turn significantly impact multiple target genes.

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Meta-analysis method to obtain proliferative indices of cancer samples in microarray experiments.The 188 genes of the “cell cycle” cluster in the conserved coexpression network identified by Stuart et al. [26] were extracted. Each of the 3787 cancer-related samples was classified in one of 5 separate bins of same size in function of the average expression level of these 188 genes. The high proliferation signature bin (High PS) corresponds to the 20% of samples that have the highest mean expression level of the 188 genes; the lowest proliferation signature bin (Low PS) corresponds to the 20% of samples that have the lowest mean expression level of the 188 genes.
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pcbi-1000011-g003: Meta-analysis method to obtain proliferative indices of cancer samples in microarray experiments.The 188 genes of the “cell cycle” cluster in the conserved coexpression network identified by Stuart et al. [26] were extracted. Each of the 3787 cancer-related samples was classified in one of 5 separate bins of same size in function of the average expression level of these 188 genes. The high proliferation signature bin (High PS) corresponds to the 20% of samples that have the highest mean expression level of the 188 genes; the lowest proliferation signature bin (Low PS) corresponds to the 20% of samples that have the lowest mean expression level of the 188 genes.

Mentions: Although tumors are diverse and heterogeneous, they all share the key ability to proliferate at a higher level than normal tissue and this despite the very tight control that the organism usually exerts on cell proliferation. To test potential links between disordered isoform expression and higher levels of proliferation, we classified the cancer types that deregulate the splicing mechanism (Figure 2C) in function of their proliferative potential. To evaluate proliferation, we extracted the 188 genes from the “cell cycle” module of Stuart et al. [26], a cluster of coexpressed genes shown to be enriched in elements that are overexpressed in highly proliferative cells and whose high expression is a marker of entry into the cell cycle [27]. We manually verified each of these 188 genes (Table S3) and confirmed that 92 were shown to be specifically over-expressed during one of the replicative phases of the cell cycle and another 17 bore significant proof of being over-expressed in proliferating cells. We thus used a high expression of these markers as a surrogate for a high level of proliferation. In order to obtain a “proliferation index” of cancer samples, we computed the median expression level of the 188 markers in each of 3787 published Affymetrix microarray experiments performed on cancer samples [28]. Samples were then binned into five categories from low to high proliferation, as shown in Figure 3. To relate proliferation levels to splicing entropy results, we considered only microarray samples that contained the exact same keywords as disease tissues in Figure 2C. Results are shown in Figure 4. Cell proliferation, as measured from the expression of cell cycle genes, is significantly correlated to splicing entropy gains.


Entropy measures quantify global splicing disorders in cancer.

Ritchie W, Granjeaud S, Puthier D, Gautheret D - PLoS Comput. Biol. (2008)

Meta-analysis method to obtain proliferative indices of cancer samples in microarray experiments.The 188 genes of the “cell cycle” cluster in the conserved coexpression network identified by Stuart et al. [26] were extracted. Each of the 3787 cancer-related samples was classified in one of 5 separate bins of same size in function of the average expression level of these 188 genes. The high proliferation signature bin (High PS) corresponds to the 20% of samples that have the highest mean expression level of the 188 genes; the lowest proliferation signature bin (Low PS) corresponds to the 20% of samples that have the lowest mean expression level of the 188 genes.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2268240&req=5

pcbi-1000011-g003: Meta-analysis method to obtain proliferative indices of cancer samples in microarray experiments.The 188 genes of the “cell cycle” cluster in the conserved coexpression network identified by Stuart et al. [26] were extracted. Each of the 3787 cancer-related samples was classified in one of 5 separate bins of same size in function of the average expression level of these 188 genes. The high proliferation signature bin (High PS) corresponds to the 20% of samples that have the highest mean expression level of the 188 genes; the lowest proliferation signature bin (Low PS) corresponds to the 20% of samples that have the lowest mean expression level of the 188 genes.
Mentions: Although tumors are diverse and heterogeneous, they all share the key ability to proliferate at a higher level than normal tissue and this despite the very tight control that the organism usually exerts on cell proliferation. To test potential links between disordered isoform expression and higher levels of proliferation, we classified the cancer types that deregulate the splicing mechanism (Figure 2C) in function of their proliferative potential. To evaluate proliferation, we extracted the 188 genes from the “cell cycle” module of Stuart et al. [26], a cluster of coexpressed genes shown to be enriched in elements that are overexpressed in highly proliferative cells and whose high expression is a marker of entry into the cell cycle [27]. We manually verified each of these 188 genes (Table S3) and confirmed that 92 were shown to be specifically over-expressed during one of the replicative phases of the cell cycle and another 17 bore significant proof of being over-expressed in proliferating cells. We thus used a high expression of these markers as a surrogate for a high level of proliferation. In order to obtain a “proliferation index” of cancer samples, we computed the median expression level of the 188 markers in each of 3787 published Affymetrix microarray experiments performed on cancer samples [28]. Samples were then binned into five categories from low to high proliferation, as shown in Figure 3. To relate proliferation levels to splicing entropy results, we considered only microarray samples that contained the exact same keywords as disease tissues in Figure 2C. Results are shown in Figure 4. Cell proliferation, as measured from the expression of cell cycle genes, is significantly correlated to splicing entropy gains.

Bottom Line: Here, we examine instead the bulk of non-specific transcript isoforms and analyze their level of disorder using a measure of uncertainty called Shannon's entropy.Interestingly, the genes that present the highest entropy gain are enriched in splicing factors.We postulate that such splicing disorders may develop in part from splicing alteration in key splice factors, which in turn significantly impact multiple target genes.

View Article: PubMed Central - PubMed

Affiliation: Université de Méditerranée, INSERM ERM 206, Technologies Avancées pour le Génome et Clinique, Marseille, France.

ABSTRACT
Most mammalian genes are able to express several splice variants in a phenomenon known as alternative splicing. Serious alterations of alternative splicing occur in cancer tissues, leading to expression of multiple aberrant splice forms. Most studies of alternative splicing defects have focused on the identification of cancer-specific splice variants as potential therapeutic targets. Here, we examine instead the bulk of non-specific transcript isoforms and analyze their level of disorder using a measure of uncertainty called Shannon's entropy. We compare isoform expression entropy in normal and cancer tissues from the same anatomical site for different classes of transcript variations: alternative splicing, polyadenylation, and transcription initiation. Whereas alternative initiation and polyadenylation show no significant gain or loss of entropy between normal and cancer tissues, alternative splicing shows highly significant entropy gains for 13 of the 27 cancers studied. This entropy gain is characterized by a flattening in the expression profile of normal isoforms and is correlated to the level of estimated cellular proliferation in the cancer tissue. Interestingly, the genes that present the highest entropy gain are enriched in splicing factors. We provide here the first quantitative estimate of splicing disruption in cancer. The expression of normal splice variants is widely and significantly disrupted in at least half of the cancers studied. We postulate that such splicing disorders may develop in part from splicing alteration in key splice factors, which in turn significantly impact multiple target genes.

Show MeSH
Related in: MedlinePlus