Literature DB >> 30952823

Prediction of Bone Metastasis in Inflammatory Breast Cancer Using a Markov Chain Model.

Takeo Fujii1,2, Jeremy Mason3,4, Angela Chen3, Peter Kuhn3,5,6,7,4, Wendy A Woodward2,8, Debu Tripathy1, Paul K Newton9,5,7, Naoto T Ueno10,2.   

Abstract

BACKGROUND: Inflammatory breast cancer (IBC) is a rare yet aggressive variant of breast cancer with a high recurrence rate. We hypothesized that patterns of metastasis differ between IBC and non-IBC. We focused on the patterns of bone metastasis throughout disease progression to determine statistical differences that can lead to clinically relevant outcomes. Our primary outcome of this study is to quantify and describe this difference with a view to applying the findings to clinically relevant outcomes for patients. SUBJECTS, MATERIALS, AND METHODS: We retrospectively collected data of patients with nonmetastatic IBC (n = 299) and non-IBC (n = 3,436). Probabilities of future site-specific metastases were calculated. Spread patterns were visualized to quantify the most probable metastatic pathways of progression and to categorize spread pattern based on their propensity to subsequent dissemination of cancer.
RESULTS: In patients with IBC, the probabilities of developing bone metastasis after chest wall, lung, or liver metastasis as the first site of progression were high: 28%, 21%, and 21%, respectively. For patients with non-IBC, the probability of developing bone metastasis was fairly consistent regardless of initial metastasis site.
CONCLUSION: Metastatic patterns of spread differ between patients with IBC and non-IBC. Selection of patients with IBC with known liver, chest wall, and/or lung metastasis would create a population in whom to investigate effective methods for preventing future bone metastasis. IMPLICATIONS FOR PRACTICE: This study demonstrated that the patterns of metastasis leading to and following bone metastasis differ significantly between patients with inflammatory breast cancer (IBC) and those with non-IBC. Patients with IBC had a progression pattern that tended toward the development of bone metastasis if they had previously developed metastases in the liver, chest wall, and lung, rather than in other sites. Selection of patients with IBC with known liver, chest wall, and/or lung metastasis would create a population in whom to investigate effective methods for preventing future bone metastasis. © AlphaMed Press 2019.

Entities:  

Keywords:  Bone metastasis; Breast cancer; Inflammatory breast cancer; Markov chain model; Prediction model

Year:  2019        PMID: 30952823      PMCID: PMC6795167          DOI: 10.1634/theoncologist.2018-0713

Source DB:  PubMed          Journal:  Oncologist        ISSN: 1083-7159


  25 in total

Review 1.  Mechanisms of bone metastasis.

Authors:  G David Roodman
Journal:  N Engl J Med       Date:  2004-04-15       Impact factor: 91.245

2.  Cancer statistics, 2018.

Authors:  Rebecca L Siegel; Kimberly D Miller; Ahmedin Jemal
Journal:  CA Cancer J Clin       Date:  2018-01-04       Impact factor: 508.702

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Authors:  Sukhbinder Dhesy-Thind; Glenn G Fletcher; Phillip S Blanchette; Mark J Clemons; Melissa S Dillmon; Elizabeth S Frank; Sonal Gandhi; Rasna Gupta; Mihaela Mates; Beverly Moy; Ted Vandenberg; Catherine H Van Poznak
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4.  Adjuvant denosumab in breast cancer (ABCSG-18): a multicentre, randomised, double-blind, placebo-controlled trial.

Authors:  Michael Gnant; Georg Pfeiler; Peter C Dubsky; Michael Hubalek; Richard Greil; Raimund Jakesz; Viktor Wette; Marija Balic; Ferdinand Haslbauer; Elisabeth Melbinger; Vesna Bjelic-Radisic; Silvia Artner-Matuschek; Florian Fitzal; Christian Marth; Paul Sevelda; Brigitte Mlineritsch; Günther G Steger; Diether Manfreda; Ruth Exner; Daniel Egle; Jonas Bergh; Franz Kainberger; Susan Talbot; Douglas Warner; Christian Fesl; Christian F Singer
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5.  Which threshold for ER positivity? a retrospective study based on 9639 patients.

Authors:  M Yi; L Huo; K B Koenig; E A Mittendorf; F Meric-Bernstam; H M Kuerer; I Bedrosian; A U Buzdar; W F Symmans; J R Crow; M Bender; R R Shah; G N Hortobagyi; K K Hunt
Journal:  Ann Oncol       Date:  2014-02-20       Impact factor: 32.976

6.  RANKL-induced migration of MDA-MB-231 human breast cancer cells via Src and MAPK activation.

Authors:  Zhen-Ning Tang; Fan Zhang; Peng Tang; Xiao-Wei Qi; Jun Jiang
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7.  RANK induces epithelial-mesenchymal transition and stemness in human mammary epithelial cells and promotes tumorigenesis and metastasis.

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Journal:  Cancer Res       Date:  2012-04-10       Impact factor: 12.701

Review 8.  Clinical potential of RANKL inhibition for the management of postmenopausal osteoporosis and other metabolic bone diseases.

Authors:  Pierre D Delmas
Journal:  J Clin Densitom       Date:  2008-04-02       Impact factor: 2.617

9.  Inflammatory infiltrate in invasive lobular and ductal carcinoma of the breast.

Authors:  A H Lee; L C Happerfield; R R Millis; L G Bobrow
Journal:  Br J Cancer       Date:  1996-09       Impact factor: 7.640

10.  Comparison of molecular subtype distribution in triple-negative inflammatory and non-inflammatory breast cancers.

Authors:  Hiroko Masuda; Keith A Baggerly; Ying Wang; Takayuki Iwamoto; Takae Brewer; Lajos Pusztai; Kazuharu Kai; Takahiro Kogawa; Pascal Finetti; Daniel Birnbaum; Luc Dirix; Wendy A Woodward; James M Reuben; Savitri Krishnamurthy; W Symmans; Steven J Van Laere; François Bertucci; Gabriel N Hortobagyi; Naoto T Ueno
Journal:  Breast Cancer Res       Date:  2013-11-25       Impact factor: 6.466

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Journal:  J Occup Environ Hyg       Date:  2022-02-22       Impact factor: 2.155

2.  Extracting Rules via Markov Chains for Cryptocurrencies Returns Forecasting.

Authors:  Kerolly Kedma Felix do Nascimento; Fábio Sandro Dos Santos; Jader Silva Jale; Silvio Fernando Alves Xavier Júnior; Tiago A E Ferreira
Journal:  Comput Econ       Date:  2022-02-11       Impact factor: 1.876

3.  Development and Validation of a Decision Analytical Model for Posttreatment Surveillance for Patients With Oropharyngeal Carcinoma.

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Journal:  JAMA Netw Open       Date:  2022-04-01
  3 in total

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