Retrospective analysis of survival improvement by molecular biomarker-based personalized chemotherapy for recurrent ovarian cancer

[MiR-125b-5 suppresses ovarian cancer cell migration and invasion by targeted downregulation of CD147]

OBJECTIVE

To investigate whether miR-125b-5p regulates biological behaviors of ovarian cancer cells by targeted regulation of CD147 expression.

METHODS

RT-qPCR was used to detect the expression of miR-125b-5p and CD147 mRNA in ovarian cancer tissues and cancer cell lines. SKOV3 cells transfected with miR-125b-5p mimic and HO8910 cells transfected with miR-125b-5p inhibitor were examined for changes in proliferation, migration and invasion using CCK-8 assay, colonyforming assay and Transwell assay. Starbase was used to predict the potential binding sites between miR-125b-5p and CD147, and double luciferase reporter gene assay was used to verify the targeting relationship. In SKOV3 cells, the effects of cotransfection with miR-125b-5p mimic and pcDNA3.1-CD147 (or pcDNA3.1) plasmid on cell proliferation, migration and invasion were assessed with CCK-8 assay and Transwell assay.

RESULTS

The expression of miR-125b-5p was significantly lowered and that of CD147 was increased in both ovarian cancer tissues and ovarian cancer cell lines (P < 0.05). Overexpression of miR-125b-5p in SKOV3 cells resulted in significantly suppressed cell proliferation, migration and invasion, while downregulation of miR-125b-5p in HO8910 cells promoted cell proliferation, migration and invasion. Bioinformatic analysis predicted that miR-125b-5p binds to CD147, which was confirmed by luciferase reporter gene assay. RT-qPCR and Western blotting showed that miR-125b-5p negatively regulated CD147 expression (P < 0.05). In SKOV3 cells, the inhibitory effects of miR-125b-5p mimic on cell proliferation, invasion and migration were significantly attenuated by co-transfection of the cells with pcDNA3.1-CD147 plasmid.

CONCLUSION

miR-125b-5p inhibits the migration and invasion of ovarian cancer cells by negatively regulating the expression of CD147.

miRNA‑199b‑3p suppresses growth and progression of ovarian cancer via the CHK1/E‑cadherin/EMT signaling pathway by targeting ZEB1

Ovarian cancer is one of the most common gynecological malignancies and its pathogenesis and progression are regulated by multiple genes. MicroRNAs (miRNAs) are endogenous non‑coding RNAs that regulate body function by altering post‑transcriptional gene expression. Previous studies have suggested that miRNAs are closely associated with the pathogenesis and progression of several malignancies, including breast cancer, hepatocellular carcinoma and glioma, among others. Therefore, miRNAs are promising novel targets for the diagnosis, treatment and determination of prognostic factors in patients with ovarian cancer. In the present study, the role of miRNA‑133b‑3p in ovarian cancer progression and its possible mechanism of action were investigated. The results demonstrated that the expression of miRNA‑199b‑3p and zinc finger E‑box binding homeobox (ZEB)1 were increased in patients with ovarian cancer. The overall survival (OS) and disease‑free survival (DFS) of patients with ovarian cancer and high miRNA‑199b‑3p expression were prolonged compared with those of patients with low miRNA‑199b‑3p expression. Additionally, the OS and DFS of patients with ovarian cancer and low ZEB1 expression were longer compared with those of patients with high ZEB1 expression. Furthermore, miRNA‑199b‑3p overexpression reduced cell proliferation and promoted apoptosis in an in vitro model of ovarian cancer. miRNA‑199b‑3p overexpression also suppressed ZEB1 and checkpoint kinase 1 expression and induced E‑cadherin expression and epithelial‑to‑mesenchymal transition in this model. Furthermore, the effects of miRNA‑199b‑3p‑mediated apoptosis and migration were attenuated by ZEB1 and E‑cadherin, respectively. The results of the present study indicated that miRNA‑199b‑3p suppressed ovarian cancer progression by targeting ZEB1, which may represent a promising therapeutic target for ovarian cancer.

PDXGEM: patient-derived tumor xenograft-based gene expression model for predicting clinical response to anticancer therapy in cancer patients

Background

Cancer is a highly heterogeneous disease with varying responses to anti-cancer drugs. Although several attempts have been made to predict the anti-cancer therapeutic responses, there remains a great need to develop highly accurate prediction models of response to the anti-cancer drugs for clinical applications toward a personalized medicine. Patient derived xenografts (PDXs) are preclinical cancer models in which the tissue or cells from a patient’s tumor are implanted into an immunodeficient or humanized mouse. In the present study, we develop a bioinformatics analysis pipeline to build a predictive gene expression model (GEM) for cancer patients’ drug responses based on gene expression and drug activity data from PDX models.

Results

Drug sensitivity biomarkers were identified by performing an association analysis between gene expression levels and post-treatment tumor volume changes in PDX models. We built a drug response prediction model (called PDXGEM) in a random-forest algorithm by using a subset of the drug sensitvity biomarkers with concordant co-expression patterns between the PDXs and pretreatment cancer patient tumors. We applied the PDXGEM to several cytotoxic chemotherapies as well as targeted therapy agents that are used to treat breast cancer, pancreatic cancer, colorectal cancer, or non-small cell lung cancer. Significantly accurate predictions of PDXGEM for pathological response or survival outcomes were observed in extensive independent validations on multiple cancer patient datasets obtained from retrospective observational studies and prospective clinical trials.

Conclusion

Our results demonstrated the strong potential of using molecular profiles and drug activity data of PDX tumors in developing a clinically translatable predictive cancer biomarkers for cancer patients. The PDXGEM web application is publicly available at http://pdxgem.moffitt.org.

