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

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.

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

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.

scholarly journals Cooperation of Cancer Stem Cell Properties and Epithelial-Mesenchymal Transition in the Establishment of Breast Cancer Metastasis

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Tetsu Hayashida ◽  
Hiromitsu Jinno ◽  
Yuko Kitagawa ◽  
Masaki Kitajima
Keyword(s):  
Breast Cancer ◽  
Stem Cell ◽  
Cancer Stem Cell ◽  
Cancer Progression ◽  
Cancer Metastasis ◽  
Epithelial Mesenchymal Transition ◽  
Metastatic Tumor ◽  
Breast Cancer Metastasis ◽  
Cell Junctions ◽  
Mesenchymal Transition

Epithelial-mesenchymal transition (EMT) is a multistep process in which cells acquire molecular alterations such as loss of cell-cell junctions and restructuring of the cytoskeleton. There is an increasing understanding that this process may promote breast cancer progression through promotion of invasive and metastatic tumor growth. Recent observations imply that there may be a cross-talk between EMT and cancer stem cell properties, leading to enhanced tumorigenicity and the capacity to generate heterogeneous tumor cell populations. Here, we review the experimental and clinical evidence for the involvement of EMT in cancer stem cell theory, focusing on the common characteristics of this phenomenon.

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

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

Protein tyrosine kinase 6 (PTK6, BRK) amplification in HER2+ breast cancer as a mechanism of HER2 resistance.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 11021-11021
Author(s):  
Tatyana A. Grushko ◽  
Maria J. Gomez-Vega ◽  
Aleix Prat ◽  
Jeffrey Mueller ◽  
Mariann Coyle ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Tyrosine Kinase ◽  
Mrna Expression ◽  
Cancer Progression ◽  
Gene Copy ◽  
Normal Epithelium ◽  
Cancer Data ◽  
Her2 Breast Cancer ◽  
Study Results

11021 Background: PTK6 gene on chromosome 20q13 encodes the intracellular non-receptor tyrosine kinase. Studies in vivo and in vitro revealed a role for PTK6 in cell proliferation and survival, particularly in HER2+ breast cancer cells suggesting that PTK6 may associate with the HER2 pathway and confer resistance to HER2-targeted therapy. PTK6 protein is frequently overexpressed in breast cancer, however, the mechanism(s) underlying PTK6 overexpression and its role in cancer remains unclear. To address this problem, we analyzed the frequency of PTK6 gene copy number variation (CNV) and expression in association with breast cancer subtypes. Methods: Retrospectiveparaffinsamples of invasive tumor and normal epithelium, and matching DCIS and metastases were mounted on TMA. PTK6 CNV was determined using PTK6:CEP20 FISH assay. Tumor subtypes were defined using the five-marker IHC classifier. The correlation between PTK6 CNV and mRNA expression and association of both with the intrinsic PAM50 tumor subtype were studied using TCGA database (547 cases) and publicly available seven breast cancer data sets (1005 cases). Data were normalized, gene median centered and standardized for the purpose of the study. Results: By FISH, 20% of 41 invasive tumors carried PTK6 CNV: amplification (10%) and gene polysomy (10%). The proportion of PTK6 amplified cases differed by subtype, with the largest proportion in HER2-enriched (17%) and LumB (14%). Strikingly, amplified invasive cases also showed amplification in matching DCIS and metastases. Analysis of the public datasets confirmed the frequent PTK6 amplification in breast cancer. Both low and high levels of amplification were detected with the largest proportion in HER2+ tumors (HER2-enriched and LumB; p=2.05e-26). None of the basal-like tumors showed high levels of PTK6 amplification. A high correlation between PTK6 gene copies and mRNA expression was observed (p=1.13e-08). Conclusions: PTK6 gene is amplified early in breast cancer progression, particularly in HER2+ tumors. Further studies on PTK6 biology may help clinicians to understand its potential role in HER2 resistance. Supported by BREAST CANCER SPORE, NCI K12CA139160 and CTSA-ITM CS UL1 RR024999.

