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

Abstract

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.

MuSA: a graphical user interface for multi-OMICs data integration in radiogenomic studies

“…Already existing tools use multi-omics data for specific scopes such as using regulatory networks to predict clinical outcomes (Imaging-AMARETTO 9 ), variable selection (mixOmics 6 ), provide pre-processed data from public databases (Broad GDAC Firehose 4 ) or allows visualization and analysis for public and private dataset (Xena UCSC browser 3 ). MuSA is set in this context as a tool that is able to cover some of these scopes but especially to be data independent (not only TCGA or public data) and data type independent (not just classic omics such as genomics, transcriptomics, etc., but also radiomic data).…”

“…Moreover, the flexibility and expandability features allow the user to adapt MuSA tool for specific analysis. In fact, compared to other imaging genomics tools, such as Imaging-AMARETTO 9 , MuSA allows users to add any customized methods, to better manage multi-omics data structure, including radiomic features, through the creation of a MAE structure, to pre-process and normalize data, which are key steps to reduce bias and systematic errors in the downstream analysis.…”

“…To date in the existing literature, there are few tools combining genomic and radiomic data, since integrating single-omics datasets from different domains in a concise manner can be a complex task. Gevaert et al 9 presented Imaging-AMARETTO tool to interrogate regulatory networks derived from multi-omics data within and across related patient studies for their relevance to radiography and histopathology imaging features predicting clinical outcomes. However, no handling data structure to better manage and organize multi-omics data, including radiomics, was considered in 9 .…”

ImaGene: A web-based software platform for tumor radiogenomic evaluation and reporting

“…At present, there are several software tools available that can detect associations between imaging characteristics and gene regulatory networks in tissue samples, including Imaging-Amaretto and Imaging-Community Amaretto. However, these tools neither have a sophisticated, user-friendly, web-based platform that can allow for the straightforward manipulation of experimental parameters, nor do they provide comprehensive reports that describe statistical correlations, their performance metrics (such as correlation co-efficients, intuitive plots such as clustered heat maps, and the AI based prediction and/or classification of labelled data along with the metrices such as RMSE:STDEV ratio, R-square and area under the receiver operating curve (AUROC / AUC) that explain the performance models in predicting and/or classifying either omics or imaging data from either imaging or omics data, respectively (Gevaert et al 2020). Additionally, users require a flexibility to select from a variety of AI model types (mainly regression based used in radigenomic domain so far) for training and testing, and consequently post analyzing or comparing results using quality control metrices such as RMSE:Stdev, R-square and AUC to arrive at reliable imaging-omics associations (Gevaert et al 2020).…”

Informatics Tools for Cancer Research and Care: Bridging the Gap Between Innovation and Implementation

“…It includes tools that support cancer research at molecular, 3-6a cellular, 7 tissue, 8-10 organ, [11][12][13][14][15] individual, [16][17][18][19] and population [20][21][22][23] levels. The tools described also support a range of cancer informatics and data science functions, including data integration, 13,[24][25][26][27][28][29] data curation, 30 deep learning, 9 information retrieval, 20,31,32 natural language processing, 22 and statistical analysis. 5,6 A catalog of all available ITCR tools can be found on the ITCR website 33…”