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Oncogenic pathway combinations predict clinical prognosis in gastric cancer.

Ooi CH, Ivanova T, Wu J, Lee M, Tan IB, Tao J, Ward L, Koo JH, Gopalakrishnan V, Zhu Y, Cheng LL, Lee J, Rha SY, Chung HC, Ganesan K, So J, Soo KC, Lim D, Chan WH, Wong WK, Bowtell D, Yeoh KG, Grabsch H, Boussioutas A, Tan P - PLoS Genet. (2009)

Bottom Line: We identified three oncogenic pathways (proliferation/stem cell, NF-kappaB, and Wnt/beta-catenin) deregulated in the majority (>70%) of gastric cancers.Patient stratification by oncogenic pathway combinations showed reproducible and significant survival differences in multiple cohorts, suggesting that pathway interactions may play an important role in influencing disease behavior.Individual GCs can be successfully taxonomized by oncogenic pathway activity into biologically and clinically relevant subgroups.

View Article: PubMed Central - PubMed

Affiliation: Duke-NUS Graduate Medical School, Singapore.

ABSTRACT
Many solid cancers are known to exhibit a high degree of heterogeneity in their deregulation of different oncogenic pathways. We sought to identify major oncogenic pathways in gastric cancer (GC) with significant relationships to patient survival. Using gene expression signatures, we devised an in silico strategy to map patterns of oncogenic pathway activation in 301 primary gastric cancers, the second highest cause of global cancer mortality. We identified three oncogenic pathways (proliferation/stem cell, NF-kappaB, and Wnt/beta-catenin) deregulated in the majority (>70%) of gastric cancers. We functionally validated these pathway predictions in a panel of gastric cancer cell lines. Patient stratification by oncogenic pathway combinations showed reproducible and significant survival differences in multiple cohorts, suggesting that pathway interactions may play an important role in influencing disease behavior. Individual GCs can be successfully taxonomized by oncogenic pathway activity into biologically and clinically relevant subgroups. Predicting pathway activity by expression signatures thus permits the study of multiple cancer-related pathways interacting simultaneously in primary cancers, at a scale not currently achievable by other platforms.

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Predicting pathway activation in cancers using gene expression signatures.(A) Schematic of the pathway prediction workflow. I) Expression profiles of a set of cancer samples are pre-processed to identify differentially expressed genes (red and green) compared against a common reference. II) A pathway signature is derived from an independent study concerning the cellular pathway of interest. III) The cancer profiles are compared to the pathway signature using connectivity metrics [37], and subsequently sorted against one another according to the strength of pathway association (pathway scoring). (B) Pathway predictions in breast cancers using a breast-derived tamoxifen sensitivity signature are corroborated by ESR1 (estrogen receptor) expression, which was used to determine estrogen receptor (ER) status (ER-positive or ER-negative). The cancer profiles are a collection of 51 breast cancer cell lines [18], and the pathway signature generated by comparing a tamoxifen-sensitive mammary xenograft (MaCa 3366) to its tamoxifen-resistant subline (MaCa 3366/TAM) [19]. (C) Pathway predictions in breast cancers using an osteosarcoma-derived estrogen response signature are corroborated by ESR1 (estrogen receptor) expression. The cancer profiles are a collection of 51 breast cancer cell lines [18], and the pathway signature generated by identifying genes upregulated by estradiol in U2OS osteosarcoma cells [20]. P-values were computed using Pearson's chi-square test, under the  hypothesis that the pathway predictor delivers comparable performance to a random predictor. The ESR1 gene is absent from both the 11-gene tamoxifen sensitivity signature and the 41-gene estrogen response signature. Only a two-gene overlap exists between both signatures.
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pgen-1000676-g001: Predicting pathway activation in cancers using gene expression signatures.(A) Schematic of the pathway prediction workflow. I) Expression profiles of a set of cancer samples are pre-processed to identify differentially expressed genes (red and green) compared against a common reference. II) A pathway signature is derived from an independent study concerning the cellular pathway of interest. III) The cancer profiles are compared to the pathway signature using connectivity metrics [37], and subsequently sorted against one another according to the strength of pathway association (pathway scoring). (B) Pathway predictions in breast cancers using a breast-derived tamoxifen sensitivity signature are corroborated by ESR1 (estrogen receptor) expression, which was used to determine estrogen receptor (ER) status (ER-positive or ER-negative). The cancer profiles are a collection of 51 breast cancer cell lines [18], and the pathway signature generated by comparing a tamoxifen-sensitive mammary xenograft (MaCa 3366) to its tamoxifen-resistant subline (MaCa 3366/TAM) [19]. (C) Pathway predictions in breast cancers using an osteosarcoma-derived estrogen response signature are corroborated by ESR1 (estrogen receptor) expression. The cancer profiles are a collection of 51 breast cancer cell lines [18], and the pathway signature generated by identifying genes upregulated by estradiol in U2OS osteosarcoma cells [20]. P-values were computed using Pearson's chi-square test, under the hypothesis that the pathway predictor delivers comparable performance to a random predictor. The ESR1 gene is absent from both the 11-gene tamoxifen sensitivity signature and the 41-gene estrogen response signature. Only a two-gene overlap exists between both signatures.

