scholarly journals Bi-clustering based biological and clinical characterization of colorectal cancer in complementary to CMS classification

2018 ◽  
Author(s):  
Sha Cao ◽  
Wennan Chang ◽  
Changlin Wan ◽  
Yong Zhang ◽  
Jing Zhao ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Drug Resistance ◽  
Large Scale ◽  
Molecular Subtype ◽  
Resistance Mechanisms ◽  
Low Rank ◽  
Data Sets ◽  
Alternative Drug ◽  
Transcriptomics Data ◽  
Consensus Molecular Subtype

In light of the marked differences in the intrinsic biological underpinnings and prognostic outcomes among different subtypes, Consensus Molecular Subtype (CMS) classification provides a new taxonomy of colorectal cancer (CRC) solely based on transcriptomics data and has been accepted as a standard rule for CRC stratification. Even though CMS was built on highly cancer relevant features, it suffers from limitations in capturing the promiscuous mechanisms in a clinical setting. There are at least two facts about using transcriptomic data for prognosis prediction: the engagement of genes or pathways that execute the clinical response pathway are highly dynamic and interactive with others; and a predefined patient stratification not only largely decrease the statistical analysis power, but also excludes the fact that clusters of patients that confer similar clinical outcomes may or may not overlap with a pre-defined subgrouping. To enable a flexible and prospective stratified exploration, we here present a novel computational framework based on bi-clustering aiming to identify gene regulatory mechanisms associated with various biological, clinical and drug-resistance features, with full recognition of the transiency of transcriptional regulation and complicacies of patients subgrouping with regards to different biological and clinical settings. Our analysis on multiple large scale CRC transcriptomics data sets using a bi-clustering based formulation suggests that the detected local low rank modules can not only generate new biological understanding coherent to CMS stratification, but also identify predictive markers for prognosis that are general to CRC or CMS dependent, as well as novel alternative drug resistance mechanisms. Our key results include: (1) a comprehensive annotation of the local low rank module landscape of CRC; (2) a mechanistic relationship between different clinical subtypes and outcomes, as well as their characteristic biological underpinnings, visible through a novel consensus map; and (3) a few (novel) resistance mechanisms of Oxaliplatin, 5-Fluorouracil, and the FOLFOX therapy are revealed, some of which are validated on independent datasets.

scholarly journals Drug Resistance Mechanisms in Colorectal Cancer Dissected with Cell Type–Specific Dynamic Logic Models

2017 ◽  
Vol 77 (12) ◽  
pp. 3364-3375 ◽  
Author(s):  
Federica Eduati ◽  
Victoria Doldàn-Martelli ◽  
Bertram Klinger ◽  
Thomas Cokelaer ◽  
Anja Sieber ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Drug Resistance ◽  
Dynamic Logic ◽  
Resistance Mechanisms ◽  
Cell Type ◽  
Logic Models ◽  
Cell Type Specific

scholarly journals Human Colorectal Cancer from the Perspective of Mouse Models

Genes ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 788 ◽  
Author(s):  
Monika Stastna ◽  
Lucie Janeckova ◽  
Dusan Hrckulak ◽  
Vitezslav Kriz ◽  
Vladimir Korinek
Keyword(s):  
Colorectal Cancer ◽  
Growth Factor ◽  
Mouse Models ◽  
Intracellular Signaling ◽  
Transforming Growth Factor ◽  
Molecular Subtype ◽  
Transforming Growth Factor Β ◽  
Dna Mismatch ◽  
Individual Mouse ◽  
Consensus Molecular Subtype

Colorectal cancer (CRC) is a heterogeneous disease that includes both hereditary and sporadic types of tumors. Tumor initiation and growth is driven by mutational or epigenetic changes that alter the function or expression of multiple genes. The genes predominantly encode components of various intracellular signaling cascades. In this review, we present mouse intestinal cancer models that include alterations in the Wnt, Hippo, p53, epidermal growth factor (EGF), and transforming growth factor β (TGFβ) pathways; models of impaired DNA mismatch repair and chemically induced tumorigenesis are included. Based on their molecular biology characteristics and mutational and epigenetic status, human colorectal carcinomas were divided into four so-called consensus molecular subtype (CMS) groups. It was shown subsequently that the CMS classification system could be applied to various cell lines derived from intestinal tumors and tumor-derived organoids. Although the CMS system facilitates characterization of human CRC, individual mouse models were not assigned to some of the CMS groups. Thus, we also indicate the possible assignment of described animal models to the CMS group. This might be helpful for selection of a suitable mouse strain to study a particular type of CRC.

scholarly journals Stratified analysis reveals chemokine-like factor (CKLF) as a potential prognostic marker in the MSI-immune consensus molecular subtype CMS1 of colorectal cancer

Oncotarget ◽  
2016 ◽  
Vol 7 (24) ◽  
pp. 36632-36644 ◽  
Author(s):  
Philip D. Dunne ◽  
Paul G. O’Reilly ◽  
Helen G. Coleman ◽  
Ronan T. Gray ◽  
Daniel B. Longley ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Prognostic Marker ◽  
Molecular Subtype ◽  
Consensus Molecular Subtype ◽  
Stratified Analysis

scholarly journals Drug Resistance Mechanisms on Colorectal Cancer

2021 ◽  
Vol 5 (1 (January)) ◽  
pp. 88-93
Author(s):  
Hasan KURTER ◽  
Janberk YEŞİL ◽  
Ezgi DASKIN ◽  
Gizem ÇALIBAŞI KOÇAL ◽  
Hülya ELLİDOKUZ ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Drug Resistance ◽  
Resistance Mechanisms

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

scholarly journals A pre-existing population of ZEB2+ quiescent cells with stemness and mesenchymal features dictate chemoresistance in colorectal cancer

