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The *AMARETTO framework for network biology and medicine: linking diseases, drivers, targets and drugs via graph-based fusion of multi-omics, clinical, imaging and perturbation data


NIH NCI CBIIT ITCR Cancer Data Science Pulse Blog

Informatics Technology for Cancer Research Program Drives and Fosters Community of Cancer Informatics Researchers: An *AMARETTO Tool Success Story.

https://datascience.cancer.gov/news-events/blog/informatics-technology-cancer-research-program-drives-and-fosters-community-cancer
The *AMARETTO software architecture. The *AMARETTO framework provides software tools for network biology and medicine, towards a data-driven platform for diagnostic, prognostic and therapeutic decision-making in cancer. The *AMARETTO platform offers modular and complementary solutions to multimodal and multiscale aspects of network graph-based fusion of multi-omics, clinical, imaging, and driver and drug perturbation data across studies of patients, etiologies and model systems of cancer. Specifically: (1) The AMARETTO algorithm learns networks of regulatory circuits - circuits of drivers and target genes - from functional genomics or multi-omics data and associates these circuits to clinical, molecular and imaging-derived phenotypes within each biological system (e.g., model systems or patients); (2) The Community-AMARETTO algorithm learns subnetworks of regulatory circuits that are shared or distinct across networks derived from multiple biological systems (e.g., model systems and patients, cohorts and individuals, diseases and etiologies, in vitro and in vivo systems); (3) The Perturbation-AMARETTO algorithm maps genetic and chemical perturbations in model systems onto patient-derived networks for driver and drug discovery, respectively, and prioritizes lead drivers, targets and drugs for follow-up with experimental validation; and (4) The Imaging-AMARETTO algorithm maps radiography and histopathology imaging data onto the patient-derived multi-omics networks for non-invasive radiography and histopathology imaging diagnostics.

The *AMARETTO framework in GenePattern Notebook

The *AMARETTO framework in GenePattern Notebook provides users with a complete analysis pipeline that enables running AMARETTO on one or multiple data cohorts and connecting them using Community-AMARETTO via GenePattern and GenomeSpace.

Mohsen Nabian#, Celine Everaert#, Jayendra Shinde#, Shaimaa Bakr#, Ted Liefeld#, Mikel Hernaez, Thomas Baumert, Michael Reich, Jill Mesirov*, Vincent Carey*, Olivier Gevaert*, Nathalie Pochet*
Preview: https://notebook.genepattern.org/services/sharing/notebooks/334/preview/
https://notebook.genepattern.org/user/amaretto-team/notebooks/The *AMARETTO framework in GenePattern Notebook.ipynb
(** Please note that you should login to https://notebook.genepattern.org with your own GenePattern user account, and then search for "The *AMARETTO framework in GenePattern Notebook" in the publicly available Notebooks. This Notebook runs directly on the GenePattern Amazon Cloud servers.)

The *AMARETTO framework in R Jupyter Notebook

The *AMARETTO framework in R Jupyter Notebook provides users with a complete analysis pipeline that enables running AMARETTO on one or multiple data cohorts and connecting them using Community-AMARETTO via GitHub and Bioconductor.

Mohsen Nabian#, Jayendra Shinde#, Celine Everaert#, Shaimaa Bakr#, Ted Liefeld, Thorin Tabor, Charles Blatti, Thomas Baumert, Michael Reich, Jill Mesirov, Mikel Hernaez*, Vincent Carey*, Olivier Gevaert*, Nathalie Pochet*
https://colab.research.google.com/drive/1JfnRoNgTVX_7VEGAAmjGjwP_yX2tdDxs
(** Please note that you can run "The *AMARETTO framework in R via GitHub and Bioconductor" directly on Google Colaboratory or your own servers.)

Brigham and Women's Hospital / Harvard Medical School meeting, Boston, 2019: Slides

NIH NCI CBIIT webinar, 2019: Abstract Slides

NIH NCI ITCR meeting, Salt Lake City, 2019: Abstract Poster Slides

R/BioC Meetup at Dana-Farber Cancer Institute / Harvard Medical School, Boston, 2019: Slides


The *AMARETTO framework

We present *AMARETTO as a software toolbox for network biology and medicine, towards developing a data-driven platform for diagnostic, prognostic and therapeutic decision-making in cancer. *AMARETTO links diseases, drivers, targets and drugs via network graph-based fusion of multi-omics, clinical, imaging, and driver and drug perturbation data across model systems and patient studies of cancer.

The *AMARETTO platform features a modular approach to incorporating prior biological knowledge based on multimodal and multiscale network-structured modeling:
  1. The AMARETTO algorithm learns networks of regulatory circuits - circuits of drivers and target genes - from functional genomics or multi-omics data and associates these circuits to clinical, molecular and imaging-derived phenotypes within each biological system (e.g., model systems or patients);
  2. The Community-AMARETTO algorithm learns subnetworks of regulatory circuits that are shared or distinct across networks derived from multiple biological systems (e.g., model systems and patients, cohorts and individuals, diseases and etiologies, in vitro and in vivo systems);
  3. The Perturbation-AMARETTO algorithm maps genetic and chemical perturbations in model systems onto patient-derived networks for driver and drug discovery, respectively, and prioritizes lead drivers, targets and drugs for follow-up with experimental validation;
  4. The Imaging-AMARETTO algorithm maps radiographic and histopathology imaging data onto the patient-derived multi-omics networks for non-invasive and histopathology imaging diagnostics.

