-
Something wrong with this record ?
Joint analysis of histopathology image features and gene expression in breast cancer
V. Popovici, E. Budinská, L. Čápková, D. Schwarz, L. Dušek, J. Feit, R. Jaggi,
Language English Country England, Great Britain
Document type Journal Article
Grant support
NT14134
MZ0
CEP Register
Digital library NLK
Full text - Article
Source
NLK
BioMedCentral
from 2000-01-12
BioMedCentral Open Access
from 2000
Directory of Open Access Journals
from 2000
Free Medical Journals
from 2000
PubMed Central
from 2000
Europe PubMed Central
from 2000
ProQuest Central
from 2009-01-01
Open Access Digital Library
from 2000-01-01
Open Access Digital Library
from 2000-01-01
Open Access Digital Library
from 2000-07-01
Medline Complete (EBSCOhost)
from 2000-01-01
Health & Medicine (ProQuest)
from 2009-01-01
ROAD: Directory of Open Access Scholarly Resources
from 2000
Springer Nature OA/Free Journals
from 2000-12-01
- MeSH
- Genomics methods MeSH
- Kaplan-Meier Estimate MeSH
- Humans MeSH
- Breast Neoplasms genetics pathology MeSH
- Image Processing, Computer-Assisted * MeSH
- Gene Expression Regulation, Neoplastic * MeSH
- Cluster Analysis MeSH
- Gene Expression Profiling MeSH
- Check Tag
- Humans MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
BACKGROUND: Genomics and proteomics are nowadays the dominant techniques for novel biomarker discovery. However, histopathology images contain a wealth of information related to the tumor histology, morphology and tumor-host interactions that is not accessible through these techniques. Thus, integrating the histopathology images in the biomarker discovery workflow could potentially lead to the identification of new image-based biomarkers and the refinement or even replacement of the existing genomic and proteomic signatures. However, extracting meaningful and robust image features to be mined jointly with genomic (and clinical, etc.) data represents a real challenge due to the complexity of the images. RESULTS: We developed a framework for integrating the histopathology images in the biomarker discovery workflow based on the bag-of-features approach - a method that has the advantage of being assumption-free and data-driven. The images were reduced to a set of salient patterns and additional measurements of their spatial distribution, with the resulting features being directly used in a standard biomarker discovery application. We demonstrated this framework in a search for prognostic biomarkers in breast cancer which resulted in the identification of several prognostic image features and a promising multimodal (imaging and genomic) prognostic signature. The source code for the image analysis procedures is freely available. CONCLUSIONS: The framework proposed allows for a joint analysis of images and gene expression data. Its application to a set of breast cancer cases resulted in image-based and combined (image and genomic) prognostic scores for relapse-free survival.
References provided by Crossref.org
- 000
- 00000naa a2200000 a 4500
- 001
- bmc17031745
- 003
- CZ-PrNML
- 005
- 20191024090627.0
- 007
- ta
- 008
- 171025s2016 enk f 000 0|eng||
- 009
- AR
- 024 7_
- $a 10.1186/s12859-016-1072-z $2 doi
- 035 __
- $a (PubMed)27170365
- 040 __
- $a ABA008 $b cze $d ABA008 $e AACR2
- 041 0_
- $a eng
- 044 __
- $a enk
- 100 1_
- $a Popovici, Vlad $u Institute of Biostatistics and Analyses, Faculty of Medicine, Masarykova Univerzita, Kamenice 5, Brno, 62500, Czech Republic. popovici@iba.muni.cz. $7 xx0213329
- 245 10
- $a Joint analysis of histopathology image features and gene expression in breast cancer / $c V. Popovici, E. Budinská, L. Čápková, D. Schwarz, L. Dušek, J. Feit, R. Jaggi,
- 520 9_
- $a BACKGROUND: Genomics and proteomics are nowadays the dominant techniques for novel biomarker discovery. However, histopathology images contain a wealth of information related to the tumor histology, morphology and tumor-host interactions that is not accessible through these techniques. Thus, integrating the histopathology images in the biomarker discovery workflow could potentially lead to the identification of new image-based biomarkers and the refinement or even replacement of the existing genomic and proteomic signatures. However, extracting meaningful and robust image features to be mined jointly with genomic (and clinical, etc.) data represents a real challenge due to the complexity of the images. RESULTS: We developed a framework for integrating the histopathology images in the biomarker discovery workflow based on the bag-of-features approach - a method that has the advantage of being assumption-free and data-driven. The images were reduced to a set of salient patterns and additional measurements of their spatial distribution, with the resulting features being directly used in a standard biomarker discovery application. We demonstrated this framework in a search for prognostic biomarkers in breast cancer which resulted in the identification of several prognostic image features and a promising multimodal (imaging and genomic) prognostic signature. The source code for the image analysis procedures is freely available. CONCLUSIONS: The framework proposed allows for a joint analysis of images and gene expression data. Its application to a set of breast cancer cases resulted in image-based and combined (image and genomic) prognostic scores for relapse-free survival.
