Phenotypic modifications in ovarian cancer stem cells following Paclitaxel treatment

• Published in: Cancer medicine (Malden, MA) Vol. 2; no. 6; pp. 751 - 762
• Main Authors: Craveiro, Vinicius, Yang‐Hartwich, Yang, Holmberg, Jennie C., Sumi, Natalia J., Pizzonia, John, Griffin, Brian, Gill, Sabrina K., Silasi, Dan‐Arin, Azodi, Masoud, Rutherford, Thomas, Alvero, Ayesha B., Mor, Gil
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01-12-2013; Blackwell Publishing Ltd
• Subjects: Animals; Antineoplastic Agents, Phytogenic - pharmacology; Antineoplastic Agents, Phytogenic - therapeutic use; Cancer Biology; Cancer therapies; Carcinoma, Ovarian Epithelial; CD44 antigen; Chemoresistance; Chemotherapy; Drug Resistance, Neoplasm; EMT; Female; Genotype & phenotype; HEK293 Cells; Humans; Hyaluronan Receptors - genetics; Hyaluronan Receptors - metabolism; Malignancy; Metastases; Metastasis; Mice; Mice, Nude; Morphology; Mortality; MyD88 protein; Myeloid Differentiation Factor 88 - genetics; Myeloid Differentiation Factor 88 - metabolism; Neoplasms, Glandular and Epithelial - drug therapy; Neoplasms, Glandular and Epithelial - metabolism; Neoplasms, Glandular and Epithelial - pathology; Neoplastic Stem Cells - metabolism; Ovarian cancer; ovarian cancer stem cells; Ovarian Neoplasms - drug therapy; Ovarian Neoplasms - metabolism; Ovarian Neoplasms - pathology; Paclitaxel; Paclitaxel - pharmacology; Paclitaxel - therapeutic use; Patients; Phenotype; Phenotypes; Phenotypic plasticity; Polymerase chain reaction; Recurrence; slug; Snail Family Transcription Factors; Stem cell transplantation; Stem cells; Transcription Factors - genetics; Transcription Factors - metabolism; Tumor Burden - drug effects; Tumors; Xenograft Model Antitumor Assays; recurrence; ovarian cancer stem cells; EMT; slug
• ISSN: 2045-7634; 2045-7634
• DOI: 10.1002/cam4.115

Epithelial ovarian cancer (EOC) is the most lethal gynecologic malignancy. Despite initial responsiveness, 80% of EOC patients recur and present with chemoresistant and a more aggressive disease. This suggests an underlying biology that results in a modified recurrent disease, which is distinct from the primary tumor. Unfortunately, the management of recurrent EOC is similar to primary disease and does not parallel the molecular changes that may have occurred during the process of rebuilding the tumor. We describe the characterization of unique in vitro and in vivo ovarian cancer models to study the process of recurrence. The in vitro model consists of GFP+/CD44+/MyD88+ EOC stem cells and mCherry+/CD44−/MyD88− EOC cells. The in vivo model consists of mCherry+/CD44+/MyD88+ EOC cells injected intraperitoneally. Animals received four doses of Paclitaxel and response to treatment was monitored by in vivo imaging. Phenotype of primary and recurrent disease was characterized by quantitative polymerase chain reaction (qPCR) and Western blot analysis. Using the in vivo and in vitro models, we confirmed that chemotherapy enriched for CD44+/MyD88+ EOC stem cells. However, we observed that the surviving CD44+/MyD88+ EOC stem cells acquire a more aggressive phenotype characterized by chemoresistance and migratory potential. Our results highlight the mechanisms that may explain the phenotypic heterogeneity of recurrent EOC and emphasize the significant plasticity of ovarian cancer stem cells. The significance of our findings is the possibility of developing new venues to target the surviving CD44+/MyD88+ EOC stem cells as part of maintenance therapy and therefore preventing recurrence and metastasis, which are the main causes of mortality in patients with ovarian cancer.
We demonstrate that putative ovarian cancer cells with tumor initiating capacity that survive chemotherapy acquire molecular phenotypic modifications, which makes them distinct from the original tumor‐initiating cells. The modifications that occur may not be the same in every patient. This suggests that treatment modalities should be modified to each individual patient. Further studies using our models will identify biomarkers for personalized treatment.