Literature DB >> 31704731

Genomic Characteristics of Triple-Negative Breast Cancer Nominate Molecular Subtypes That Predict Chemotherapy Response.

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.

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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


  4 in total

1.  Organoid biobanks as a new tool for pre-clinical validation of candidate drug efficacy and safety.

Authors:  Gerardo Botti; Maurizio Di Bonito; Monica Cantile
Journal:  Int J Physiol Pathophysiol Pharmacol       Date:  2021-02-15

2.  Molecular Analysis of Luminal Androgen Receptor Reveals Activated Pathways and Potential Therapeutic Targets in Breast Cancer.

Authors:  Stefania Stella; Silvia Rita Vitale; Michele Massimino; Gianmarco Motta; Claudio Longhitano; Katia Lanzafame; Federica Martorana; Carmine Fazzari; Giada Maria Vecchio; Elena Tirrò; Nicola Inzerilli; Rosaria Carciotto; Livia Manzella; Michele Caruso; Paolo Vigneri
Journal:  Cancer Genomics Proteomics       Date:  2022 Jul-Aug       Impact factor: 3.395

3.  An Immune Model to Predict Prognosis of Breast Cancer Patients Receiving Neoadjuvant Chemotherapy Based on Support Vector Machine.

Authors:  Mozhi Wang; Zhiyuan Pang; Yusong Wang; Mingke Cui; Litong Yao; Shuang Li; Mengshen Wang; Yanfu Zheng; Xiangyu Sun; Haoran Dong; Qiang Zhang; Yingying Xu
Journal:  Front Oncol       Date:  2021-04-27       Impact factor: 6.244

Review 4.  Organoid of ovarian cancer: genomic analysis and drug screening.

Authors:  H-D Liu; B-R Xia; M-Z Jin; G Lou
Journal:  Clin Transl Oncol       Date:  2020-01-14       Impact factor: 3.405

  4 in total

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