Uncovering the subtype-specific temporal order of cancer pathway dysregulation

AbstractCancer is driven by genetic mutations that dysregulate pathways important for proper cell function. Therefore, discovering these cancer pathways and their dysregulation order is key to understanding and treating cancer. However, the heterogeneity of mutations between different individuals makes this challenging and requires that cancer progression is studied in a subtype-specific way. To address this challenge, we provide a mathematical model, called Subtype-specific Pathway Linear Progression Model (SPM), that simultaneously captures cancer subtypes and pathways and order of dysregulation of the pathways within each subtype. Experiments with synthetic data indicate the robustness of SPM to problem specifics including noise compared to an existing method. Moreover, experimental results on glioblastoma multiforme and colorectal adenocarcinoma show the consistency of SPM’s results with the existing knowledge and its superiority to an existing method in certain cases. The implementation of our method is available at https://github.com/Dalton386/SPM.

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Oncogenetic Network Estimation with Disjunctive Bayesian Networks: Learning from Unstratified Samples while Preserving Mutual Exclusivity Relations

10.1101/2020.04.13.040022 ◽

2020 ◽

Author(s):

Phillip B. Nicol ◽

Kevin R. Coombes ◽

Courtney Deaver ◽

Oksana A. Chkrebtii ◽

Subhadeep Paul ◽

...

Keyword(s):

Cancer Progression ◽

Synthetic Data ◽

Ground Truth ◽

Genetic Alterations ◽

Mutual Exclusivity ◽

Cross Sectional ◽

Cancer Subtypes ◽

Cancer Data ◽

Progression Model ◽

Network Estimation

ABSTRACTCancer is the process of accumulating genetic alterations that confer selective advantages to tumor cells. The order in which aberrations occur is not arbitrary, and inferring the order of events is a challenging problem due to the lack of longitudinal samples from tumors. Moreover, a network model of oncogenesis should capture biological facts such as distinct progression trajectories of cancer subtypes and patterns of mutual exclusivity of alterations in the same pathways. In this paper, we present the Disjunctive Bayesian Network (DBN), a novel cancer progression model. Unlike previous models of oncogenesis, DBN naturally captures mutually exclusive alterations. Besides, DBN is flexible enough to represent progression trajectories of cancer subtypes, therefore allowing one to learn the progression network from unstratified data, i.e., mixed samples from multiple subtypes. We provide a scalable genetic algorithm to learn the structure of DBN from cross-sectional cancer data. To test our model, we simulate synthetic data from known progression networks and show that our algorithm infers the ground truth network with high accuracy. Finally, we apply our model to copy number data for colon cancer and mutation data for bladder cancer and observe that the recovered progression network matches known biological facts.

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Molecular Profiles of Matched Primary and Metastatic Tumor Samples Support a Linear Evolutionary Model of Breast Cancer

10.1101/667303 ◽

2019 ◽

Author(s):

Runpu Chen ◽

Steve Goodison ◽

Yijun Sun

Keyword(s):

Breast Cancer ◽

Cancer Progression ◽

Evolutionary Model ◽

Metastatic Tumor ◽

Cancer Diagnostics ◽

Tumor Evolution ◽

Paired Data ◽

Cancer Subtypes ◽

Cancer Data ◽

Progression Model

AbstractThe interpretation of accumulating genomic data with respect to tumor evolution and cancer progression requires integrated models. We developed a computational approach that enables the construction of disease progression models using static sample data. Application to breast cancer data revealed a linear, branching evolutionary model with two distinct trajectories for malignant progression. Here, we used the progression model as a foundation to investigate the relationships between matched primary and metastasis breast tumor samples. Mapping paired data onto the model confirmed that molecular breast cancer subtypes can shift during progression, and supported directional tumor evolution through luminal subtypes to increasingly malignant states. Cancer progression modeling through the analysis of available static samples represents a promising breakthrough. Further refinement of a roadmap of breast cancer progression will facilitate the development of improved cancer diagnostics, prognostics and targeted therapeutics.

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YB-1 Is Altered in Pregnancy-Associated Disorders and Affects Trophoblast in Vitro Properties via Alternation of Multiple Molecular Traits

International Journal of Molecular Sciences ◽

10.3390/ijms22137226 ◽

2021 ◽

Vol 22 (13) ◽

pp. 7226

Author(s):

Violeta Stojanovska ◽

Aneri Shah ◽

Katja Woidacki ◽

Florence Fischer ◽

Mario Bauer ◽

...

