Short somatic alterations at the site of copy number variation in breast cancer

Murakami, Fumi; Tsuboi, Yoshikatsu; Takahashi, Yuka; Horimoto, Yoshiya; Mogushi, Kaoru; Ito, Takeshi; Emi, Mitsuru; Matsubara, Daisuke; Shibata, Tatsuhiro; Saito, Mitsue; Murakami, Yoshinori

doi:10.1111/cas.14630

Cited by 8 publications

(8 citation statements)

References 44 publications

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“…We found that a large number of copy number alterations were detected, with amplification of part of chromosomes 1q, 8q, 16p, and 17q, and deletion of part of chromosomes 8p, 16q, and 17p ( Figure 1A ). Copy number variation chromosome maps also validated the copy number variation profile of these regions ( 26 , 27 )( Figure 1B ). At the same time, we found that some chromosomes showed the same variation trend on the entire chromosome, such as the overall copy number amplification of chromosome 3, the overall copy number loss of chromosome 4, but other chromosomes such as chromosomes 1, 8 and 16 showed the opposite trend of copy number variation at arm-level ( Figure 1B ).…”

Section: Resultssupporting

confidence: 54%

Identifying enhancer-driven subtype-specific prognostic markers in breast cancer based on multi-omics data

Zhao

Zhang²,

Yin³

et al. 2022

Front. Immunol.

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Breast cancer is a cancer of high complexity and heterogeneity, with differences in prognosis and survival among patients of different subtypes. Copy number variations (CNVs) within enhancers are crucial drivers of tumorigenesis by influencing expression of their targets. In this study, we performed an integrative approach to identify CNA-driven enhancers and their effect on expression of target genes in four breast cancer subtypes by integrating expression data, copy number data and H3K27ac data. We identified 672, 555, 531, 361 CNA-driven enhancer-gene pairs and 280, 189, 113 and 98 CNA-driven enhancer-lncRNA pairs in the Basal-like, Her2, LumA and LumB subtypes, respectively. We then reconstructed a CNV-driven enhancer-lncRNA-mRNA regulatory network in each subtype. Functional analysis showed CNA-driven enhancers play an important role in the progression of breast cancer subtypes by influencing P53 signaling pathway, PPAR signaling pathway, systemic lupus erythematosus and MAPK signaling pathway in the Basal-like, Her2, LumA and LumB subtypes, respectively. We characterized the potentially prognostic value of target genes of CNV-driven enhancer and lncRNA-mRNA pairs in the subtype-specific network. We identified MUM1 and AC016876.1 as prognostic biomarkers in LumA and Basal-like subtypes, respectively. Higher expression of MUM1 with an amplified enhancer exhibited poorer prognosis in LumA patients. Lower expression of AC016876.1 with a deleted enhancer exhibited poorer survival outcomes of Basal-like patients. We also identified enhancer-related lncRNA-mRNA pairs as prognostic biomarkers, including AC012313.2-MUM1 in the LumA, AC026471.4-PLK5 in the LumB, AC027307.2-OAZ1 in the Basal-like and AC022431.1-HCN2 in the Her2 subtypes. Finally, our results highlighted target genes of CNA-driven enhancers and enhancer-related lncRNA-mRNA pairs could act as prognostic markers and potential therapeutic targets in breast cancer subtypes.

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Section: Resultssupporting

confidence: 54%

Identifying enhancer-driven subtype-specific prognostic markers in breast cancer based on multi-omics data

Zhao

Zhang²,

Yin³

et al. 2022

Front. Immunol.

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“…Immunostaining for Ki‐67 was performed. The immunostaining method followed our previous report 31 . The average Ki‐67 positivity (%) was calculated from the number of Ki‐67 positive and Ki‐67 negative cells counted using BZ‐X800 Analyzer software (Keyence) from six independent rectangular regions (400× field of view) of each xenograft tumor.…”

Section: Methodsmentioning

confidence: 99%

“…The immunostaining method followed our previous report. 31 The average Ki‐67 positivity (%) was calculated from the number of Ki‐67 positive and Ki‐67 negative cells counted using BZ‐X800 Analyzer software (Keyence) from six independent rectangular regions (400× field of view) of each xenograft tumor. Figure S2 shows Ki‐67 staining images of H1650 (cell line with the highest Ki‐67 positivity in the BE phenotype), HCC4006 (cell line with the lowest Ki‐67 positivity in the BE phenotype), H460 (cell line with the highest Ki‐67 positivity in the non‐BE phenotype), and H1651 (cell line with the lowest Ki‐67 positivity in the BE phenotype).…”

Section: Methodsmentioning

confidence: 99%

Genetic and phenotypic determinants of morphologies in 3D cultures and xenografts of lung tumor cell lines

