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Gene Expression Ratios Lead to Accurate and Translatable Predictors of DR5 Agonism across Multiple Tumor Lineages.

Reddy A, Growney JD, Wilson NS, Emery CM, Johnson JA, Ward R, Monaco KA, Korn J, Monahan JE, Stump MD, Mapa FA, Wilson CJ, Steiger J, Ledell J, Rickles RJ, Myer VE, Ettenberg SA, Schlegel R, Sellers WR, Huet HA, Lehár J - PLoS ONE (2015)

Bottom Line: High expression of DR5 and Casp8 were significantly associated with sensitivity, but their expression thresholds were difficult to translate due to low dynamic ranges.Importantly, the ratio classifier shows translatability across gene expression platforms (from Affymetrix microarrays to RNA-seq) and across model systems (in vitro to in vivo).Our approach of using gene expression ratios presents a robust and novel method for constructing translatable biomarkers of compound response, which can also probe the underlying biology of treatment response.

View Article: PubMed Central - PubMed

Affiliation: Novartis Institutes for Biomedical Research, Cambridge, MA, United States of America.

ABSTRACT
Death Receptor 5 (DR5) agonists demonstrate anti-tumor activity in preclinical models but have yet to demonstrate robust clinical responses. A key limitation may be the lack of patient selection strategies to identify those most likely to respond to treatment. To overcome this limitation, we screened a DR5 agonist Nanobody across >600 cell lines representing 21 tumor lineages and assessed molecular features associated with response. High expression of DR5 and Casp8 were significantly associated with sensitivity, but their expression thresholds were difficult to translate due to low dynamic ranges. To address the translational challenge of establishing thresholds of gene expression, we developed a classifier based on ratios of genes that predicted response across lineages. The ratio classifier outperformed the DR5+Casp8 classifier, as well as standard approaches for feature selection and classification using genes, instead of ratios. This classifier was independently validated using 11 primary patient-derived pancreatic xenograft models showing perfect predictions as well as a striking linearity between prediction probability and anti-tumor response. A network analysis of the genes in the ratio classifier captured important biological relationships mediating drug response, specifically identifying key positive and negative regulators of DR5 mediated apoptosis, including DR5, CASP8, BID, cFLIP, XIAP and PEA15. Importantly, the ratio classifier shows translatability across gene expression platforms (from Affymetrix microarrays to RNA-seq) and across model systems (in vitro to in vivo). Our approach of using gene expression ratios presents a robust and novel method for constructing translatable biomarkers of compound response, which can also probe the underlying biology of treatment response.

No MeSH data available.


Related in: MedlinePlus

GREP reveals informative relationships between genes.(A) Network representation of ratios that significantly differentiate response identified by GREPDR5. Genes are connected if they are involved in a ratio, sized based on the number of ratios in which they appear, and colored based on their positivity (%times they appear in the numerator of ratios; ratios were ordered so that they are positively correlated with sensitivity). Red indicates positive, while blue indicates negative. Ratios used in the classifier are shown as bold connections. (B) Importance of individual genes in GREPDR5. Importance of individual features, each assessed using the receiver operator characteristics area under curve (AUCROC) accuracy measure between the full GREPDR5 and one built with that feature excluded.
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pone.0138486.g006: GREP reveals informative relationships between genes.(A) Network representation of ratios that significantly differentiate response identified by GREPDR5. Genes are connected if they are involved in a ratio, sized based on the number of ratios in which they appear, and colored based on their positivity (%times they appear in the numerator of ratios; ratios were ordered so that they are positively correlated with sensitivity). Red indicates positive, while blue indicates negative. Ratios used in the classifier are shown as bold connections. (B) Importance of individual genes in GREPDR5. Importance of individual features, each assessed using the receiver operator characteristics area under curve (AUCROC) accuracy measure between the full GREPDR5 and one built with that feature excluded.

Mentions: In order to facilitate interpretation of relevant ratios, we created a network visualization of all significant ratios in the GREPDR5 model by representing the component genes in a ratio as connected nodes (Fig 6A). Genes in a ratio are ordered so that the ratio is positively correlated with sensitivity. Nodes in the network sized based on the number of connections and are colored based on their positivity (proportion of times the gene is in the numerator). All the nodes, expect DAP1 are all either positive or negative. The positive nodes: TNFRSF10B, CASP8, PARP4, CASP4 and BID are also the largest nodes in the network. As expected, TNFRSF10B, CASP8, form key nodes in this network and are strongly positive regulators. BID, a substrate of CASP8 that mediates mitochondrial apoptosis in type II cells [5], also forms a key node as a proximal signaling mediator. CASP4, an inflammatory caspase [19], has previously been associated with Apo2L/TRAIL sensitivity in melanoma [20] and rheumatoid arthritis synovial fibroblasts [21], but otherwise is not widely associated with DR5 signaling and thus was an unexpected finding. Likewise, the role of PARP4, a vault poly (ADP) polymerase, in regulating the Apo2L/TRAIL pathway has not been previously demonstrated.


