Multi-omics profiling of younger Asian breast cancers reveals distinctive molecular signatures

Kan, Zhengyan; Ding, Ying; Kim, Jin-Ho; Jung, Hae Hyun; Chung, Wingyan; Lal, Samir; Cho, Soonweng; Fernandez-Banet, Julio; Lee, Se Kyung; Kim, Seok Won; Lee, Jeong Eon; Choi, Yoon‐La; Deng, Shibing; Ahn, Jin Seok; Sha, Ying; Mu, Xinmeng Jasmine; Nam, Jae‐Yong; Im, Young‐Hyuck; Lee, Soo Hyun; Park, Woong-Yang; Nam, Seok Jin; Park, Yeon Hee

doi:10.1038/s41467-018-04129-4

Cited by 148 publications

(174 citation statements)

References 59 publications

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“…We analyzed two available, independent datasets to replicate our findings: 564 luminal patients in TCGA 3 and 112 luminal patients in a Korean BC genomic study (KBC) 44 . We analyzed TCGA Asians (n=29, mean age: 51 years), African Americans (AA, n=72, mean age: 58 years), and European ancestry (EA, n=463, mean age: 60 years) separately.…”

Section: Replication Datasetsmentioning

confidence: 71%

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Immune gene expression profiling reveals heterogeneity in luminal breast tumors

Zhu

Tse

Wang

et al. 2019

Preprint

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Disease heterogeneity of immune gene expression patterns of luminal breast cancer (BC) has not been well studied. We performed immune gene expression profiling of tumor and adjacent normal tissue in 92 Asian luminal BC patients and identified three distinct immune subtypes. Tumors in one subtype exhibited signs of T-cell activation, lower ESR1/ESR2 expression ratio and higher expression of immune checkpoint genes, nonsynonymous mutation burden, APOBEC-signature mutations, and increasing body mass index compared to other luminal tumors. Tumors in a second subtype were characterized by increased expression of interferonstimulated genes and enrichment for TP53 somatic mutations. The presence of three immune subtypes within luminal BC was replicated in cases drawn from The Cancer Genome Atlas and a Korean breast cancer study. Our findings suggest that immune gene expression and associated genomic features could be useful to further stratify luminal BC beyond the current luminal A/B classification.Introduction:

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Section: Replication Datasetsmentioning

confidence: 71%

“…KBC patients were much younger, with a mean age at diagnosis of 40 years. PAM50 subtype and mutation calling for KBC were previously detailed 44 . Immune classification and composition across all datasets (HKBC, TCGA, and KBC) were analyzed using the same methods.…”

Section: Replication Datasetsmentioning

confidence: 99%

Immune gene expression profiling reveals heterogeneity in luminal breast tumors

Zhu

Tse

Wang

et al. 2019

Preprint

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“…In recent years, detailed information regarding prognosis evaluation for cancer patients can be effectively provided by genome‐wide expression profiling detection . Numerous studies have evaluated the prognostic roles of array‐based gene expression signatures acquired from tumours .…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, detailed information regarding prognosis evaluation for cancer patients can be effectively provided by genome-wide expression profiling detection. [14][15][16][17] Numerous studies have evaluated the prognostic roles of array-based gene expression signatures acquired from tumours. 14,18,19 Several gene signatures have also been established to distinguish the prognosis of patients beyond the BC clinicopathologic features; however, most of them are not used clinically.…”

mentioning

confidence: 99%

Identification of a 4‐mRNA metastasis‐related prognostic signature for patients with breast cancer