Keyword(s):

Cell Lines ◽

Cancer Progression ◽

Cell Function ◽

Trophoblast Cell ◽

Mrna Levels ◽

Trophoblast Cells ◽

Invasion And Migration ◽

And Migration ◽

Apoptosis And Inflammation

Cold shock Y-box binding protein-1 (YB-1) coordinates several molecular processes between the nucleus and the cytoplasm and plays a crucial role in cell function. Moreover, it is involved in cancer progression, invasion, and metastasis. As trophoblast cells share similar characteristics with cancer cells, we hypothesized that YB-1 might also be necessary for trophoblast functionality. In samples of patients with intrauterine growth restriction, YB-1 mRNA levels were decreased, while they were increased in preeclampsia and unchanged in spontaneous abortions when compared to normal pregnant controls. Studies with overexpression and downregulation of YB-1 were performed to assess the key trophoblast processes in two trophoblast cell lines HTR8/SVneo and JEG3. Overexpression of YB-1 or exposure of trophoblast cells to recombinant YB-1 caused enhanced proliferation, while knockdown of YB-1 lead to proliferative disadvantage in JEG3 or HTR8/SVneo cells. The invasion and migration properties were affected at different degrees among the trophoblast cell lines. Trophoblast expression of genes mediating migration, invasion, apoptosis, and inflammation was altered upon YB-1 downregulation. Moreover, IL-6 secretion was excessively increased in HTR8/SVneo. Ultimately, YB-1 directly binds to NF-κB enhancer mark in HTR8/SVneo cells. Our data show that YB-1 protein is important for trophoblast cell functioning and, when downregulated, leads to trophoblast disadvantage that at least in part is mediated by NF-κB.

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Algorithmic Methods to Infer the Evolutionary Trajectories in Cancer Progression

10.1101/027359 ◽

2015 ◽

Cited By ~ 3

Author(s):

Giulio Caravagna ◽

Alex Graudenzi ◽

DANIELE RAMAZZOTTI ◽

Rebeca Sanz-Pamplona ◽

Luca De Sano ◽

...

Keyword(s):

Cancer Progression ◽

Current Knowledge ◽

Population Level ◽

Selective Advantage ◽

Explanatory Models ◽

Driver Mutations ◽

Cross Sectional ◽

Progression Model ◽

Colorectal Cancer Progression ◽

Ngs Data

The genomic evolution inherent to cancer relates directly to a renewed focus on the voluminous next generation sequencing (NGS) data, and machine learning for the inference of explanatory models of how the (epi)genomic events are choreographed in cancer initiation and development. However, despite the increasing availability of multiple additional -omics data, this quest has been frustrated by various theoretical and technical hurdles, mostly stemming from the dramatic heterogeneity of the disease. In this paper, we build on our recent works on "selective advantage" relation among driver mutations in cancer progression and investigate its applicability to the modeling problem at the population level. Here, we introduce PiCnIc (Pipeline for Cancer Inference), a versatile, modular and customizable pipeline to extract ensemble-level progression models from cross-sectional sequenced cancer genomes. The pipeline has many translational implications as it combines state-of-the-art techniques for sample stratification, driver selection, identification of fitness-equivalent exclusive alterations and progression model inference. We demonstrate PiCnIc's ability to reproduce much of the current knowledge on colorectal cancer progression, as well as to suggest novel experimentally verifiable hypotheses.

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Contactin 1: An Important and Emerging Oncogenic Protein Promoting Cancer Progression and Metastasis

Genes ◽

10.3390/genes11080874 ◽

2020 ◽

Vol 11 (8) ◽

pp. 874

Author(s):

Yan Gu ◽

Taosha Li ◽

Anil Kapoor ◽

Pierre Major ◽

Damu Tang

Keyword(s):

Breast Cancer ◽

Cancer Progression ◽

Epithelial Mesenchymal Transition ◽

System Development ◽

Notch Signaling Pathway ◽

Experimental Models ◽

Vegf Receptor ◽

Mesenchymal Transition ◽

Cancer Pathways ◽

Factor C

Even with recent progress, cancer remains the second leading cause of death, outlining a need to widen the current understanding on oncogenic factors. Accumulating evidence from recent years suggest Contactin 1 (CNTN1)’s possession of multiple oncogenic activities in a variety of cancer types. CNTN1 is a cell adhesion molecule that is dysregulated in many human carcinomas and plays important roles in cancer progression and metastases. Abnormalities in CNTN1 expression associate with cancer progression and poor prognosis. Mechanistically, CNTN1 functions in various signaling pathways frequently altered in cancer, such as the vascular endothelial growth factor C (VEGFC)-VEGF receptor 3 (VEFGR3)/fms-related tyrosine kinase 4 (Flt4) axis, phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT), Notch signaling pathway and epithelial-mesenchymal transition (EMT) process. These oncogenic events are resulted via interactions between tumor and stroma, which can be contributed by CNTN1, an adhesion protein. CNTN1 expression in breast cancer correlates with the expression of genes functioning in cancer-stroma interactions and skeletal system development. Evidence supports that CNTN1 promotes cancer-stromal interaction, resulting in activation of a complex network required for cancer progression and metastasis (bone metastasis for breast cancer). CNTN1 inhibitions has been proven to be effective in experimental models to reduce oncogenesis. In this paper, we will review CNTN1′s alterations in cancer, its main biochemical mechanisms and interactions with its relevant cancer pathways.