Single drug biomarker prediction for ER- breast cancer outcome from chemotherapy

ER-negative breast cancer includes most aggressive subtypes of breast cancer such as triple negative (TN) breast cancer. Excluded from hormonal and targeted therapies effectively used for other subtypes of breast cancer, standard chemotherapy is one of the primary treatment options for these patients. However, as ER- patients have shown highly heterogeneous responses to different chemotherapies, it has been difficult to select most beneficial chemotherapy treatments for them. In this study, we have simultaneously developed single drug biomarker models for four standard chemotherapy agents: paclitaxel (T), 5-fluorouracil (F), doxorubicin (A) and cyclophosphamide (C) to predict responses and survival of ER- breast cancer patients treated with combination chemotherapies. We then flexibly combined these individual drug biomarkers for predicting patient outcomes of two independent cohorts of ER- breast cancer patients who were treated with different drug combinations of neoadjuvant chemotherapy. These individual and combined drug biomarker models significantly predicted chemotherapy response for 197 ER- patients in the Hatzis cohort (AUC = 0.637, P = 0.002) and 69 ER- patients in the Hess cohort (AUC = 0.635, P = 0.056). The prediction was also significant for the TN subgroup of both cohorts (AUC = 0.60, 0.72, P = 0.043, 0.009). In survival analysis, our predicted responder patients showed significantly improved survival with a >17 months longer median PFS than the predicted non-responder patients for both ER- and TN subgroups (log-rank test P-value = 0.018 and 0.044). This flexible prediction capability based on single drug biomarkers may allow us to even select new drug combinations most beneficial to individual patients with ER- breast cancer.

CONCORD biomarker prediction for novel drug introduction to different cancer types

Many cancer therapeutic agents have shown to be effective for treating multiple cancer types. Yet major challenges exist toward introducing a novel drug used in one cancer type to different cancer types, especially when a relatively small number of patients with the other cancer type often benefit from anti-cancer therapy with the drug. Recently, many novel agents were introduced to different cancer types together with companion biomarkers which were obtained or biologically assumed from the original cancer type. However, there is no guarantee that biomarkers from one cancer can directly predict a therapeutic response in another. To tackle this challenging question, we have developed a concordant expression biomarker-based technique ("CONCORD") that overcomes these limitations. CONCORD predicts drug responses from one cancer type to another by identifying concordantly co-expressed biomarkers across different cancer systems. Application of CONCORD to three standard chemotherapeutic agents and two targeted agents demonstrated its ability to accurately predict the effectiveness of a drug against new cancer types and predict therapeutic response in patients.

Bioinformatory-assisted analysis of next-generation sequencing data for precision medicine in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is a tumor with an extremely poor prognosis, predominantly as a result of chemotherapy resistance and numerous somatic mutations. Consequently, PDAC is a prime candidate for the use of sequencing to identify causative mutations, facilitating subsequent administration of targeted therapy. In a feasibility study, we retrospectively assessed the therapeutic recommendations of a novel, evidence-based software that analyzes next-generation sequencing (NGS) data using a large panel of pharmacogenomic biomarkers for efficacy and toxicity. Tissue from 14 patients with PDAC was sequenced using NGS with a 620 gene panel. FASTQ files were fed into treatmentmap. The results were compared with chemotherapy in the patients, including all side effects. No changes in therapy were made. Known driver mutations for PDAC were confirmed (e.g. KRAS, TP53). Software analysis revealed positive biomarkers for predicted effective and ineffective treatments in all patients. At least one biomarker associated with increased toxicity could be detected in all patients. Patients had been receiving one of the currently approved chemotherapy agents. In two patients, toxicity could have been correctly predicted by the software analysis. The results suggest that NGS, in combination with an evidence-based software, could be conducted within a 2-week period, thus being feasible for clinical routine. Therapy recommendations were principally off-label use. Based on the predominant KRAS mutations, other drugs were predicted to be ineffective. The pharmacogenomic biomarkers indicative of increased toxicity could be retrospectively linked to reported negative side effects in the respective patients. Finally, the occurrence of somatic and germline mutations in cancer syndrome-associated genes is noteworthy, despite a high frequency of these particular variants in the background population. These results suggest software-analysis of NGS data provides evidence-based information on effective, ineffective and toxic drugs, potentially forming the basis for precision cancer medicine in PDAC.

Drug Selection in the Genomic Age: Application of the Coexpression Extrapolation Principle for Drug Repositioning in Cancer Therapy

The use of patient-specific data in drug and dose selection is becoming an increasingly important component in cancer therapy. Basing drug choice on molecular aspects of the tumor is consistent with the identification of cancer as a molecular disease or diseases, even within the same histological type, and its treatment specific to the background from which it arose and exists. Multiple examples exist of single-gene mutations, and over- or underexpression that convey either sensitivity or resistance to a given agent. What about the picture that global gene expression in a cancer presents regarding drug sensitivity or resistance? Coexpression extrapolation (COXEN) is a methodology that acts as a Rosetta stone between patterns of gene expression that correlate to drug responses in vitro and those in tumors of untreated patients to predict chemosensitivity in such tumors even to drugs that are not specifically indicated for that histotype. Further applications of COXEN in drug discovery allow for in silico screens correlating drug response and gene expression in a genetically diverse cell panel to gene expression patterns in a target tumor with the potential for identifying and repurposing compounds. Here we discuss how COXEN is being developed and tested for application in drug selection, repositioning, and repurposing in oncology.