scholarly journals A combined oncogenic pathway signature ofBRAF,KRASandPI3KCAmutation improves colorectal cancer classification and cetuximab treatment prediction

Gut ◽  
2012 ◽  
Vol 62 (4) ◽  
pp. 540-549 ◽  
Author(s):  
Sun Tian ◽  
Iris Simon ◽  
Victor Moreno ◽  
Paul Roepman ◽  
Josep Tabernero ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cancer Classification ◽  
Oncogenic Pathway ◽  
Treatment Prediction

EGFR signalling and oncogenic pathway signature in colorectal cancer

Gut ◽  
2013 ◽  
Vol 62 (11) ◽  
pp. 1670-1670 ◽  
Author(s):  
Ibrahim Halil Sahin ◽  
Ahmet Afsin Oktay
Keyword(s):  
Colorectal Cancer ◽  
Oncogenic Pathway

Predictive and prognostic markers of recurrence after resection of primary or metastatic colorectal cancer.

2012 ◽  
Vol 30 (4_suppl) ◽  
pp. 447-447
Author(s):  
Jeffrey Bryan VanDeusen ◽  
Joshua Uronis ◽  
Michael Morse ◽  
Michael L. Gatza ◽  
Michael B. Datto ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Metastatic Colorectal Cancer ◽  
Surgical Resection ◽  
Mixture Modeling ◽  
Mtor Pathway ◽  
Oncogenic Signaling ◽  
Pathway Activity ◽  
Oncogenic Pathway ◽  
Pathway Dysregulation

447 Background: Current biomarkers for colorectal cancer sub-classify tumors based on single mutations, such as KRAS; however, studies of single mutations belie the molecular complexity of colorectal cancer in which an average of 14 key genes per tumor is dysregulated. We hypothesize that colorectal cancer may be molecularly sub-classified based on an oncogenic pathway prediction model in which tumors are grouped based on patterns of oncogenic pathway dysregulation/expression. Methods: Affymetrix microarray data from 850 patients with primary colorectal cancer from publically available datasets were combined using Bayesian factor regression modeling normalization. The activity of 19 separate oncogenic pathways was predicted among tumors to generate patterns of pathway activity for each sample. Mixture modeling was applied to these samples to identify subgroups of tumors with unique patterns of pathway dysregulation. Validation of subclasses was performed on a dataset of 133 primary and metastatic colorectal cancer samples of patients undergoing curative surgical resection at our institution. Tumors were subgrouped according to our previous model and recurrence free survival was calculated. In vivo validation was performed by treating NOD/SCID mice bearing patient derived tumors with everolimus with changes in tumor size calculated between day 0 and day 21. Results: Mixture modeling resulted in 6 individual subgroups of colorectal cancer based on pathway dysregulation. Kaplan Meier curves revealed that patients in subclass 4 had the poorest prognosis while patients in subclass 6 had the best prognosis (p=0.05). Further, tumors in subclass 4 were generally enriched for high mTOR pathway activation and patient derived explants from subclass 4 with high predicted mTOR activity were found to be sensitive to the MTOR pathway inhibitor everolimus. Conclusions: Prediction of oncogenic signaling pathway activity is a powerful tool that may be used to molecularly sub-classify colorectal cancer into biologically relevant subgroups. These subgroups have prognostic and predictive implications for recurrence following surgical resection and responsiveness to targeted therapy.

A New Deep Learning Model Selection Method for Colorectal Cancer Classification

2020 ◽  
Vol 11 (3) ◽  
pp. 72-88
Author(s):  
Nassima Dif ◽  
Zakaria Elberrichi
Keyword(s):  
Colorectal Cancer ◽  
Deep Learning ◽  
Learning Strategy ◽  
Cancer Classification ◽  
Complex Morphology ◽  
Histopathological Image ◽  
Voting Strategy ◽  
Deep Learning Model ◽  
Aided Diagnosis ◽  
Whole Slide Images

Deep learning is one of the most commonly used techniques in computer-aided diagnosis systems. Their exploitation for histopathological image analysis is important because of the complex morphology of whole slide images. However, the main limitation of these methods is the restricted number of available medical images, which can lead to an overfitting problem. Many studies have suggested the use of static ensemble learning methods to address this issue. This article aims to propose a new dynamic ensemble deep learning method. First, it generates a set of models based on the transfer learning strategy from deep neural networks. Then, the relevant subset of models is selected by the particle swarm optimization algorithm and combined by voting or averaging methods. The proposed approach was tested on a histopathological dataset for colorectal cancer classification, based on seven types of CNNs. The method has achieved accurate results (94.52%) by the Resnet121 model and the voting strategy, which provides important insights into the efficiency of dynamic ensembling in deep learning.

Is the new era of colorectal cancer classification finally here?

2013 ◽  
Vol 10 (7) ◽  
pp. 391-393 ◽  
Author(s):  
Iris D. Nagtegaal ◽  
J. Han J. M. van Krieken
Keyword(s):  
Colorectal Cancer ◽  
Cancer Classification ◽  
New Era

scholarly journals Colorectal Cancer Classification and Cell Heterogeneity: A Systems Oncology Approach

2015 ◽  
Vol 16 (12) ◽  
pp. 13610-13632 ◽  
Author(s):  
Moisés Blanco-Calvo ◽  
Ángel Concha ◽  
Angélica Figueroa ◽  
Federico Garrido ◽  
Manuel Valladares-Ayerbes
Keyword(s):  
Colorectal Cancer ◽  
Cancer Classification ◽  
Cell Heterogeneity

scholarly journals Identification of a novel immune signature for optimizing prognosis and treatment prediction in colorectal cancer

Aging ◽  
2021 ◽  
Author(s):  
Yan Li ◽  
Yiyi Li ◽  
Zijin Xia ◽  
Dun Zhang ◽  
Xiaomei Chen ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Immune Signature ◽  
Treatment Prediction

scholarly journals A colorectal cancer classification system that associates cellular phenotype and responses to therapy

2013 ◽  
Vol 19 (5) ◽  
pp. 619-625 ◽  
Author(s):  
Anguraj Sadanandam ◽  
Costas A Lyssiotis ◽  
Krisztian Homicsko ◽  
Eric A Collisson ◽  
William J Gibb ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Classification System ◽  
Cancer Classification ◽  
Cellular Phenotype
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