Imperial College London
Portrait Picture

DrPaoloInglese

Faculty of Medicine, Institute of Clinical Sciences

Research Associate in Data Science and Machine Learning
 
 
 
//

Contact

 

p.inglese14 Website

 
 
//

Location

 

Robert Steiner MR unitHammersmith Campus

//

Summary

 

Publications

Citation

BibTex format

@unpublished{Inglese:2017:10.1101/230052,
author = {Inglese, P and Strittmatter, N and Doria, L and Mroz, A and Speller, A and Poynter, L and Dannhorn, A and Kudo, H and Mirnezami, R and Goldin, RD and Nicholson, JK and Takats, Z and Glen, RC},
doi = {10.1101/230052},
title = {Network analysis of mass spectrometry imaging data from colorectal cancer identifies key metabolites common to metastatic development},
url = {http://dx.doi.org/10.1101/230052},
year = {2017}
}
Download

RIS format (EndNote, RefMan)

TY  - UNPB
AB  - <jats:title>Abstract</jats:title><jats:p>A deeper understanding of inter-tumor and intra-tumor heterogeneity is a critical factor for the advancement of next generation strategies against cancer. The heterogeneous morphology exhibited by solid tumors is mirrored by their metabolic heterogeneity. Defining the basic biological mechanisms that underlie tumor cell variability will be fundamental to the development of personalized cancer treatments. Variability in the molecular signatures found in local regions of cancer tissues can be captured through an untargeted analysis of their metabolic constituents. Here we demonstrate that DESI mass spectrometry imaging (MSI) combined with network analysis can provide detailed insight into the metabolic heterogeneity of colorectal cancer (CRC). We show that network modules capture signatures which differentiate tumor metabolism in the core and in the surrounding region. Moreover, module preservation analysis of network modules between patients with and without metastatic recurrence explains the inter-subject metabolic differences associated with diverse clinical outcomes such as metastatic recurrence.</jats:p><jats:sec><jats:title>Significance</jats:title><jats:p>Network analysis of DESI-MSI data from CRC human tissue reveals clinically relevant co-expression ion patterns associated with metastatic susceptibility. This delineates a more complex picture of tumor heterogeneity than conventional hard segmentation algorithms. Using tissue sections from central regions and at a distance from the tumor center, ion co-expression patterns reveal common features among patients who developed metastases (up of > 5 years) not preserved in patients who did not develop metastases. This offers insight into the nature of the complex molecular interactions associated with cancer recurrence. Presently, predicting CRC relapse is challenging, and histopathologically like-for-like cancers freque
AU  - Inglese,P
AU  - Strittmatter,N
AU  - Doria,L
AU  - Mroz,A
AU  - Speller,A
AU  - Poynter,L
AU  - Dannhorn,A
AU  - Kudo,H
AU  - Mirnezami,R
AU  - Goldin,RD
AU  - Nicholson,JK
AU  - Takats,Z
AU  - Glen,RC
DO  - 10.1101/230052
PY  - 2017///
TI  - Network analysis of mass spectrometry imaging data from colorectal cancer identifies key metabolites common to metastatic development
UR  - http://dx.doi.org/10.1101/230052
ER  -
Download