Use Cases of the *AMARETTO framework

We demonstrate the utility of *AMARETTO via Jupyter Notebook workflows for several Use Cases integrating multi-omics, clinical, imaging, and driver and drug perturbation data across model systems and patient studies of cancer:
  1. A study of hepatitis C and B virus-induced hepatocellular carcinoma (LIHC) with driver and drug discovery for chemoprevention across pan-etiologies of hepatocellular carcinoma, experimentally validated in rat models;
  2. A study of glioblastoma multiforme (GBM) and low-grade glioma (LGG) with driver discovery for diagnostic and prognostic molecular subclasses associated with imaging-derived features for non-invasive imaging diagnostics;
  3. A pan-cancer study of squamous cell carcinoma (SCC) across five SCC cancer sites, in particular, lung (LUSC), head and neck (HNSC), esophageal (ESCA), cervical (CESC) and bladder (BLCA).

Resources

*AMARETTO is available as user-friendly tools via GitHub, Bioconductor and R Jupyter Notebook to enable further algorithm and software development and via GenePattern, GenomeSpace and GenePattern Notebook to reach a broad audience of biomedical researchers.

*AMARETTO in GitHub

- AMARETTO in GitHub: https://github.com/gevaertlab/AMARETTO
- Community-AMARETTO in GitHub: https://github.com/broadinstitute/CommunityAMARETTO
- Perturbation-AMARETTO in GitHub: https://github.com/broadinstitute/PerturbationAMARETTO
- Imaging-AMARETTO in GitHub: https://github.com/broadinstitute/ImagingAMARETTO

*AMARETTO in Bioconductor

- AMARETTO in Bioconductor: https://bioconductor.org/packages/release/bioc/html/AMARETTO.html
- Community-AMARETTO in Bioconductor: in preparation for submission

*AMARETTO in GenePattern

- AMARETTO in GenePattern: https://cloud.genepattern.org/gp/pages/index.jsf?lsid=urn:lsid:broad.mit.edu:cancer.software.genepattern.module.analysis:00378
- Community-AMARETTO in GenePattern: https://cloud.genepattern.org/gp/pages/index.jsf?lsid=urn:lsid:broad.mit.edu:cancer.software.genepattern.module.analysis:00381

*AMARETTO in GenomeSpace

The AMARETTO and Community-AMARETTO modules in GenePattern are also available within GenomeSpace: http://www.genomespace.org/

*AMARETTO in GenePattern Notebook and R Jupyter Notebook

The *AMARETTO framework in GenePattern Notebook and in R Jupyter Notebook provides users with a complete analysis pipeline that enables running AMARETTO on one or multiple data cohorts and connecting them using Community-AMARETTO. Each AMARETTO and Community-AMARETTO analysis generates a detailed report of genome-wide networks inferred from one cohort and/or shared/distinct across multiple cohorts. These reports include queryable tables and visualizations (heatmaps and network graphs) of shared/distinct cell circuits and their drivers, as well as their functional and phenotypic characterizations.

The GenePattern Notebook runs the *AMARETTO framework directly on the GenePattern Amazon Cloud servers.
https://notebook.genepattern.org/services/sharing/notebooks/334/preview/
https://notebook.genepattern.org/user/amaretto-team/notebooks/The *AMARETTO framework in GenePattern Notebook.ipynb

The R Jupyter Notebook runs the *AMARETTO framework via GitHub or Bioconductor on Google Colaboratory or your own servers.
https://colab.research.google.com/drive/1JfnRoNgTVX_7VEGAAmjGjwP_yX2tdDxs

*AMARETTO example reports

Studying hepatitis C & B virus-induced hepatocellular carcinoma using *AMARETTO:
- An example report that learns regulatory networks from multi-omics - genetic, epigenic and functional genomics - data of the hepatocellular carcinoma patient cohort from TCGA and integrates them with regulatory networks learned from functional genomics data of liver cancer cell lines from CCLE: Community-AMARETTO Report Liver 2 data sets and also available from NDEx: NDEx Community-AMARETTO Network Liver 2 data sets
- An example report that integrates regulatory networks derived from >6 liver data sources (multi-omics hepatocellular carcinoma patient data from TCGA, ~25 liver cell line models from CCLE, time course hepatitis C virus infection data in Huh7 models, time course hepatitis B virus infection data in HepG2 models, single-cell hepatitis C virus infection data in Huh7 models, single-cell hepatitis B virus infection data in HepG2 models, further augmented with previously published prognostic network models that were derived from hepatocellular carcinoma patient data): Community-AMARETTO Report Liver 6 data sets and also available from NDEx: NDEx Community-AMARETTO Network Liver 6 data sets
- An example of ongoing work on developing gene-level ontology network representations from *AMARETTO modules and communities: Shiny App

Multi-omics & imaging data fusion for glioblastoma multiforme and low grade gliomas using *AMARETTO:
- An example report that integrates imaging data into the multi-omics regulatory networks for glioblastoma multiforme and low grade gliomas based on multi-omics and non-invasive imaging data from TCGA/TCIA (that we will later connect with networks learned from integrating RNA-Seq refined for anatomic structures and stem cells with histopathology imaging data from IvyGAP and that we will subsequently further refine based on single-cell RNA-Seq studies): Community-AMARETTO Report Brain 2 data sets and also available from NDEx: NDEx Community-AMARETTO Network Brain 2 data sets

Questions?

For any questions with the *AMARETTO framework, please contact Nathalie Pochet (npochet@broadinstitute.org) and Olivier Gevaert (ogevaert@stanford.edu). See also gevaertlab.stanford.edu and http://med.stanford.edu/gevaertlab/software.html.

Funding

This work was supported by grants from NIH NCI ITCR R21 CA209940 (Pochet), NIH NCI ITCR U01 CA214846 Collaborative Supplement (Carey/Pochet) and NIH NIAID R03 AI131066 (Pochet).