2020 ◽  
Vol 39 (1) ◽  
Author(s):  
Federica Francescangeli ◽  
Paola Contavalli ◽  
Maria Laura De Angelis ◽  
Silvia Careccia ◽  
Michele Signore ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Stem Cell ◽  
Molecular Subtype ◽  
Cancer Prognosis ◽  
Mesenchymal Transition ◽  
Tumor Xenografts ◽  
Dismal Prognosis ◽  
Large Patient ◽  
Slow Cycling ◽  
Consensus Molecular Subtype

Abstract Background Quiescent/slow cycling cells have been identified in several tumors and correlated with therapy resistance. However, the features of chemoresistant populations and the molecular factors linking quiescence to chemoresistance are largely unknown. Methods A population of chemoresistant quiescent/slow cycling cells was isolated through PKH26 staining (which allows to separate cells on the basis of their proliferation rate) from colorectal cancer (CRC) xenografts and subjected to global gene expression and pathway activation analyses. Factors expressed by the quiescent/slow cycling population were analyzed through lentiviral overexpression approaches for their ability to induce a dormant chemoresistant state both in vitro and in mouse xenografts. The correlation between quiescence-associated factors, CRC consensus molecular subtype and cancer prognosis was analyzed in large patient datasets. Results Untreated colorectal tumors contain a population of quiescent/slow cycling cells with stem cell features (quiescent cancer stem cells, QCSCs) characterized by a predetermined mesenchymal-like chemoresistant phenotype. QCSCs expressed increased levels of ZEB2, a transcription factor involved in stem cell plasticity and epithelial-mesenchymal transition (EMT), and of antiapototic factors pCRAF and pASK1. ZEB2 overexpression upregulated pCRAF/pASK1 levels resulting in increased chemoresistance, enrichment of cells with stemness/EMT traits and proliferative slowdown of tumor xenografts. In parallel, chemotherapy treatment of tumor xenografts induced the prevalence of QCSCs with a stemness/EMT phenotype and activation of the ZEB2/pCRAF/pASK1 axis, resulting in a chemotherapy-unresponsive state. In CRC patients, increased ZEB2 levels correlated with worse relapse-free survival and were strongly associated to the consensus molecular subtype 4 (CMS4) characterized by dismal prognosis, decreased proliferative rates and upregulation of EMT genes. Conclusions These results show that chemotherapy-naive tumors contain a cell population characterized by a coordinated program of chemoresistance, quiescence, stemness and EMT. Such population becomes prevalent upon drug treatment and is responsible for chemotherapy resistance, thus representing a key target for more effective therapeutic approaches.

scholarly journals Landscape of drug-resistance mutations in kinase regulatory hotspots

2020 ◽  
Author(s):  
Pora Kim ◽  
Hanyang Li ◽  
Junmei Wang ◽  
Zhongming Zhao
Keyword(s):  
Drug Resistance ◽  
Large Scale ◽  
Kinase Inhibitors ◽  
Growth Factor Receptor ◽  
Kinase Domain ◽  
Systematic Investigation ◽  
Drug Binding ◽  
Data Sets ◽  
Resistance Mutations ◽  
Cancer Data

Abstract More than 48 kinase inhibitors (KIs) have been approved by Food and Drug Administration. However, drug-resistance (DR) eventually occurs, and secondary mutations have been found in the previously targeted primary-mutated cancer cells. Cancer and drug research communities recognize the importance of the kinase domain (KD) mutations for kinasopathies. So far, a systematic investigation of kinase mutations on DR hotspots has not been done yet. In this study, we systematically investigated four types of representative mutation hotspots (gatekeeper, G-loop, αC-helix and A-loop) associated with DR in 538 human protein kinases using large-scale cancer data sets (TCGA, ICGC, COSMIC and GDSC). Our results revealed 358 kinases harboring 3318 mutations that covered 702 drug resistance hotspot residues. Among them, 197 kinases had multiple genetic variants on each residue. We further computationally assessed and validated the epidermal growth factor receptor mutations on protein structure and drug-binding efficacy. This is the first study to provide a landscape view of DR-associated mutation hotspots in kinase’s secondary structures, and its knowledge will help the development of effective next-generation KIs for better precision medicine.

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 Integration of the Microbiome, Metabolome and Transcriptomics Data Identified Novel Metabolic Pathway Regulation in Colorectal Cancer

2021 ◽  
Vol 22 (11) ◽  
pp. 5763
Author(s):  
Vartika Bisht ◽  
Katrina Nash ◽  
Yuanwei Xu ◽  
Prasoon Agarwal ◽  
Sofie Bosch ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Single Cell ◽  
Rna Sequencing ◽  
Therapeutic Targets ◽  
Data Sets ◽  
Cancer Pathogenesis ◽  
Oxidative Phosphorylation Pathway ◽  
Transcriptomics Data ◽  
Pathway Regulation ◽  
Potential Interactions

Integrative multiomics data analysis provides a unique opportunity for the mechanistic understanding of colorectal cancer (CRC) in addition to the identification of potential novel therapeutic targets. In this study, we used public omics data sets to investigate potential associations between microbiome, metabolome, bulk transcriptomics and single cell RNA sequencing datasets. We identified multiple potential interactions, for example 5-aminovalerate interacting with Adlercreutzia; cholesteryl ester interacting with bacterial genera Staphylococcus, Blautia and Roseburia. Using public single cell and bulk RNA sequencing, we identified 17 overlapping genes involved in epithelial cell pathways, with particular significance of the oxidative phosphorylation pathway and the ACAT1 gene that indirectly regulates the esterification of cholesterol. These findings demonstrate that the integration of multiomics data sets from diverse populations can help us in untangling the colorectal cancer pathogenesis as well as postulate the disease pathology mechanisms and therapeutic targets.

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