Publicaciones científicas

Imaging-AMARETTO: An Imaging Genomics Software Tool to Interrogate Multiomics Networks for Relevance to Radiography and Histopathology Imaging Biomarkers of Clinical Outcomes

01-may-2020 | Revista: JCO Clinical Cancer Informatics

Olivier Gevaert  1   2 , Mohsen Nabian  2   3 , Shaimaa Bakr  1 , Celine Everaert  2   3 , Jayendra Shinde  1 , Artur Manukyan  2   3 , Ted Liefeld  4 , Thorin Tabor  4 , Jishu Xu  2   5 , Joachim Lupberger  6 , Brian J Haas  2 , Thomas F Baumert  6 , Mikel Hernaez  7 , Michael Reich  4 , Francisco J Quintana  2   3 , Erik J Uhlmann  3 , Anna M Krichevsky  3 , Jill P Mesirov  4 , Vincent Carey  2   8 , Nathalie Pochet  2   3

Purpose: The availability of increasing volumes of multiomics, imaging, and clinical data in complex diseases such as cancer opens opportunities for the formulation and development of computational imaging genomics methods that can link multiomics, imaging, and clinical data.

Methods: Here, we present the Imaging-AMARETTO algorithms and software tools to systematically interrogate regulatory networks derived from multiomics data within and across related patient studies for their relevance to radiography and histopathology imaging features predicting clinical outcomes.

Results: To demonstrate its utility, we applied Imaging-AMARETTO to integrate three patient studies of brain tumors, specifically, multiomics with radiography imaging data from The Cancer Genome Atlas (TCGA) glioblastoma multiforme (GBM) and low-grade glioma (LGG) cohorts and transcriptomics with histopathology imaging data from the Ivy Glioblastoma Atlas Project (IvyGAP) GBM cohort.

Our results show that Imaging-AMARETTO recapitulates known key drivers of tumor-associated microglia and macrophage mechanisms, mediated by STAT3, AHR, and CCR2, and neurodevelopmental and stemness mechanisms, mediated by OLIG2. Imaging-AMARETTO provides interpretation of their underlying molecular mechanisms in light of imaging biomarkers of clinical outcomes and uncovers novel master drivers, THBS1 and MAP2, that establish relationships across these distinct mechanisms.

Conclusion: Our network-based imaging genomics tools serve as hypothesis generators that facilitate the interrogation of known and uncovering of novel hypotheses for follow-up with experimental validation studies. We anticipate that our Imaging-AMARETTO imaging genomics tools will be useful to the community of biomedical researchers for applications to similar studies of cancer and other complex diseases with available multiomics, imaging, and clinical data.

CITA DEL ARTÍCULO  JCO Clin Cancer Inform. 2020 May;4:421-435.  doi: 10.1200/CCI.19.00125

Nuestros autores

Dr. Mikel Hernáez Arrazola
Investigador | Investigador principal Grupo de investigación en Machine Learning en Biomedicina