Matsubara

Yoshimoto

Akolekar³

et al. 2023

Cancer Science

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We previously proposed the classification of lung adenocarcinoma into two groups: the bronchial epithelial phenotype (BE phenotype) with high‐level expressions of bronchial epithelial markers and actionable genetic abnormalities of tyrosine kinase receptors and the non‐BE phenotype with low‐level expressions of bronchial Bronchial epithelial (BE) epithelial markers and no actionable genetic abnormalities of tyrosine kinase receptors. Here, we performed a comprehensive analysis of tumor morphologies in 3D cultures and xenografts across a panel of lung cancer cell lines. First, we demonstrated that 40 lung cancer cell lines (23 BE and 17 non‐BE) can be classified into three groups based on morphologies in 3D cultures on Matrigel: round (n = 31), stellate (n = 5), and grape‐like (n = 4). The latter two morphologies were significantly frequent in the non‐BE phenotype (1/23 BE, 8/17 non‐BE, p = 0.0014), and the stellate morphology was only found in the non‐BE phenotype. SMARCA4 mutations were significantly frequent in stellate‐shaped cells (4/4 stellate, 4/34 non‐stellate, p = 0.0001). Next, from the 40 cell lines, we successfully established 28 xenograft tumors (18 BE and 10 non‐BE) in NOD/SCID mice and classified histological patterns of the xenograft tumors into three groups: solid (n = 20), small nests in desmoplasia (n = 4), and acinar/papillary (n = 4). The latter two patterns were characteristically found in the BE phenotype. The non‐BE phenotype exhibited a solid pattern with significantly less content of alpha‐SMA‐positive fibroblasts (p = 0.0004) and collagen (p = 0.0006) than the BE phenotype. Thus, the morphology of the tumors in 3D cultures and xenografts, including stroma genesis, reflects the intrinsic properties of the cancer cell lines. Furthermore, this study serves as an excellent resource for lung adenocarcinoma cell lines, with clinically relevant information on molecular and morphological characteristics and drug sensitivity.

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“…The APOBEC3B gene, which is located on Chromosome 22q13.1, regulates breast cancer cell development by promoting ER transcriptional activity (Cescon et al 2015). APOBEC3B copy number gain is associated with poor survival in patients with ER + breast cancer (Periyasamy et al 2015;Murakami et al 2021). A summary of each detected event along with the CNV state (1 = two copy losses, 2 = one copy loss, 3 = neutral, 4 = one copy gain, 5 = two copy gains, 6 = three or more copy gains) is given in Supplemental Tables S8 and S9, with source data identities.…”

Section: Expression-based Cnv Inferencementioning

confidence: 99%

Marker-free characterization of full-length transcriptomes of single live circulating tumor cells

et al. 2022

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The identification and characterization of circulating tumor cells (CTCs) are important for gaining insights into the biology of metastatic cancers, monitoring disease progression, and medical management of the disease. The limiting factor in the enrichment of purified CTC populations is their sparse availability, heterogeneity, and altered phenotypes relative to the primary tumor. Intensive research both at the technical and molecular fronts led to the development of assays that ease CTC detection and identification from peripheral blood. Most CTC detection methods based on single-cell RNA sequencing (scRNA-seq) use a mix of size selection, marker-based white blood cells (WBC) depletion, and antibodies targeting tumor-associated antigens. However, the majority of these methods either miss atypical CTCs or suffer from WBC contamination. We present unCTC, an R package for unbiased identification and characterization of CTCs from single-cell transcriptomic data. unCTC features many standard and novel computational and statistical modules for various analyses. These include a novel method of scRNA-seq clustering, named Deep Dictionary Learning usingk-means clustering cost (DDLK), expression-based copy number variation (CNV) inference, and combinatorial, marker-based verification of the malignant phenotypes. DDLK enables robust segregation of CTCs and WBCs in the pathway space, as opposed to the gene expression space. We validated the utility of unCTC on scRNA-seq profiles of breast CTCs from six patients, captured and profiled using an integrated ClearCell FX and Polaris workflow that works by the principles of size-based separation of CTCs and marker-based WBC depletion.

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Short somatic alterations at the site of copy number variation in breast cancer

Abstract: This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Cited by 8 publications

References 44 publications

Identifying enhancer-driven subtype-specific prognostic markers in breast cancer based on multi-omics data

Identifying enhancer-driven subtype-specific prognostic markers in breast cancer based on multi-omics data

Genetic and phenotypic determinants of morphologies in 3D cultures and xenografts of lung tumor cell lines

Marker-free characterization of full-length transcriptomes of single live circulating tumor cells

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