scholarly journals Consensus molecular subtype classification of colorectal adenomas

2018 ◽  
Vol 246 (3) ◽  
pp. 266-276 ◽  
Author(s):  
Malgorzata A Komor ◽  
Linda JW Bosch ◽  
Gergana Bounova ◽  
Anne S Bolijn ◽  
Pien M Delis-van Diemen ◽  
...  
Keyword(s):  
Molecular Subtype ◽  
Colorectal Adenomas ◽  
Subtype Classification ◽  
Consensus Molecular Subtype

scholarly journals Investigation of colorectal cancer in accordance with consensus molecular subtype classification

2020 ◽  
Vol 4 (5) ◽  
pp. 528-539
Author(s):  
Hiroshi Sawayama ◽  
Yuji Miyamoto ◽  
Katsuhiro Ogawa ◽  
Naoya Yoshida ◽  
Hideo Baba
Keyword(s):  
Colorectal Cancer ◽  
Molecular Subtype ◽  
Subtype Classification ◽  
Consensus Molecular Subtype

H&E image-based consensus molecular subtype classification of colorectal cancer using weak labeling.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e16097-e16097
Author(s):  
Andrew J. Kruger ◽  
Lingdao Sha ◽  
Madhavi Kannan ◽  
Rohan P. Joshi ◽  
Benjamin D. Leibowitz ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Molecular Subtypes ◽  
Molecular Subtype ◽  
Multiple Instance Learning ◽  
Data Sets ◽  
Learning Models ◽  
Subtype Identification ◽  
Consensus Molecular Subtype

e16097 Background: Using gene-expression, consensus molecular subtypes (CMS) divide colorectal cancers (CRC) into four categories with prognostic and therapy-predictive clinical utilities. These subtypes also manifest as different morphological phenotypes in whole-slide images (WSIs). Here, we implemented and trained a novel deep multiple instance learning (MIL) framework that requires only a single label per WSI to identify morphological biomarkers and accelerate CMS classification. Methods: Deep learning models can be trained by MIL frameworks to classify tissue in localized tiles from large ( > 1 Gb) WSIs using only weakly supervised, slide-level classification labels. Here we demonstrate a novel framework that advances on instance-based MIL by using a multi-phase approach to training deep learning models. The framework allows us to train on WSIs that contain multiple CMS classes while further identifying previously undiscovered tissue features that have low or no correlation with any subtype. Identification of these uncorrelated features results in improved insights into the specific tissue features that are most associated with the four CMS classes and a more accurate classification of CMS status. Results: We trained and validated (n = 735 WSIs and 184 withheld WSIs, respectively) a ResNet34 convolutional neural network to classify 224x224 pixel tiles distributed across tumor, lymphocyte, and stroma tissue regions. The slide-level CMS classification probability was calculated by an aggregation of the tiles correlated with each one of the four subtypes. The receiver operating characteristic curves had the following one-vs-all AUCs: CMS1 = 0.854, CMS2 = 0.921, CMS3 = 0.850, and CMS4 = 0.866, resulting in an average AUC of 0.873. Initial tests to generalize to other data sets, such as TCGA, are promising and constitute one of the future directions of this work. Conclusions: The MIL framework robustly identified tissue features correlated with CMS groups, allowing for a more efficient classification of CRC samples. We also demonstrated that the morphological features indicative of different molecular subtypes can be identified from the deep neural network.

scholarly journals Efficient molecular subtype classification of high-grade serous ovarian cancer

2015 ◽  
Vol 236 (3) ◽  
pp. 272-277 ◽  
Author(s):  
Huei San Leong ◽  
Laura Galletta ◽  
Dariush Etemadmoghadam ◽  
Joshy George ◽  
Martin Köbel ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Molecular Subtype ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Subtype Classification

scholarly journals AKT-dependent NOTCH3 activation drives tumor progression in a model of mesenchymal colorectal cancer

2020 ◽  
Vol 217 (10) ◽  
Author(s):  
Julia Varga ◽  
Adele Nicolas ◽  
Valentina Petrocelli ◽  
Marina Pesic ◽  
Abdelrahman Mahmoud ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Molecular Subtype ◽  
Distant Metastases ◽  
Intestinal Epithelial ◽  
Invasion And Metastasis ◽  
Akt Activation ◽  
Therapeutic Concepts ◽  
Cancer Subtype ◽  
Consensus Molecular Subtype

Recently, a transcriptome-based consensus molecular subtype (CMS) classification of colorectal cancer (CRC) has been established, which may ultimately help to individualize CRC therapy. However, the lack of animal models that faithfully recapitulate the different molecular subtypes impedes adequate preclinical testing of stratified therapeutic concepts. Here, we demonstrate that constitutive AKT activation in intestinal epithelial cells markedly enhances tumor invasion and metastasis in Trp53ΔIEC mice (Trp53ΔIECAktE17K) upon challenge with the carcinogen azoxymethane. Gene-expression profiling indicates that Trp53ΔIECAktE17K tumors resemble the human mesenchymal colorectal cancer subtype (CMS4), which is characterized by the poorest survival rate among the four CMSs. Trp53ΔIECAktE17K tumor cells are characterized by Notch3 up-regulation, and treatment of Trp53ΔIECAktE17K mice with a NOTCH3-inhibiting antibody reduces invasion and metastasis. In CRC patients, NOTCH3 expression correlates positively with tumor grading and the presence of lymph node as well as distant metastases and is specifically up-regulated in CMS4 tumors. Therefore, we suggest NOTCH3 as a putative target for advanced CMS4 CRC patients.

scholarly journals Molecular subtype classification of urothelial carcinoma in Lynch syndrome

2018 ◽  
Vol 17 (2) ◽  
pp. e1521
Author(s):  
P. Eriksson ◽  
C. Therkildsen ◽  
M. Höglund ◽  
M. Jönsson ◽  
G. Sjödahl ◽  
...  
Keyword(s):  
Lynch Syndrome ◽  
Urothelial Carcinoma ◽  
Molecular Subtype ◽  
Subtype Classification

scholarly journals Molecular subtype classification of urothelial carcinoma in Lynch syndrome

2018 ◽  
Vol 12 (8) ◽  
pp. 1286-1295 ◽  
Author(s):  
Christina Therkildsen ◽  
Pontus Eriksson ◽  
Mattias Höglund ◽  
Mats Jönsson ◽  
Gottfrid Sjödahl ◽  
...  
Keyword(s):  
Lynch Syndrome ◽  
Urothelial Carcinoma ◽  
Molecular Subtype ◽  
Subtype Classification
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