Distinct gene expression profiles between primary breast cancers and brain metastases from pair-matched samples

Inomata, Takayuki; Niikura, Naoki; Ogiya, Rin; Yasojima, Hiroyuki; Watanabe, Ken Ichi; Kanbayashi, Chizuko; Tsuneizumi, Michiko; Matsui, Akira; Fujisawa, Tomomi; Iwasa, Tsutomu; Shien, Tadahiko; Masuda, Norikazu; Iwata, Hiroji

doi:10.1038/s41598-019-50099-y

Cited by 193 publications

(557 citation statements)

References 33 publications

(46 reference statements)

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“…The confusion matrix ( Supplementary Figure 3) further delineates the shared gene expression signatures between primary and metastatic tumors. This result agreed with earlier studies that have also shown majority genes' expression of primary and metastasized tumors resemble each other [23][24][25].…”

Section: Predicting Types Of Unseen Primary and Metastatic Tumors Witsupporting

confidence: 93%

CancerSiamese: one-shot learning for predicting primary and metastatic tumor types unseen during model training

Mostavi

Chiu²,

Chen³

et al. 2020

Preprint

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We consider cancer classification based on one single gene expression profile. We proposed CancerSiamese, a new one-shot learning model, to predict the cancer type of a query primary or metastatic tumor sample based on a support set that contains only one known sample for each cancer type. CancerSiamese receives pairs of gene expression profiles and learns a representation of similar or dissimilar cancer types through two parallel Convolutional Neural Networks joined by a similarity function. We trained CancerSiamese for both primary and metastatic cancer type predictions using samples from TCGA and MET500. Test results for different N-way predictions yielded an average accuracy improvement of 8% and 4% over the benchmark 1-Nearest Neighbor (1-NN) classifier for primary and metastatic tumors, respectively. Moreover, we applied the guided gradient saliency map and feature selection to CancerSiamese to identify and analyze the marker-gene candidates for primary and metastatic cancers. Our work demonstrated, for the first time, the feasibility of applying one-shot learning for expression-based cancer type prediction when gene expression data of cancer types are limited and could inspire new and ingenious applications of one-shot and few-shot learning solutions for improving cancer diagnosis, treatment planning, and our understanding of cancer.

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Section: Predicting Types Of Unseen Primary and Metastatic Tumors Witsupporting

confidence: 93%

CancerSiamese: one-shot learning for predicting primary and metastatic tumor types unseen during model training

Mostavi

Chiu²,

Chen³

et al. 2020

Preprint

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show abstract

“…In agreement with the findings of other studies, we have identified a number of differences between the primary tumor and metastatic transcriptomes (29,30,32,33). However, by combining data from several models, we show that more significant differences exist in the gene expression between models than between primary tumors and matched metastases, and in fact model may play a larger role in shaping the metastatic transcriptome than tissue type or oncogenic driver.…”

Section: T1supporting

confidence: 91%

Metastasis-Specific Gene Expression in Autochthonous and Allograft Mouse Mammary Tumor Models: Stratification and Identification of Targetable Signatures

Ross

Szczepanek

Lee

et al. 2020

Molecular Cancer Research

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Breast cancer metastasis is a leading cause of cancer-related death of women in the United States. A hurdle in advancing metastasis-targeted intervention is the phenotypic heterogeneity between primary and secondary lesions. To identify metastasisspecific gene expression profiles we performed RNA-sequencing of breast cancer mouse models; analyzing metastases from models of various drivers and routes. We contrasted the models and identified common, targetable signatures. Allograft models exhibited more mesenchymal-like gene expression than genetically engineered mouse models (GEMM), and primary culturing of GEMM-derived metastatic tissue induced mesenchymal-like gene expression. In addition, metastasis-specific transcriptomes differed between tail vein and orthotopic injection of the same cell line. Gene expression common to models of spontaneous metastasis included sildenafil response and nicotine degradation pathways. Strikingly, in vivo sildenafil treatment significantly reduced metastasis by 54%, while nicotine significantly increased metastasis by 46%. These data suggest that (i) actionable metastasis-specific pathways can be readily identified, (ii) already available drugs may have great potential to alleviate metastatic incidence, and (iii) metastasis may be influenced greatly by lifestyle choices such as the choice to consume nicotine products. In summary, while mouse models of breast cancer metastasis vary in ways that must not be ignored, there are shared features that can be identified and potentially targeted therapeutically. Implications: The data we present here exposes critical variances between preclinical models of metastatic breast cancer and identifies targetable pathways integral to metastatic spread.

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“…The incidence of brain metastases in cancer patients is rising, which may be due to the improvements in systematic therapies to control extracranial disease and prolong the survive of patients. Thus, patients who previously may have died sooner from other manifestations of the disease may develop brain metastases [1]. Breast cancer is the second most common cause of brain metastases following lung cancer [2].…”

Section: Introductionmentioning

confidence: 99%

Genomic landscape of the immune microenvironments of brain metastases in breast cancer

Xie

Yuan

et al. 2020

J Transl Med

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Background: This study was intended to investigate the genomic landscape of the immune microenvironments of brain metastases in breast cancer. Methods: Three gene expression profile datasets (GSE76714, GSE125989 and GSE43837) of breast cancer with brain metastases were downloaded from Gene Expression Omnibus (GEO) database. After differential expression analysis, the tumor immune microenvironment and immune cell infiltration were analyzed. Then immune-related genes were identified, followed by function analysis, transcription factor (TF)-miRNA-mRNA co-regulatory network analysis, and survival analysis of metastatic recurrence. Results: The present results showed that the tumor immune microenvironment in brain metastases was immunosuppressed compared with primary caner. Compared with primary cancer samples, the infiltration ratio of plasma cells in brain metastases samples was significantly higher, while the infiltration ratio of macrophages M2 cells in brain metastases samples was significantly lower. Total 42 immune-related genes were identified, such as THY1 and NEU2. CD1B, THY1 and DOCK2 were found to be implicated in the metastatic recurrence of breast cancer. Conclusions: Targeting macrophages or plasma cells may be new strategies for immunotherapy of breast cancer with brain metastases. THY1 and NEU2 may be potential therapeutic targets for breast cancer with brain metastases, and THY1, CD1B and DOCK2 may serve as potential prognostic markers for improvement of brain metastases survival.

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Distinct gene expression profiles between primary breast cancers and brain metastases from pair-matched samples

Cited by 193 publications

References 33 publications

CancerSiamese: one-shot learning for predicting primary and metastatic tumor types unseen during model training

CancerSiamese: one-shot learning for predicting primary and metastatic tumor types unseen during model training

Metastasis-Specific Gene Expression in Autochthonous and Allograft Mouse Mammary Tumor Models: Stratification and Identification of Targetable Signatures

Genomic landscape of the immune microenvironments of brain metastases in breast cancer

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