- 650 _2
- $a nádory prsu $x genetika $x patologie $7 D001943
- 650 _2
- $a shluková analýza $7 D016000
- 650 _2
- $a ženské pohlaví $7 D005260
- 650 _2
- $a stanovení celkové genové exprese $7 D020869
- 650 12
- $a regulace genové exprese u nádorů $7 D015972
- 650 _2
- $a genomika $x metody $7 D023281
- 650 _2
- $a lidé $7 D006801
- 650 12
- $a počítačové zpracování obrazu $7 D007091
- 650 _2
- $a Kaplanův-Meierův odhad $7 D053208
- 655 _2
- $a časopisecké články $7 D016428
- 700 1_
- $a Budinská, Eva $u Institute of Biostatistics and Analyses, Faculty of Medicine, Masarykova Univerzita, Kamenice 5, Brno, 62500, Czech Republic. RECETOX, Masarykova Univerzita, Kamenice 5, Brno, 62500, Czech Republic. $7 xx0142844
- 700 1_
- $a Čápková, Lenka $u Institute of Biostatistics and Analyses, Faculty of Medicine, Masarykova Univerzita, Kamenice 5, Brno, 62500, Czech Republic.
- 700 1_
- $a Schwarz, Daniel, $u Institute of Biostatistics and Analyses, Faculty of Medicine, Masarykova Univerzita, Kamenice 5, Brno, 62500, Czech Republic. $d 1977- $7 ola2002146812
- 700 1_
- $a Dušek, Ladislav, $u Institute of Biostatistics and Analyses, Faculty of Medicine, Masarykova Univerzita, Kamenice 5, Brno, 62500, Czech Republic. $d 1967- $7 mzk2003181727
- 700 1_
- $a Feit, Josef $u Institute of Biostatistics and Analyses, Faculty of Medicine, Masarykova Univerzita, Kamenice 5, Brno, 62500, Czech Republic.
- 700 1_
- $a Jaggi, Rolf $u Department of Clinical Research, Faculty of Medicine, University of Bern, Bern, Switzerland.
- 773 0_
- $w MED00008167 $t BMC bioinformatics $x 1471-2105 $g Roč. 17, č. 1 (2016), s. 209
- 856 41
- $u https://pubmed.ncbi.nlm.nih.gov/27170365 $y Pubmed
- 910 __
- $a ABA008 $b sig $c sign $y a $z 0
- 990 __
- $a 20171025 $b ABA008
- 991 __
- $a 20191024091102 $b ABA008
- 999 __
- $a ok $b bmc $g 1255338 $s 992772
- BAS __
- $a 3
- BAS __
- $a PreBMC
- BMC __
- $a 2016 $b 17 $c 1 $d 209 $e 20160511 $i 1471-2105 $m BMC bioinformatics $n BMC Bioinformatics $x MED00008167
- GRA __
- $a NT14134 $p MZ0
- LZP __
- $a Pubmed-20171025