Mentions: Our strategy for predicting levels of oncogenic pathway activation in cancers involves four steps (Figure 1A). First, we defined ‘pathway signatures’ - sets of genes exhibiting altered expression after functional perturbation of a specific pathway in a well-defined in vitro or in vivo experimental system. Second, we mapped the pathway signatures onto gene expression profiles from a heterogeneous series of cancers. Third, using a nonparametric, rank-based pattern matching procedure, activation scores were assigned to individual cancers based upon the strength of association to the pathway signature. Finally, the individual cancers were sorted based upon their pathway activation scores.


Oncogenic pathway combinations predict clinical prognosis in gastric cancer.

Ooi CH, Ivanova T, Wu J, Lee M, Tan IB, Tao J, Ward L, Koo JH, Gopalakrishnan V, Zhu Y, Cheng LL, Lee J, Rha SY, Chung HC, Ganesan K, So J, Soo KC, Lim D, Chan WH, Wong WK, Bowtell D, Yeoh KG, Grabsch H, Boussioutas A, Tan P - PLoS Genet. (2009)

Predicting pathway activation in cancers using gene expression signatures.(A) Schematic of the pathway prediction workflow. I) Expression profiles of a set of cancer samples are pre-processed to identify differentially expressed genes (red and green) compared against a common reference. II) A pathway signature is derived from an independent study concerning the cellular pathway of interest. III) The cancer profiles are compared to the pathway signature using connectivity metrics [37], and subsequently sorted against one another according to the strength of pathway association (pathway scoring). (B) Pathway predictions in breast cancers using a breast-derived tamoxifen sensitivity signature are corroborated by ESR1 (estrogen receptor) expression, which was used to determine estrogen receptor (ER) status (ER-positive or ER-negative). The cancer profiles are a collection of 51 breast cancer cell lines [18], and the pathway signature generated by comparing a tamoxifen-sensitive mammary xenograft (MaCa 3366) to its tamoxifen-resistant subline (MaCa 3366/TAM) [19]. (C) Pathway predictions in breast cancers using an osteosarcoma-derived estrogen response signature are corroborated by ESR1 (estrogen receptor) expression. The cancer profiles are a collection of 51 breast cancer cell lines [18], and the pathway signature generated by identifying genes upregulated by estradiol in U2OS osteosarcoma cells [20]. P-values were computed using Pearson's chi-square test, under the  hypothesis that the pathway predictor delivers comparable performance to a random predictor. The ESR1 gene is absent from both the 11-gene tamoxifen sensitivity signature and the 41-gene estrogen response signature. Only a two-gene overlap exists between both signatures.
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Related In: Results  -  Collection

