Breast Cancer Heterogeneity and Response to Novel Therapeutics

Baliu-Piqué, Mariona; Pandiella, Atanasio; Ocaña, Alberto

doi:10.3390/cancers12113271

Cited by 41 publications

(39 citation statements)

References 141 publications

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“…Breast cancer is a heterogeneous disease that differs greatly not only among patients, but also within each individual tumor, which may explain the variability regarding therapeutic responses and disease progression ( Coates et al, 2015 ; Baliu-Pique et al, 2020 ). 3D organotypic models represent powerful tools to replicate such heterogeneity, including tumor-stroma interactions, being central for comprehending cancer-related mechanisms and drug response.…”

Section: Introductionmentioning

confidence: 99%

Engineering a Vascularized 3D Hybrid System to Model Tumor-Stroma Interactions in Breast Cancer

Teixeira

Chaves

Torres

et al. 2021

Front. Bioeng. Biotechnol.

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The stromal microenvironment of breast tumors, namely the vasculature, has a key role in tumor development and metastatic spread. Tumor angiogenesis is a coordinated process, requiring the cooperation of cancer cells, stromal cells, such as fibroblasts and endothelial cells, secreted factors and the extracellular matrix (ECM). In vitro models capable of capturing such complex environment are still scarce, but are pivotal to improve success rates in drug development and screening. To address this challenge, we developed a hybrid alginate-based 3D system, combining hydrogel-embedded mammary epithelial cells (parenchymal compartment) with a porous scaffold co-seeded with fibroblasts and endothelial cells (vascularized stromal compartment). For the stromal compartment, we used porous alginate scaffolds produced by freeze-drying with particle leaching, a simple, low-cost and non-toxic approach that provided storable ready-to-use scaffolds fitting the wells of standard 96-well plates. Co-seeded endothelial cells and fibroblasts were able to adhere to the surface, spread and organize into tubular-like structures. For the parenchymal compartment, a designed alginate gel precursor solution load with mammary epithelial cells was added to the pores of pre-vascularized scaffolds, forming a hydrogel in situ by ionic crosslinking. The 3D hybrid system supports epithelial morphogenesis in organoids/tumoroids and endothelial tubulogenesis, allowing heterotypic cell-cell and cell-ECM interactions, while presenting excellent experimental tractability for whole-mount confocal microscopy, histology and mild cell recovery for down-stream analysis. It thus provides a unique 3D in vitro platform to dissect epithelial-stromal interactions and tumor angiogenesis, which may assist in the development of selective and more effective anticancer therapies.

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Section: Introductionmentioning

confidence: 99%

Engineering a Vascularized 3D Hybrid System to Model Tumor-Stroma Interactions in Breast Cancer

Teixeira

Chaves

Torres

et al. 2021

Front. Bioeng. Biotechnol.

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“…1 BC has profound genetic heterogeneity and tumor heterogeneity, which is closely associated with poor prognosis and survival, leading determinants of therapeutic resistance and treatment failure. 2,3 Therefore, an accurate assessment of tumor pharmacological heterogeneity is required for the development of effective therapies. A great number of studies have been done using cancer cell lines and patient-derived xenografts, which include different advantages.…”

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confidence: 99%

Optimizing individualized treatment strategy based on breast cancer organoid model