scholarly journals Metabolic Stress Adaptations Underlie Mammary Gland Morphogenesis and Breast Cancer Progression

Cells ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 2641
Author(s):  
Chun-Chao Wang
Keyword(s):  
Breast Cancer ◽  
Mammary Gland ◽  
Cancer Progression ◽  
Cancer Metabolism ◽  
Experimental Models ◽  
Premalignant Lesions ◽  
Primary Tumors ◽  
Cancer Subtypes ◽  
Mammary Gland Morphogenesis ◽  
Gland Morphogenesis

Breast cancers display dynamic reprogrammed metabolic activities as cancers develop from premalignant lesions to primary tumors, and then metastasize. Numerous advances focus on how tumors develop pro-proliferative metabolic signaling that differs them from adjacent, non-transformed epithelial tissues. This leads to targetable oncogene-driven liabilities among breast cancer subtypes. Other advances demonstrate how microenvironments trigger stress-response at single-cell resolution. Microenvironmental heterogeneities give rise to cell regulatory states in cancer cell spheroids in three-dimensional cultures and at stratified terminal end buds during mammary gland morphogenesis, where stress and survival signaling juxtapose. The cell-state specificity in stress signaling networks recapture metabolic evolution during cancer progression. Understanding lineage-specific metabolic phenotypes in experimental models is useful for gaining a deeper understanding of subtype-selective breast cancer metabolism.

scholarly journals Association Analysis of Obesity/Overweight and Breast Cancer Using Data Mining Techniques

2021 ◽  
Vol 10 (1) ◽  
pp. 60
Author(s):  
Mahsa Dehghani Soufi ◽  
Reza Ferdousi
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Hierarchical Clustering ◽  
Cancer Progression ◽  
Clustering Algorithm ◽  
Breast Cancer Incidence ◽  
Research Work ◽  
Model Assessment ◽  
Cancer Data ◽  
Data Mining Algorithms

Introduction: Growing evidence has shown that some overweight factors could be implicated in tumor genesis, higher recurrence and mortality. In addition, association of various overweight factors and breast cancer has not been extensively explored. The goal of this research was to explore and evaluate the association of various overweight/obesity factors and breast cancer, based on obesity breast cancer data set.Material and Methods: Several studies show that a significantly stronger association is obvious between overweight and higher breast cancer incidence, but the role of some overweight factors such as BMI, insulin-resistance, Homeostasis Model Assessment (HOMA), Leptin, adiponectin, glucose and MCP.1 is still debatable, So for experiment of research work several clinical and biochemical overweight factors, including age, Body Mass Index (BMI), Glucose, Insulin, Homeostatic Model Assessment (HOMA), Leptin, Adiponectin, Resistin and Monocyte chemo attractant protein-1(MCP-1) were analyzed. Data mining algorithms including k-means, Apriori, Hierarchical clustering algorithm (HCM) were applied using orange version 3.22 as an open source data mining tool.Results: The Apriori algorithm generated a list of frequent item sets and some strong rules from dataset and found that insulin, HOMA and leptin are two items often simultaneously were seen for BC patients that leads to cancer progression. K-means algorithm applied and it divided samples on three clusters and its results showed that the pair of andlt;Adiponectin, MCP.1andgt;  has the highest effect on seperation of clusters. In addition HCM was carried out and classified BC patients into 1-32 clusters to So this research apply HCM algorithm. We carried out hierarchical clustering with average linkage without purning and classified BC patients into 1–32 clusters in order to identify BC patients with similar charestrictics.Conclusion: These finding provide the employed algorithms in this study can be helpful to our aim.

scholarly journals Engineering Breast Cancer On-chip—Moving Toward Subtype Specific Models

2021 ◽  
Vol 9 ◽  
Author(s):  
Carmen Moccia ◽  
Kristina Haase
Keyword(s):  
Breast Cancer ◽  
Tumor Microenvironment ◽  
Cancer Progression ◽  
Triple Negative ◽  
New Drugs ◽  
Patient Specific ◽  
Preclinical Models ◽  
Cancer Subtypes ◽  
On Chip