Xie

Wang

Shi

et al. 2018

J Cellular Molecular Medi

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Metastasis‐related mRNAs have showed great promise as prognostic biomarkers in various types of cancers. Therefore, we attempted to develop a metastasis‐associated gene signature to enhance prognostic prediction of breast cancer (BC) based on gene expression profiling. We firstly screened and identified 56 differentially expressed mRNAs by analysing BC tumour tissues with and without metastasis in the discovery cohort (GSE102484, n = 683). We then found 26 of these differentially expressed genes were associated with metastasis‐free survival (MFS) in the training set (GSE20685, n = 319). A metastasis‐associated gene signature built using a LASSO Cox regression model, which consisted of four mRNAs, can classify patients into high‐ and low‐risk groups in the training cohort. Patients with high‐risk scores in the training cohort had shorter MFS (hazard ratio [HR] 3.89, 95% CI 2.53‐5.98; P < 0.001), disease‐free survival (DFS) (HR 4.69, 2.93‐7.50; P < 0.001) and overall survival (HR 4.06, 2.56‐6.45; P < 0.001) than patients with low‐risk scores. The prognostic accuracy of mRNAs signature was validated in the two independent validation cohorts (GSE21653, n = 248; GSE31448, n = 246). We then developed a nomogram based on the mRNAs signature and clinical‐related risk factors (T stage and N stage) that predicted an individual's risk of disease, which can be assessed by calibration curves. Our study demonstrated that this 4‐mRNA signature might be a reliable and useful prognostic tool for DFS evaluation and will facilitate tailored therapy for BC patients at different risk of disease.

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“…Pan-omics or multi-omics analysis in breast cancer has revealed significant differences in molecular subtype distribution [3]. Genomics and transcriptomics analysis of breast cancer data of Korean and Caucasian cohorts showed underlying molecular differences, which are responsible for the occurrence of breast cancer at the younger age in the Asian population compared to the western population [3].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Pan-Omics Data in Human Interactome Network (APODHIN)

Biswas

Kumar

Bose

et al. 2020

Preprint

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Analysis of Pan-Omics Data in Human Interactome Network (APODHIN) is a platform for integrative analysis of transcriptomics, proteomics, genomics, and metabolomics data for identification of key molecular players and their interconnections exemplified in cancer scenario. APODHIN works on a meta-interactome networks consisting of human proteinprotein interactions, miRNA-target gene regulatory interactions, and transcription factortarget gene regulatory relationships, respectively. In its first module, APODHIN maps proteins/genes/miRNAs from different omics data in its meta-interactome network and extracts the network of biomolecules that are differentially altered in the given scenario.Using this context specific, filtered interaction network, APODHIN identifies topologically important nodes (TINs) implementing graph theory based network topology analysis and further justifies their role via pathway and disease marker mapping. These TINs could be used as prospective diagnostic and/or prognostic biomarkers and/or potential therapeutic targets.In its second module, APODHIN attempts to identify cross pathway regulatory and proteinprotein interaction (PPI) links connecting signaling proteins, transcription factors, and miRNAs to metabolic enzymes via utilization of single-omics and/or pan-omics data and implementation of mathematical modeling. Interconnections between regulatory components such as signaling proteins/TFs/miRNAs and metabolic pathways need to be elucidated more elaborately in order to understand the role of oncogene and tumor suppressors in regulation of metabolic reprogramming during cancer. APODHIN platform contains a web server component where users can upload single/multi omics data to identify TINs and cross-pathway links. Tabular, graphical and 3D network representations of the identified TINs and cross-pathway links are provided for better appreciation. Additionally, this platform also provides a database part where cancer specific, single and/or multi omics dataset centric meta-interactome networks, TINs, and crosspathway links are provided for cervical, ovarian, and breast cancers, respectively. APODHIN platform is freely available at http://www.hpppi.iicb.res.in/APODHIN/home.html.

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Multi-omics profiling of younger Asian breast cancers reveals distinctive molecular signatures

Cited by 148 publications

References 59 publications

Immune gene expression profiling reveals heterogeneity in luminal breast tumors

Immune gene expression profiling reveals heterogeneity in luminal breast tumors

Identification of a 4‐mRNA metastasis‐related prognostic signature for patients with breast cancer

Analysis of Pan-Omics Data in Human Interactome Network (APODHIN)

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