Abstract 2708: A custom gene expression panel for consensus molecular subtype classification of archival primary and metastatic colorectal cancers

2017 ◽  
Author(s):  
Ling-Yuh Huw ◽  
Robert Piskol ◽  
Felipe de Sosa e Melo ◽  
Doris Kim ◽  
Xueping Qu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Molecular Subtype ◽  
Colorectal Cancers ◽  
Subtype Classification ◽  
Consensus Molecular Subtype

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 Genomic and transcriptomic characterization of heterogeneous immune subgroups of microsatellite instability-high colorectal cancers

2021 ◽  
Vol 9 (12) ◽  
pp. e003414
Author(s):  
Jung Ho Kim ◽  
Mi-Kyoung Seo ◽  
Ji Ae Lee ◽  
Seung-Yeon Yoo ◽  
Hyeon Jeong Oh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Microsatellite Instability ◽  
Molecular Subtype ◽  
Colorectal Cancers ◽  
Molecular Characteristics ◽  
Immune Parameters ◽  
Whole Exome ◽  
Tumor Mutational Burden ◽  
Consensus Molecular Subtype

BackgroundColorectal cancers (CRCs) with microsatellite instability-high (MSI-H) are hypermutated tumors and are generally regarded as immunogenic. However, their heterogeneous immune responses and underlying molecular characteristics remain largely unexplained.MethodsWe conducted a retrospective analysis of 73 primary MSI-H CRC tissues to characterize heterogeneous immune subgroups. Based on combined tumor-infiltrating lymphocyte (TIL) immunoscore and tertiary lymphoid structure (TLS) activity, MSI-H CRCs were classified into immune-high, immune-intermediate, and immune-low subgroups. Of these, the immune-high and immune-low subgroups were further analyzed using whole-exome and transcriptome sequencing.ResultsWe found considerable variations in immune parameters between MSI-H CRCs, and immune subgrouping of MSI-H CRCs was performed accordingly. The TIL densities and TLS activities of immune-low MSI-H CRCs were comparable to those of an immune-low or immune-intermediate subgroup of microsatellite-stable CRCs. There were remarkable differences between immune-high and immune-low MSI-H CRCs, including their pathological features (medullary vs mucinous), genomic alterations (tyrosine kinase fusions vs KRAS mutations), and activated signaling pathways (immune-related vs Wnt and Notch signaling), whereas no significant differences were found in tumor mutational burden (TMB) and neoantigen load. The immune-low MSI-H CRCs were subdivided by the consensus molecular subtype (CMS1 vs CMS3) with different gene expression signatures (mesenchymal/stem-like vs epithelial/goblet-like), suggesting distinct immune evasion mechanisms. Angiogenesis and CD200 were identified as potential therapeutic targets in immune-low CMS1 and CMS3 MSI-H CRCs, respectively.ConclusionsMSI-H CRCs are immunologically heterogeneous, regardless of TMB. The unusual immune-low MSI-H CRCs are characterized by mucinous histology, KRAS mutations, and Wnt/Notch activation, and can be further divided into distinct gene expression subtypes, including CMS4-like CMS1 and CMS3. Our data provide novel insights into precise immunotherapeutic strategies for subtypes of MSI-H tumors.

scholarly journals Reliable subtype classification of diffuse large B-cell lymphoma samples from GELA LNH2003 trials using the Lymph2Cx gene expression assay

Haematologica ◽  
2017 ◽  
Vol 102 (10) ◽  
pp. e404-e406 ◽  
Author(s):  
Jean-Philippe Jais ◽  
Thierry Jo Molina ◽  
Philippe Ruminy ◽  
David Gentien ◽  
Cecile Reyes ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cell ◽  
Cell Lymphoma ◽  
B Cell Lymphoma ◽  
Gene Expression Assay ◽  
Subtype Classification ◽  
Large B Cell Lymphoma ◽  
Large B Cell

scholarly journals A Cascade Flexible Neural Forest Model for Cancer Subtypes Classification on Gene Expression Data

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Lianxin Zhong ◽  
Qingfang Meng ◽  
Yuehui Chen
Keyword(s):  
Gene Expression ◽  
Sample Size ◽  
Gene Expression Data ◽  
Small Sample Size ◽  
Small Sample ◽  
Expression Data ◽  
Cancer Subtypes ◽  
Subtype Classification ◽  
Cancer Subtype

The correct classification of cancer subtypes is of great significance for the in-depth study of cancer pathogenesis and the realization of accurate treatment for cancer patients. In recent years, the classification of cancer subtypes using deep neural networks and gene expression data has become a hot topic. However, most classifiers may face the challenges of overfitting and low classification accuracy when dealing with small sample size and high-dimensional biological data. In this paper, the Cascade Flexible Neural Forest (CFNForest) Model was proposed to accomplish cancer subtype classification. CFNForest extended the traditional flexible neural tree structure to FNT Group Forest exploiting a bagging ensemble strategy and could automatically generate the model’s structure and parameters. In order to deepen the FNT Group Forest without introducing new hyperparameters, the multilayer cascade framework was exploited to design the FNT Group Forest model, which transformed features between levels and improved the performance of the model. The proposed CFNForest model also improved the operational efficiency and the robustness of the model by sample selection mechanism between layers and setting different weights for the output of each layer. To accomplish cancer subtype classification, FNT Group Forest with different feature sets was used to enrich the structural diversity of the model, which make it more suitable for processing small sample size datasets. The experiments on RNA-seq gene expression data showed that CFNForest effectively improves the accuracy of cancer subtype classification. The classification results have good robustness.

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

Identification and validation of a 24-gene expression signature for subtype classification of medulloblastoma.

2018 ◽  
Vol 36 (15_suppl) ◽  
pp. 10553-10553
Author(s):  
Qinghua Xu ◽  
Qifeng WANG ◽  
Jinying Chen ◽  
Chengshu Chen ◽  
Yifeng Sun ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Signature ◽  
Expression Signature ◽  
Subtype Classification

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.

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