Breast cancer is the second leading cause of death among women worldwide, and while hormone receptor positive subtypes have a clear and effective treatment strategy, other subtypes, such as triple negative breast cancers, do not. Development of new drugs, antibodies, or immune targets requires significant re-consideration of current preclinical models, which frequently fail to mimic the nuances of patient-specific breast cancer subtypes. Each subtype, together with the expression of different markers, genetic and epigenetic profiles, presents a unique tumor microenvironment, which promotes tumor development and progression. For this reason, personalized treatments targeting components of the tumor microenvironment have been proposed to mitigate breast cancer progression, particularly for aggressive triple negative subtypes. To-date, animal models remain the gold standard for examining new therapeutic targets; however, there is room for in vitro tools to bridge the biological gap with humans. Tumor-on-chip technologies allow for precise control and examination of the tumor microenvironment and may add to the toolbox of current preclinical models. These new models include key aspects of the tumor microenvironment (stroma, vasculature and immune cells) which have been employed to understand metastases, multi-organ interactions, and, importantly, to evaluate drug efficacy and toxicity in humanized physiologic systems. This review provides insight into advanced in vitro tumor models specific to breast cancer, and discusses their potential and limitations for use as future preclinical patient-specific tools.

scholarly journals Cis-Compound Mutations are Prevalent in Triple Negative Breast Cancer and Can Drive Tumor Progression

2016 ◽  
Author(s):  
Nao Hiranuma ◽  
Jie Liu ◽  
Chaozhong Song ◽  
Jacob Goldsmith ◽  
Michael Dorschner ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Progression ◽  
Triple Negative ◽  
The Cancer Genome Atlas ◽  
Breast Cancers ◽  
Single Nucleotide Variants ◽  
Primary Tumors ◽  
Cancer Subtypes ◽  
Multiple Biopsies ◽  
Cancer Genome Atlas

About 16% of breast cancers fall into a clinically aggressive category designated triple negative (TNBC) due to a lack of ERBB2, estrogen receptor and progesterone receptor expression1-3. The mutational spectrum of TNBC has been characterized as part of The Cancer Genome Atlas (TCGA)4; however, snapshots of primary tumors cannot reveal the mechanisms by which TNBCs progress and spread. To address this limitation we initiated the Intensive Trial of OMics in Cancer (ITOMIC)-001, in which patients with metastatic TNBC undergo multiple biopsies over space and time5. Whole exome sequencing (WES) of 67 samples from 11 patients identified 426 genes containing multiple distinct single nucleotide variants (SNVs) within the same sample, instances we term Multiple SNVs affecting the Same Gene and Sample (MSSGS). We find that >90% of MSSGS result from cis-compound mutations (in which both SNVs affect the same allele), that MSSGS comprised of SNVs affecting adjacent nucleotides arise from single mutational events, and that most other MSSGS result from the sequential acquisition of SNVs. Some MSSGS drive cancer progression, as exemplified by a TNBC driven by FGFR2(S252W;Y375C). MSSGS are more prevalent in TNBC than other breast cancer subtypes and occur at higher-than-expected frequencies across TNBC samples within TCGA. MSSGS may denote genes that play as yet unrecognized roles in cancer progression.

scholarly journals Uncovering the subtype-specific temporal order of cancer pathway dysregulation

2019 ◽  
Author(s):  
Sahand Khakabmamaghani ◽  
Dujian Ding ◽  
Oliver Snow ◽  
Martin Ester
Keyword(s):  
Cancer Progression ◽  
Colorectal Adenocarcinoma ◽  
Temporal Order ◽  
Cell Function ◽  
Synthetic Data ◽  
Genetic Mutations ◽  
Cancer Subtypes ◽  
Cancer Pathways ◽  
Progression Model ◽  
Specific Pathway

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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