Evaluation of deacetylase inhibition in metaplastic breast carcinoma using multiple derivations of preclinical models of a new patient-derived tumor

Abstract

Metaplastic breast carcinoma (MBC) is a clinically aggressive and rare subtype of breast cancer, with similar features to basal-like breast cancers. Due to rapid growth rates and characteristic heterogeneity, MBC is often unresponsive to standard chemotherapies; and novel targeted therapeutic discovery is urgently needed. Histone deacetylase inhibitors (DACi) suppress tumor growth and metastasis through regulation of the epithelial-to-mesenchymal transition axis in various cancers, including basal-like breast cancers. We utilized a new MBC patient-derived xenograft (PDX) to examine the effect of DACi therapy on MBC. Cell morphology, cell cycle-associated gene expressions, transwell migration, and metastasis were evaluated in patient-derived cells and tumors after treatment with romidepsin and panobinostat. Derivations of our PDX model, including cells, spheres, organoids, explants, and in vivo implanted tumors were treated. Finally, we tested the effects of combining DACi with approved chemotherapeutics on relative cell biomass. DACi significantly suppressed the total number of lung metastasis in vivo using our PDX model, suggesting a role for DACi in preventing circulating tumor cells from seeding distal tissue sites. These data were supported by our findings that DACi reduced cell migration, populations, and expression of mesenchymal-associated genes. While DACi treatment did affect cell cycle-regulating genes in vitro, tumor growth was not affected compared to controls. Importantly, gene expression results varied depending on the cellular or tumor system used, emphasizing the importance of using multiple derivations of cancer models in preclinical therapeutic discovery research. Furthermore, DACi sensitized and produced a synergistic effect with approved oncology PLOS ONE |

Quantifying Breast Cancer-Driven Fiber Alignment and Collagen Deposition in Primary Human Breast Tissue

“…The metaplastic breast tumor model, known as TU-BcX-4IC, was obtained from a mastectomy of a 57-year-old white female with metaplastic breast carcinoma unresponsive to neoadjuvant adriamycin/cyclophosphamide therapy as previously described ( Chang et al, 2020 ). The tumor was obtained from the surgical specimen just after mastectomy.…”

Aspects of the Epigenetic Regulation of EMT Related to Cancer Metastasis

“…The first-generation of epidrugs included DNA methyltransferase inhibitors (the nucleoside analogs of DNMTi: 5-azacytidine, AZA [ 81 ], Vidaza ® ; 5-aza-2′-deoxycytidine, decytabine [ 82 ], Dacogen ® ), and histone deacetylase inhibitors (HDACi, the hydroxamic acids: suberoylanilide hydroxamic acid, SAHA, vorinostat [ 83 ], Zolinza ® ; trichostatin A, TSA [ 84 ]; cyclic peptides: trapoxin A, FK228, romidepsin [ 85 ], Istodax ® ) with a low degree of selectivity, and showed undesirable pharmacokinetic properties, poor target selectivity, and low bioavailability, were more active within non-physiological pH ranges, and are targets of cellular deaminases, which ultimately mean a short half-life for these compounds. The second-generation of epidrugs have improved physiological properties, including DNA methyltransferase inhibitors (the nucleoside analogs of DNMTi: zebularine; guadecitabine, S110 or SGI-110 [ 79 ], and non-nucleoside analogs: hydralazine, procainamide, RG108), and histone deacetylase inhibitors (HDACi: hydroxamic acid: belinostat, Beleodaq ® ; dacinostat; panobinostat [ 85 ], Farydak ® ; quisinostat [ 86 ]; benzamides: etinostat; mocetinostat [ 81 ]; chidamide, Tucidinostat, Epidaza ® ; carboxylic acids: valproic acid [ 87 ]). The mechanism of interaction of the third generation of epidrugs corresponds to the assumption that epigenetic factors could write, delete, or read epigenetic marks in the form of protein complexes, which is essential for the design of highly selective epidrugs.…”

Evaluation of liver kinase B1 downstream signaling expression in various breast cancers and relapse free survival after systemic chemotherapy treatment

“…In addition to cell lines, we have immunoblotting data for characterized PDX models. For example, TU-BcX-4IC's would be an ideal model for studying the effects of low LKB1 activity in a more aggressive and drug-resistant disease (Supplementary Figure 3) [9]. In conclusion, these data demonstrate LKB1 and its downstream targets are differently correlated with patient survival, depending on subtype and chemotherapy exposure.…”

“…In addition to cell lines, we have immunoblotting data for characterized PDX models. For example, TU-BcX-4IC’s would be an ideal model for studying the effects of low LKB1 activity in a more aggressive and drug-resistant disease (Supplementary Figure 3) [ 9 ].…”