Pan

Zhao

et al. 2021

Clinical & Translational Med

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Pharmacological characteristics of PDTOs derived from different BC patients. (A) Drug dose-response curves of PDTOs from different molecular types of BC patients for Tamoxifen, Fulvestrant, Paclitaxel, Docetaxel, Cyclophosphamide, Epirubicin, Carboplatin, Palbociclib. Mean ± SD of results from three independent experiments is shown for each drug. (B) Drug dose-response curves of multifocal BC PDTOs for Tamoxifen, Fulvestrant, Paclitaxel, Docetaxel, Cyclophosphamide, Epirubicin, Carboplatin, and Palbociclib. Mean ± SD of results from three independent experiments is shown for each drug. (C) Drug dose-response curves of bilateral BC PDTOs for Tamoxifen, Fulvestrant, Paclitaxel, Docetaxel, Cyclophosphamide, Epirubicin, Carboplatin, and Palbociclib. Mean ± SD of results from three independent experiments is shown for each drug. (D) Drug dose-response curves of neoadjuvant BC PDTOs for Docetaxel, Cyclophosphamide, and Epirubicin. Mean ± SD of results from three independent experiments is shown for each drug. Bright-field microscopy images of neoadjuvant BC PDTOs were treated with indicated drugs. Scale bars, 200 μm. CTX: Cyclophosphamide. (E) Drug dose-response curves of neoadjuvant BC PDTOs for Tamoxifen, Fulvestrant, Paclitaxel, Palbociclib, and Carboplatin. Mean ± SD of results from three independent experiments is shown for each drug. Bright-field microscopy images of neoadjuvant BC PDTOs were treated with indicated drugs. Scale bars, 200 μm

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“…Multivariate regression analysis showed that high-risk lncRNA (such as AC026401.3) was highly expressed in low-risk patients, while low-risk lncRNA (such as AC078778.1) showed high expression in high-risk patients. Previous reports have shown that tumor cells show a high degree of heterogeneity ( Baliu-Piqué et al, 2020 ; Louault et al, 2020 ), and there may be individualized internal mechanisms or even opposite biological processes in different tumor stages. Therefore, we suspect that this inconsistent result may be related to the stage of tumor progression, and there would be different metabolic conditions between local and progressive tumor.…”

Section: Discussionmentioning

confidence: 99%

A Glycolysis-Based Long Non-coding RNA Signature Accurately Predicts Prognosis in Renal Carcinoma Patients

et al. 2021

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BackgroundThe prognosis of renal cell carcinoma (RCC) varies greatly among different risk groups, and the traditional indicators have limited effect in the identification of risk grade in patients with RCC. The purpose of our study is to explore a glycolysis-based long non-coding RNAs (lncRNAs) signature and verify its potential clinical significance in prognostic prediction of RCC patients.MethodsIn this study, RNA data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate cox regression displayed six significantly related lncRNAs (AC124854.1, AC078778.1, EMX2OS, DLGAP1-AS2, AC084876.1, and AC026401.3) which were utilized in construction of risk score by a formula. The accuracy of risk score was verified by a series of statistical methods such as receiver operating characteristic (ROC) curves, nomogram and Kaplan-Meier curves. Its potential clinical significance was excavated by gene enrichment analysis.ResultsKaplan-Meier curves and ROC curves showed reliability of the risk score to predict the prognosis of RCC patients. Stratification analysis indicated that the risk score was independent predictor compare to other traditional clinical parameters. The clinical nomogram showed highly rigorous with index of 0.73 and precisely predicted 1-, 3-, and 5-year survival time of RCC patients. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene set enrichment analysis (GSEA) depicted the top ten correlated pathways in both high-risk group and low-risk group. There are 6 lncRNAs and 25 related mRNAs including 36 lncRNA-mRNA links in lncRNA-mRNA co-expression network.ConclusionThis research demonstrated that glycolysis-based lncRNAs possessed an important value in survival prediction of RCC patients, which would be a potential target for future treatment.

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Breast Cancer Heterogeneity and Response to Novel Therapeutics

Cited by 41 publications

References 141 publications

Engineering a Vascularized 3D Hybrid System to Model Tumor-Stroma Interactions in Breast Cancer

Engineering a Vascularized 3D Hybrid System to Model Tumor-Stroma Interactions in Breast Cancer

Optimizing individualized treatment strategy based on breast cancer organoid model

A Glycolysis-Based Long Non-coding RNA Signature Accurately Predicts Prognosis in Renal Carcinoma Patients

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