PRECISION HEALTH INITIATIVE

FACT SHEET: Obama Administration Announces Key Actions to Accelerate Precision Medicine Initiative

White House Announcement: UA Health Sciences Commits Biomedical Informatics and Genome Medicine Teams to National Precision Medicine Initiative

In response to the national Precision Medicine Initiative (PMI), UAHS has committed significant resources to expand the clinical utility of its open-source, patient-centric analytic methods, such as the N-of-1-pathways software, which aids physicians in interpreting the dynamic changes of disease-associated gene expression arising from patients’ own DNA blueprints. As part of the initiative, UAHS will translate large-scale clinical and genomic data into actionable individual outcomes through two of its centers: the UA Center for Biomedical Informatics and Biostatistics (CB2) and the UA Center for Applied Genetics and Genomic Medicine (TCAG2M). Both centers bring together physicians, scientists, patients and other key stakeholders to develop strategies that advance understanding of the factors contributing to individual health and disease and personalized approaches to disease prevention, early detection and treatment.

Yves Lussier, MD, FACMI, will lead the UAHS patient-centric analytical methods and was invited to attend the Precision Medicine Initiative Summit held Feb 25th in Washington, D.C. UAHS’ involvement in the PMI was initiated and facilitated by Ikbel Achour, PhD, who serves as CB2’s director for precision health. Dr. Lussier collaborates closely with Kenneth S. Ramos, MD, PhD, PharmB, associate vice president for precision health sciences, director of the UA TCAG2M and an elected member of the National Academy of Medicine.

ICD-9/ICD-10

ICD-9-CM To ICD-10-CM Conversion Tool

Vol.3 ICD-9-CM to ICD-10-PCS Conversion Tool

About: Automatically identify errors from ICD-9-CM to CD-10-CM code transition that potentially can disrupt billing and clinical practice.

Goal: Clarify and quantify the administrative and financial impact from ICD-10-CM implementation in clinical datasets to reduce inaccuracies and reporting errors.

Impact. ICD-9 to ICD-10 transition based on:

• All reimbursed payments for primary care patients of University of Illinois
• 38,644 patients
• 1,446,581 patient encounters
• $382 million in payments
• 299 institutions included

Publications

• The discriminatory cost of ICD-10-CM transition between clinical specialties: metrics, case study, and mitigating tools. - JAMIA 2013 Editor Pick

• COPD Hospitalization Risk Increased with Distinct Patterns of Multiple Systems Comorbidities Unveiled by Network Modeling - AMIA Annu Symp Proc. 2014

• Challenges and remediation for Patient Safety Indicators in the transition to ICD-10-CM. - JAMIA, 2015

• Metrics and tools for consistent cohort discovery and financial analyses post-transition to ICD-10-CM. - JAMIA 2015

N-OF-1-PATHWAYS

N-of-1-Pathways Tool

In this era of precision medicine, accurate personal transcriptome interpretation and N-of-1 (single-subject) efficacy trials remain unmet challenges. We therefore developed a method, “N-of-1-pathways,” that translates gene expression data profiles into disease mechanism significance for a pair of samples - one patient at the time.

The emergence of precision medicine ushered in the opportunity to incorporate individual molecular data into patient care. In contrast to personal DNA sequencing profiling increasingly pursued in clinical practice, genome-wide transcriptome profiling has often provided biological information at the gene and pathway levels that are common and applicable only to a larger cohort. The N-of-1-pathways method, a global framework, relies on three principles: i) the statistical universe is a single patient/sample; ii) significance is derived from genesets/biomodules; and iii) similarity metric of inter-mechanisms’ relationships. N-of-1-pathways provides a unique and novel framework for N-of-1-studies (e.g. patients, cell-lines, tissues, etc.) aimed at predicting individual response to therapy and biomarker discovery. N-of-1-pathways offers opportunities to include patient-centered “omics” reports into electronic medical records for individualized clinical interpretation and precise treatment.

Publications

• 'N-of-1-pathways' unveils personal deregulated mechanisms from a single pair of RNA-Seq samples: towards precision medicine. Gardeux V*, Achour I*, Li J, Maienschein-Cline M, Li H, Pesce L, Parinandi G, Bahroos N, Winn R, Foster I, Garcia JG, Lussier YA. - J Am Med Inform Assoc. 2014

  JAMIA Editor Pick 2014 - AWARD Best TBC Research Paper 2013; Presentation at Translational Bioinformatics Conference (TBC) - TBI AMIA 2013 Joint Summits on Translational Science

• Concordance of deregulated mechanisms unveiled in underpowered experiments: PTBP1 knockdown case study. Gardeux V*, Arslan AD*, Achour I*, Ho TT*, Beck WT, Lussier YA. - BMC Med Genomics. 2014.

  Presentation at Translational Bioinformatics Conference (TBC)

• Dynamic changes of RNA-sequencing expression for precision medicine: N-of-1-pathways Mahalanobis distance within pathways of single subjects predicts breast cancer survival. Schissler AG*, Gardeux V*, Li Q*, Achour I*, Li H, Piegorsch WW, Lussier YA. - Bioinformatics. 2015

  Presentation at Intelligent Systems for Molecular Biology, ISMB 2015

• Towards a PBMC "virogram assay" for precision medicine: Concordance between ex vivo and in vivo viral infection transcriptomes. Gardeux V, Bosco A, Li J, Halonen MJ, Jackson D, Martinez FD, Lussier YA. - J Biomed Inform. 2015

* These authors contributed equally to the work

Integrative genomics analyses unveil downstream biological effectors of disease-specific polymorphisms buried in intergenic regions

Haiquan Li*, Ikbel Achour*, Lisa Bastarache*, Joanne Berghout, Vincent Gardeux, Jianrong Li,Younghee Lee, Lorenzo Pesce7, Xinan Yang, Kenneth S Ramos, Ian Foster, Joshua C Denny, Jason H Moore and Yves A Lussier.

* These authors contributed equally to this work

Functionally altered biological mechanisms arising from disease-associated polymorphisms, remain difficult to characterise when those variants are intergenic, or, fall between genes. We sought to identify shared downstream mechanisms by which inter- and intragenic single-nucleotide polymorphisms (SNPs) contribute to a specific physiopathology. Using computational modelling of 2 million pairs of disease-associated SNPs drawn from genome-wide association studies (GWAS), integrated with expression Quantitative Trait Loci (eQTL) and Gene Ontology functional annotations, we predicted 3,870 inter–intra and inter–intra SNP pairs with convergent biological mechanisms (FDR 0.05). These prioritised SNP pairs with overlapping messenger RNA targets or similar functional annotations were more likely to be associated with the same disease than unrelated pathologies (OR412). We additionally confirmed synergistic and antagonistic genetic interactions for a subset of prioritised SNP pairs in independent studies of Alzheimer’s disease (entropy P = 0.046), bladder cancer (entropy P = 0.039), and rheumatoid arthritis (PheWAS case–control Po10− 4). Using ENCODE data sets, we further statistically validated that the biological mechanisms shared within prioritised SNP pairs are frequently governed by matching transcription factor binding sites and long-range chromatin interactions. These results provide a ‘roadmap’ of disease mechanisms emerging from GWAS and further identify candidate therapeutic targets among downstream effectors of intergenic SNPs.

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