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gpps: an ILP-based approach for inferring cancer progression with mutation losses from single cell data

BMC Bioinformatics ◽

10.1186/s12859-020-03736-7 ◽

2020 ◽

Vol 21 (S1) ◽

Author(s):

Simone Ciccolella ◽

Mauricio Soto Gomez ◽

Murray D. Patterson ◽

Gianluca Della Vedova ◽

Iman Hajirasouliha ◽

...

Keyword(s):

Single Cell ◽

Cancer Progression ◽

False Negative ◽

Fixed Number ◽

Phylogeny Reconstruction ◽

Sequencing Data ◽

Progression Model ◽

Great Specificity ◽

Cell Data ◽

Tumor Phylogeny

Abstract Background Cancer progression reconstruction is an important development stemming from the phylogenetics field. In this context, the reconstruction of the phylogeny representing the evolutionary history presents some peculiar aspects that depend on the technology used to obtain the data to analyze: Single Cell DNA Sequencing data have great specificity, but are affected by moderate false negative and missing value rates. Moreover, there has been some recent evidence of back mutations in cancer: this phenomenon is currently widely ignored. Results We present a new tool, , that reconstructs a tumor phylogeny from Single Cell Sequencing data, allowing each mutation to be lost at most a fixed number of times. The General Parsimony Phylogeny from Single cell () tool is open source and available at https://github.com/AlgoLab/gpps. Conclusions provides new insights to the analysis of intra-tumor heterogeneity by proposing a new progression model to the field of cancer phylogeny reconstruction on Single Cell data.

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Casodex treatment induces hypoxia-related gene expression in the LNCaP prostate cancer progression model

BMC Urology ◽

10.1186/1471-2490-5-5 ◽

2005 ◽

Vol 5 (1) ◽

Cited By ~ 15

Author(s):

Christy A Rothermund ◽

Velliyur K Gopalakrishnan ◽

James D Eudy ◽

Jamboor K Vishwanatha

Keyword(s):

Gene Expression ◽

Prostate Cancer ◽

Cancer Progression ◽

Prostate Cancer Progression ◽

Related Gene ◽

Progression Model

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Abstract 4468: Repression of CD44 expression by p53 in a breast cancer progression model

10.1158/1538-7445.am2018-4468 ◽

2018 ◽

Author(s):

Fang Liu ◽

Zhengxue Liu ◽

Guannan Wang ◽

Tanima Roy-Kundu ◽

Isao Matsuura

Keyword(s):

Breast Cancer ◽

Cancer Progression ◽

Breast Cancer Progression ◽

Cd44 Expression ◽

Progression Model

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Identification of Genetic Mutations in Human Lung Cancer by Targeted Sequencing

Cancer Informatics ◽

10.4137/cin.s22941 ◽

2015 ◽

Vol 14 ◽

pp. CIN.S22941 ◽

Cited By ~ 6

Author(s):

Hongxiang Feng ◽

Xiaowei Wang ◽

Zhenrong Zhang ◽

Chuanning Tang ◽

Hua Ye ◽

...

Keyword(s):

Lung Cancer ◽

Human Lung ◽

Chinese Patients ◽

Human Lung Cancer ◽

Genetic Mutations ◽

Ion Torrent ◽

Cancer Subtypes ◽

Formalin Fixed Paraffin ◽

Formalin Fixed Paraffin Embedded ◽

Frequent Mutations

Lung cancer remains the most prevalent malignancy and the primary cause of cancer-related deaths worldwide. Unique mutations patterns can be found in lung cancer subtypes, in individual cancers, or within a single tumor, and drugs that target these genetic mutations and signal transduction pathways are often beneficial to patients. In this study, we used the Ion Torrent AmpliSeq Cancer Panel to sequence 737 loci from 45 cancer-related genes and oncogenes to identify genetic mutations in 48 formalin-fixed, paraffin-embedded (FFPE) human lung cancer samples from Chinese patients. We found frequent mutations in EGFR, KRAS, PIK3CA, and TP53 genes. Moreover, we observed that a portion of the lung cancer samples harbored two or more mutations in these key genes. This study demonstrates the feasibility of using the Ion Torrent sequencing to efficiently identify genetic mutations in individual tumors for targeted lung cancer therapy.

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