Abstract
| Original language | English |
|---|---|
| Publication status | Published - 2009 |
| MoE publication type | Not Eligible |
| Event | 100th Annual Meeting of the American Association for Cancer Research - Denver, United States Duration: 18 Apr 2009 → 22 Apr 2009 |
Conference
| Conference | 100th Annual Meeting of the American Association for Cancer Research |
|---|---|
| Country | United States |
| City | Denver |
| Period | 18/04/09 → 22/04/09 |
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Analysis of mRNA expression profiles of 4332 drug targets across 68 major cancer types : A new bioinformatic method to predict dependency of tumors on drug targets. / Mpindi, John; Sara, Henri; Kilpinen, Sami; Pisto, Tommi; Bucher, Elmar; Ojala, Kalle; Iljin, Kristiina; Haapa-Paananen, Saija; Joensuu, Heikki; Nees, Matthias; Kallioniemi, Olli.
2009. Paper presented at 100th Annual Meeting of the American Association for Cancer Research, Denver, United States.Research output: Contribution to conference › Conference article › Scientific
TY - CONF
T1 - Analysis of mRNA expression profiles of 4332 drug targets across 68 major cancer types
T2 - A new bioinformatic method to predict dependency of tumors on drug targets
AU - Mpindi, John
AU - Sara, Henri
AU - Kilpinen, Sami
AU - Pisto, Tommi
AU - Bucher, Elmar
AU - Ojala, Kalle
AU - Iljin, Kristiina
AU - Haapa-Paananen, Saija
AU - Joensuu, Heikki
AU - Nees, Matthias
AU - Kallioniemi, Olli
PY - 2009
Y1 - 2009
N2 - Bringing a new drug from discovery to the clinic takes a decade, is very expensive and often fails due to efficacy or safety concerns. Many drugs are developed against specific molecular targets and mechanisms which may be shared across a wide variety of tumor types. Therefore, systematic identification of tumor types and subtypes, where these targets play a particularly important role would be highly informative. Here, we have developed a new bioinformatic strategy towards the identification of tumor dependencies on individual drug targets. We applied our annotated database of mRNA expression data from 9,783 samples across 43 normal human tissue types and 68 major cancer types as a basis for these studies (www.genesapiens.org). We modified a statistical method from economics and adapted this for gene expression meta-analyses and calculated Gene Tissue outlier Indices (GTI) for all genes. GTI scores highlight subsets of tumor types where expression of a drug target is higher than in any other normal and malignant tissue included in the database. GTI scores were analyzed for 4332 known and emerging drug targets. This resulted in the identification of 494 statistically significant associations between a particular tumor type and a drug target. Among the strongest outliers were known drug target-disease combinations, such as KIT in gastrointestinal stromal tumors and FLT3 in AML and ALL. This highlights the known potential of Gleevec in GIST and emerging potential of FLT3 inhibitor Sunitinib in leukemias. In summary, we have developed a new bioinformatic method to predict the dependency of tumors on a given drug target. This is based on a statistical method, where expression levels of mRNA are compared not just between tumors and normal tissues in a specific organ, but also across all normal and tumor tissues. The GTI scores indicated many potential new therapeutic opportunities to explore in preclinical and clinical studies.
AB - Bringing a new drug from discovery to the clinic takes a decade, is very expensive and often fails due to efficacy or safety concerns. Many drugs are developed against specific molecular targets and mechanisms which may be shared across a wide variety of tumor types. Therefore, systematic identification of tumor types and subtypes, where these targets play a particularly important role would be highly informative. Here, we have developed a new bioinformatic strategy towards the identification of tumor dependencies on individual drug targets. We applied our annotated database of mRNA expression data from 9,783 samples across 43 normal human tissue types and 68 major cancer types as a basis for these studies (www.genesapiens.org). We modified a statistical method from economics and adapted this for gene expression meta-analyses and calculated Gene Tissue outlier Indices (GTI) for all genes. GTI scores highlight subsets of tumor types where expression of a drug target is higher than in any other normal and malignant tissue included in the database. GTI scores were analyzed for 4332 known and emerging drug targets. This resulted in the identification of 494 statistically significant associations between a particular tumor type and a drug target. Among the strongest outliers were known drug target-disease combinations, such as KIT in gastrointestinal stromal tumors and FLT3 in AML and ALL. This highlights the known potential of Gleevec in GIST and emerging potential of FLT3 inhibitor Sunitinib in leukemias. In summary, we have developed a new bioinformatic method to predict the dependency of tumors on a given drug target. This is based on a statistical method, where expression levels of mRNA are compared not just between tumors and normal tissues in a specific organ, but also across all normal and tumor tissues. The GTI scores indicated many potential new therapeutic opportunities to explore in preclinical and clinical studies.
M3 - Conference article
ER -