Gene Expression Ratios Lead to Accurate and Translatable Predictors of DR5 Agonism across Multiple Tumor Lineages.

Reddy A, Growney JD, Wilson NS, Emery CM, Johnson JA, Ward R, Monaco KA, Korn J, Monahan JE, Stump MD, Mapa FA, Wilson CJ, Steiger J, Ledell J, Rickles RJ, Myer VE, Ettenberg SA, Schlegel R, Sellers WR, Huet HA, Lehár J - PLoS ONE (2015)

GREP reveals informative relationships between genes.(A) Network representation of ratios that significantly differentiate response identified by GREPDR5. Genes are connected if they are involved in a ratio, sized based on the number of ratios in which they appear, and colored based on their positivity (%times they appear in the numerator of ratios; ratios were ordered so that they are positively correlated with sensitivity). Red indicates positive, while blue indicates negative. Ratios used in the classifier are shown as bold connections. (B) Importance of individual genes in GREPDR5. Importance of individual features, each assessed using the receiver operator characteristics area under curve (AUCROC) accuracy measure between the full GREPDR5 and one built with that feature excluded.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4574744&req=5

pone.0138486.g006: GREP reveals informative relationships between genes.(A) Network representation of ratios that significantly differentiate response identified by GREPDR5. Genes are connected if they are involved in a ratio, sized based on the number of ratios in which they appear, and colored based on their positivity (%times they appear in the numerator of ratios; ratios were ordered so that they are positively correlated with sensitivity). Red indicates positive, while blue indicates negative. Ratios used in the classifier are shown as bold connections. (B) Importance of individual genes in GREPDR5. Importance of individual features, each assessed using the receiver operator characteristics area under curve (AUCROC) accuracy measure between the full GREPDR5 and one built with that feature excluded.
Mentions: In order to facilitate interpretation of relevant ratios, we created a network visualization of all significant ratios in the GREPDR5 model by representing the component genes in a ratio as connected nodes (Fig 6A). Genes in a ratio are ordered so that the ratio is positively correlated with sensitivity. Nodes in the network sized based on the number of connections and are colored based on their positivity (proportion of times the gene is in the numerator). All the nodes, expect DAP1 are all either positive or negative. The positive nodes: TNFRSF10B, CASP8, PARP4, CASP4 and BID are also the largest nodes in the network. As expected, TNFRSF10B, CASP8, form key nodes in this network and are strongly positive regulators. BID, a substrate of CASP8 that mediates mitochondrial apoptosis in type II cells [5], also forms a key node as a proximal signaling mediator. CASP4, an inflammatory caspase [19], has previously been associated with Apo2L/TRAIL sensitivity in melanoma [20] and rheumatoid arthritis synovial fibroblasts [21], but otherwise is not widely associated with DR5 signaling and thus was an unexpected finding. Likewise, the role of PARP4, a vault poly (ADP) polymerase, in regulating the Apo2L/TRAIL pathway has not been previously demonstrated.

Bottom Line: High expression of DR5 and Casp8 were significantly associated with sensitivity, but their expression thresholds were difficult to translate due to low dynamic ranges.Importantly, the ratio classifier shows translatability across gene expression platforms (from Affymetrix microarrays to RNA-seq) and across model systems (in vitro to in vivo).Our approach of using gene expression ratios presents a robust and novel method for constructing translatable biomarkers of compound response, which can also probe the underlying biology of treatment response.

View Article: PubMed Central - PubMed

Affiliation: Novartis Institutes for Biomedical Research, Cambridge, MA, United States of America.

ABSTRACT
Death Receptor 5 (DR5) agonists demonstrate anti-tumor activity in preclinical models but have yet to demonstrate robust clinical responses. A key limitation may be the lack of patient selection strategies to identify those most likely to respond to treatment. To overcome this limitation, we screened a DR5 agonist Nanobody across >600 cell lines representing 21 tumor lineages and assessed molecular features associated with response. High expression of DR5 and Casp8 were significantly associated with sensitivity, but their expression thresholds were difficult to translate due to low dynamic ranges. To address the translational challenge of establishing thresholds of gene expression, we developed a classifier based on ratios of genes that predicted response across lineages. The ratio classifier outperformed the DR5+Casp8 classifier, as well as standard approaches for feature selection and classification using genes, instead of ratios. This classifier was independently validated using 11 primary patient-derived pancreatic xenograft models showing perfect predictions as well as a striking linearity between prediction probability and anti-tumor response. A network analysis of the genes in the ratio classifier captured important biological relationships mediating drug response, specifically identifying key positive and negative regulators of DR5 mediated apoptosis, including DR5, CASP8, BID, cFLIP, XIAP and PEA15. Importantly, the ratio classifier shows translatability across gene expression platforms (from Affymetrix microarrays to RNA-seq) and across model systems (in vitro to in vivo). Our approach of using gene expression ratios presents a robust and novel method for constructing translatable biomarkers of compound response, which can also probe the underlying biology of treatment response.

No MeSH data available.


Related in: MedlinePlus