Tumor microRNA expression patterns associated with resistance to platinum based chemotherapy and survival in ovarian cancer patients.

BACKGROUND: Ovarian cancer, the leading cause of gynecologic cancer deaths, is usually diagnosed in advanced stages. Prognosis relates to stage at diagnosis and sensitivity to platinum based chemotherapy. We aimed to assess the expression of microRNAs in ovarian tumors and identify microRNA expression patterns that are associated with outcome, response to chemotherapy and survival. METHODS: Patients, who were surgically treated for ovarian cancer between January 2000 and December 2004 were identified. Patient charts were reviewed for clinicopathologic information, follow-up and survival. Total RNA was extracted from tumor samples and microRNA expression levels were measured by microarrays. Expression levels were compared between groups of samples and statistically analyzed. RESULTS: Fifty-seven patients were identified to fit study criteria. Of them, 19 patients had stage I disease at diagnosis, and 38 patients, stage III. All patients received platinum based chemotherapy as first line treatment. 18 microRNAs were differentially expressed (p<0.05) between stage I and stage III disease. Seven microRNAs were found to be significantly differentially expressed in tumors from platinum-sensitive vs. platinum-resistant patients (p<0.05). Five microRNAs were associated with significant differences (p<0.05) in survival or recurrence-free survival. High expression of hsa-mir-27a identified a sub-group of patients with very poor prognosis. CONCLUSIONS: We have found an array of tumor specific markers that are associated with response to platinum based first line chemotherapy. Expression of some of these miRNAs also correlated closely with prognosis. This approach can potentially be used to tailor chemotherapy and further management to specific patient needs.

MicroRNAs as a potential prognostic factor in gastric cancer.

AIM: To compare the microRNA (miR) profiles in the primary tumor of patients with recurrent and non-recurrent gastric cancer. METHODS: The study group included 45 patients who underwent curative gastrectomies from 1995 to 2005 without adjuvant or neoadjuvant therapy and for whom adequate tumor content was available. Total RNA was extracted from formalin-fixed paraffin-embedded tumor samples, preserving the small RNA fraction. Initial profiling using miR microarrays was performed to identify potential biomarkers of recurrence after resection. The expression of the differential miRs was later verified by quantitative real-time polymerase chain reaction (qRT-PCR). Findings were compared between patients who had a recurrence within 36 mo of surgery (bad-prognosis group, n = 14, 31%) and those who did not (good-prognosis group, n = 31, 69%). RESULTS: Three miRs, miR-451, miR-199a-3p and miR-195 were found to be differentially expressed in tumors from patients with good prognosis vs patients with bad prognosis (P < 0.0002, 0.0027 and 0.0046 respectively). High expression of each miR was associated with poorer prognosis for both recurrence and survival. Using miR-451, the positive predictive value for non-recurrence was 100% (13/13). The expression of the differential miRs was verified by qRT-PCR, showing high correlation to the microarray data and similar separation into prognosis groups. CONCLUSION: This study identified three miRs, miR-451, miR-199a-3p and miR-195 to be predictive of recurrence of gastric cancer. Of these, miR-451 had the strongest prognostic impact.

Accurate molecular classification of renal tumors using microRNA expression.

Subtypes of renal tumors have different genetic backgrounds, prognoses, and responses to surgical and medical treatment, and their differential diagnosis is a frequent challenge for pathologists. New biomarkers can help improve the diagnosis and hence the management of renal cancer patients. We extracted RNA from 71 formalin-fixed paraffin-embedded (FFPE) renal tumor samples and measured expression of more than 900 microRNAs using custom microarrays. Clustering revealed similarity in microRNA expression between oncocytoma and chromophobe subtypes as well as between conventional (clear-cell) and papillary tumors. By basing a classification algorithm on this structure, we followed inherent biological correlations and could achieve accurate classification using few microRNAs markers. We defined a two-step decision-tree classifier that uses expression levels of six microRNAs: the first step uses expression levels of hsa-miR-210 and hsa-miR-221 to distinguish between the two pairs of subtypes; the second step uses either hsa-miR-200c with hsa-miR-139-5p to identify oncocytoma from chromophobe, or hsa-miR-31 with hsa-miR-126 to identify conventional from papillary tumors. The classifier was tested on an independent set of FFPE tumor samples from 54 additional patients, and identified correctly 93% of the cases. Validation on qRT-PCR platform demonstrated high correlation with microarray results and accurate classification. MicroRNA expression profiling is a very effective molecular bioassay for classification of renal tumors and can offer a quantitative standardized complement to current methods of tumor classification.