Dataset Information

Personal omics profiling reveals dynamic molecular and medical phenotypes.

ABSTRACT: Personalized medicine is expected to benefit from combining genomic information with regular monitoring of physiological states by multiple high-throughput methods. Here, we present an integrative personal omics profile (iPOP), an analysis that combines genomic, transcriptomic, proteomic, metabolomic, and autoantibody profiles from a single individual over a 14 month period. Our iPOP analysis revealed various medical risks, including type 2 diabetes. It also uncovered extensive, dynamic changes in diverse molecular components and biological pathways across healthy and diseased conditions. Extremely high-coverage genomic and transcriptomic data, which provide the basis of our iPOP, revealed extensive heteroallelic changes during healthy and diseased states and an unexpected RNA editing mechanism. This study demonstrates that longitudinal iPOP can be used to interpret healthy and diseased states by connecting genomic information with additional dynamic omics activity.

SUBMITTER: Chen R

PROVIDER: S-EPMC3341616 | biostudies-literature | 2012 Mar

REPOSITORIES: biostudies-literature

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Personal omics profiling reveals dynamic molecular and medical phenotypes.

Chen Rui R Mias George I GI Li-Pook-Than Jennifer J Jiang Lihua L Lam Hugo Y K HY Chen Rong R Miriami Elana E Karczewski Konrad J KJ Karczewski Konrad J KJ Hariharan Manoj M Dewey Frederick E FE Cheng Yong Y Clark Michael J MJ Im Hogune H Habegger Lukas L Balasubramanian Suganthi S O'Huallachain Maeve M Dudley Joel T JT Hillenmeyer Sara S Haraksingh Rajini R Sharon Donald D Euskirchen Ghia G Lacroute Phil P Bettinger Keith K Boyle Alan P AP Kasowski Maya M Grubert Fabian F Seki Scott S Garcia Marco M Whirl-Carrillo Michelle M Gallardo Mercedes M Blasco Maria A MA Greenberg Peter L PL Snyder Phyllis P Klein Teri E TE Altman Russ B RB Butte Atul J AJ Ashley Euan A EA Gerstein Mark M Nadeau Kari C KC Tang Hua H Snyder Michael M

Cell 20120301 6

Personalized medicine is expected to benefit from combining genomic information with regular monitoring of physiological states by multiple high-throughput methods. Here, we present an integrative personal omics profile (iPOP), an analysis that combines genomic, transcriptomic, proteomic, metabolomic, and autoantibody profiles from a single individual over a 14 month period. Our iPOP analysis revealed various medical risks, including type 2 diabetes. It also uncovered extensive, dynamic changes ...[more]

PMID: 22424236

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Project description:Recent technological advances and international efforts, such as The Cancer Genome Atlas (TCGA), have made available several pan-cancer datasets encompassing multiple omics layers with detailed clinical information in large collection of samples. The need has thus arisen for the development of computational methods aimed at improving cancer subtyping and biomarker identification from multi-modal data. Here we apply the Integrative Network Fusion (INF) pipeline, which combines multiple omics layers exploiting Similarity Network Fusion (SNF) within a machine learning predictive framework. INF includes a feature ranking scheme (rSNF) on SNF-integrated features, used by a classifier over juxtaposed multi-omics features (juXT). In particular, we show instances of INF implementing Random Forest (RF) and linear Support Vector Machine (LSVM) as the classifier, and two baseline RF and LSVM models are also trained on juXT. A compact RF model, called rSNFi, trained on the intersection of top-ranked biomarkers from the two approaches juXT and rSNF is finally derived. All the classifiers are run in a 10x5-fold cross-validation schema to warrant reproducibility, following the guidelines for an unbiased Data Analysis Plan by the US FDA-led initiatives MAQC/SEQC. INF is demonstrated on four classification tasks on three multi-modal TCGA oncogenomics datasets. Gene expression, protein expression and copy number variants are used to predict estrogen receptor status (BRCA-ER, N = 381) and breast invasive carcinoma subtypes (BRCA-subtypes, N = 305), while gene expression, miRNA expression and methylation data is used as predictor layers for acute myeloid leukemia and renal clear cell carcinoma survival (AML-OS, N = 157; KIRC-OS, N = 181). In test, INF achieved similar Matthews Correlation Coefficient (MCC) values and 97% to 83% smaller feature sizes (FS), compared with juXT for BRCA-ER (MCC: 0.83 vs. 0.80; FS: 56 vs. 1801) and BRCA-subtypes (0.84 vs. 0.80; 302 vs. 1801), improving KIRC-OS performance (0.38 vs. 0.31; 111 vs. 2319). INF predictions are generally more accurate in test than one-dimensional omics models, with smaller signatures too, where transcriptomics consistently play the leading role. Overall, the INF framework effectively integrates multiple data levels in oncogenomics classification tasks, improving over the performance of single layers alone and naive juxtaposition, and provides compact signature sizes.

Dataset Information

Personal omics profiling reveals dynamic molecular and medical phenotypes.

Publications

Personal omics profiling reveals dynamic molecular and medical phenotypes.

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