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pgen-1000676-g001: Predicting pathway activation in cancers using gene expression signatures.(A) Schematic of the pathway prediction workflow. I) Expression profiles of a set of cancer samples are pre-processed to identify differentially expressed genes (red and green) compared against a common reference. II) A pathway signature is derived from an independent study concerning the cellular pathway of interest. III) The cancer profiles are compared to the pathway signature using connectivity metrics [37], and subsequently sorted against one another according to the strength of pathway association (pathway scoring). (B) Pathway predictions in breast cancers using a breast-derived tamoxifen sensitivity signature are corroborated by ESR1 (estrogen receptor) expression, which was used to determine estrogen receptor (ER) status (ER-positive or ER-negative). The cancer profiles are a collection of 51 breast cancer cell lines [18], and the pathway signature generated by comparing a tamoxifen-sensitive mammary xenograft (MaCa 3366) to its tamoxifen-resistant subline (MaCa 3366/TAM) [19]. (C) Pathway predictions in breast cancers using an osteosarcoma-derived estrogen response signature are corroborated by ESR1 (estrogen receptor) expression. The cancer profiles are a collection of 51 breast cancer cell lines [18], and the pathway signature generated by identifying genes upregulated by estradiol in U2OS osteosarcoma cells [20]. P-values were computed using Pearson's chi-square test, under the hypothesis that the pathway predictor delivers comparable performance to a random predictor. The ESR1 gene is absent from both the 11-gene tamoxifen sensitivity signature and the 41-gene estrogen response signature. Only a two-gene overlap exists between both signatures.
Mentions: Our strategy for predicting levels of oncogenic pathway activation in cancers involves four steps (Figure 1A). First, we defined ‘pathway signatures’ - sets of genes exhibiting altered expression after functional perturbation of a specific pathway in a well-defined in vitro or in vivo experimental system. Second, we mapped the pathway signatures onto gene expression profiles from a heterogeneous series of cancers. Third, using a nonparametric, rank-based pattern matching procedure, activation scores were assigned to individual cancers based upon the strength of association to the pathway signature. Finally, the individual cancers were sorted based upon their pathway activation scores.

Bottom Line: We identified three oncogenic pathways (proliferation/stem cell, NF-kappaB, and Wnt/beta-catenin) deregulated in the majority (>70%) of gastric cancers.Patient stratification by oncogenic pathway combinations showed reproducible and significant survival differences in multiple cohorts, suggesting that pathway interactions may play an important role in influencing disease behavior.Individual GCs can be successfully taxonomized by oncogenic pathway activity into biologically and clinically relevant subgroups.

View Article: PubMed Central - PubMed

Affiliation: Duke-NUS Graduate Medical School, Singapore.

ABSTRACT
Many solid cancers are known to exhibit a high degree of heterogeneity in their deregulation of different oncogenic pathways. We sought to identify major oncogenic pathways in gastric cancer (GC) with significant relationships to patient survival. Using gene expression signatures, we devised an in silico strategy to map patterns of oncogenic pathway activation in 301 primary gastric cancers, the second highest cause of global cancer mortality. We identified three oncogenic pathways (proliferation/stem cell, NF-kappaB, and Wnt/beta-catenin) deregulated in the majority (>70%) of gastric cancers. We functionally validated these pathway predictions in a panel of gastric cancer cell lines. Patient stratification by oncogenic pathway combinations showed reproducible and significant survival differences in multiple cohorts, suggesting that pathway interactions may play an important role in influencing disease behavior. Individual GCs can be successfully taxonomized by oncogenic pathway activity into biologically and clinically relevant subgroups. Predicting pathway activity by expression signatures thus permits the study of multiple cancer-related pathways interacting simultaneously in primary cancers, at a scale not currently achievable by other platforms.

Show MeSH
Related in: MedlinePlus