| Literature DB >> 31704731 |
Jihyun Kim1, Doyeong Yu1, Youngmee Kwon2, Keun Seok Lee2, Sung Hoon Sim2,3, Sun-Young Kong3,4, Eun Sook Lee2,4, In Hae Park5,3, Charny Park6.
Abstract
The heterogeneity of triple-negative breast cancer (TNBC) poses difficulties for suitable treatment and leads to poor outcome. This study aimed to define a consensus molecular subtype (CMS) of TNBC and thus elucidate genomic characteristics and relevant therapy. We integrated the expression profiles of 957 TNBC samples from published datasets. We identified genomic characteristics of subtype by exploring the pathway activity, microenvironment, and clinical relevance. In addition, drug response (DR) scores (n = 181) were computationally investigated using chemical perturbation gene signatures and validated in our own patient with TNBC (n = 38) who received chemotherapy and organoid biobank data (n = 64). Subsequently, cooperative functions with drugs were also explored. Finally, we classified TNBC into four CMSs: stem-like; mesenchymal-like; immunomodulatory; luminal-androgen receptor. CMSs also elucidated distinct tumor-associated microenvironment and pathway activities. Furthermore, we discovered metastasis-promoting genes, such as secreted phosphoprotein 1 by comparing with primary. Computational DR scores associated with CMS revealed drug candidates (n = 18), and it was successfully evaluated in cisplatin response of both patients and organoids. Our CMS recapitulated in-depth functional and cellular heterogeneity encompassing primary and metastatic TNBC. We suggest DR scores to predict CMS-specific DRs and to be successfully validated. Finally, our approach systemically proposes a relevant therapeutic prediction model as well as prognostic markers for TNBC. IMPLICATIONS: We delineated the genomic characteristic and computational DR prediction for TNBC CMS from gene expression profile. Our systematic approach provides diagnostic markers for subtype and metastasis verified by machine-learning and novel therapeutic candidates for patients with TNBC. ©2019 American Association for Cancer Research.Entities:
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Year: 2019 PMID: 31704731 DOI: 10.1158/1541-7786.MCR-19-0453
Source DB: PubMed Journal: Mol Cancer Res ISSN: 1541-7786 Impact factor: 5.852