Background and Aims: Emerging evidence that microRNAs (miRNAs) play an important role in cancer development has opened up new opportunities for cancer diagnosis. Recent studies demonstrated that released exosomes which contain a subset of both cellular mRNA and miRNA could be a useful source of biomarkers for cancer detection. Here, we aim to develop a novel biomarker for breast cancer diagnosis using exosomal miRNAs in plasma. Methods: We have developed a rapid and novel isolation protocol to enrich tumor-associated exosomes from plasma samples by capturing tumor specific surface markers containing exosomes. After enrichment, we performed miRNA profiling on four sample sets; (1) Ep-CAM marker enriched plasma exosomes of breast cancer patients; (2) breast tumors of the same patients; (3) adjacent non-cancerous tissues of the same patients; (4) Ep-CAM marker enriched plasma exosomes of normal control subjects. Profiling is performed using PCR-based array with human microRNA panels that contain more than 700 miRNAs. Results: Our profiling data showed that 15 miRNAs are concordantly up-regulated and 13 miRNAs are concordantly down-regulated in both plasma exosomes and corresponding tumors. These account for ∼25% (up-regulation) and ∼15% (down-regulation) of all miRNAs detectable in plasma exosomes. Our findings demonstrate that miRNA profile in EpCAM-enriched plasma exosomes from breast cancer patients exhibit certain similar pattern to that in the corresponding tumors. Based on our profiling results, plasma signatures that differentiated breast cancer from control are generated and some of the well-known breast cancer related miRNAs such as miR-10b, miR-21, miR-155 and miR-145 are included in our panel list. The putative miRNA biomarkers are validated on plasma samples from an independent cohort from more than 100 cancer patients. Further validation of the selected markers is likely to offer an accurate, noninvasive and specific diagnostic assay for breast cancer. Conclusions: These results suggest that exosomal miRNAs in plasma may be a novel biomarker for breast cancer diagnosis. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P4-08-05.

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Background and Aims: Germline mutations in the two breast cancer susceptibility genes, BRCA1 and BRCA2 account for a significant portion of hereditary breast/ovarian cancer. Most of the BRCA mutations reported in Southern Chinese patients were point mutations, small deletions, and insertions. The spectrum of large genomic rearrangement (LGR) is largely unknown. Here we perform the first study on the LGR of BRCA genes in a Hong Kong Chinese population. We aimed to determine the spectrum of BRCA LGRs in Southern Chinese patients with breast cancer. Methods: A total of 555 clinically high-risk breast and/or ovarian cancer patients were recruited from the Hong Kong Hereditary Breast Cancer Family Registry, diagnosed from March 2007 to November 2011. Multiplex ligation-dependent probe amplification (MLPA) for detecting BRCA LGRs together with comprehensive BRCA1 and BRCA2 gene sequencing of all coding exons were performed. cDNA sequencing of the LGRs was performed to locate the breakpoint of the deletions. Results: Overall BRCA1/2 mutation prevalence among this cohort was 12.4% (69/555). Among the 69 mutations identified, 4 novel LGRs (2 in BRCA1 and 2 in BRCA2) were detected only by MLPA but not full gene sequencing. Overall the LGR genes accounted for 5.8% (4/69) of all BRCA mutations in our cohort, 6.9% (2/29) of all BRCA1 mutations and 5% (2/40) of all BRCA2 mutations. Conclusions: These findings highlight the LGR spectrum of BRCA1 and BRCA2 genes in Southern Chinese breast cancer patients. LGR testing together with BRCA1/2 full gene sequencing is superior to other methods for comprehensive BRCA1/2 analysis in clinical settings. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P4-11-02.

Background: Risk models (BRCAPRO, Myriad, Couch and Shattuck-Eidens, BOADICEA) are well established in Caucasian and African American cohorts to estimate the probability of BRCA1/2 mutation. Few studies have suggested its performance limitation in Asian cohorts. Most studies did not account for gender specific prediction. The aim of the study is to evaluate the performance of these models in a Chinese cohort who have breast/ovarian cancer at a pre-genetic test setting. Methods: Four risk assessment models, Boadicea, BRCAPRO, Myriad, Couch and Shattuck-Eidens, were used to perform risk calculations to 217 non-BRCA carriers (198 females and 19 males) and 32 BRCA carriers (28 females and 4 males. Sensitivity, specificity and area under the receiver operator characteristic (ROC) curve were calculated for each model to evaluate for calibration, discrimination and accuracy in BRCA mutation prediction stratified by gender. Results: The mean in prediction score in all models were statistically significantly higher in female BRCA mutation carriers. However, there were no statistically difference in mean prediction score between BRCA carriers and non-carriers in all models for male patients. BRCAPRO slightly over-estimated the total numbers of BRCA1 and BRCA2 female carriers (13 vs. 11 and 20 vs.17), but underestimated the number of BRCA2 male carriers (2.8 vs. 4). While Myriad underestimated both the total numbers of BRCA1/2 male (3.1 vs. 4) and female (25.6 vs. 28) carriers. Boadicea did the closest estimation for both male (3.2 vs. 4) and female (1.5 vs. 11 for BRCA1 and 16.3 vs. 17 in BRCA2). BRCAPRO showed the greatest ROC area for BRCA1 (93%), BRCA2 (73%) and BRCA1/2 (79%) combination mutation prediction and highest sensitivity at conventional thresholds of 10% and 20% in female patients (71.4% vs.60.7%). Boadicea had the greatest ROC area for BRCA2 and BRCA1/2 combination mutation prediction and the same sensitivity at conventional thresholds of 10% and 20% in male patients (92% vs. 93%). Conclusion: All 4 models could perform reasonably well in female patients, but not in male patients. Boadicea has the best performance in male and female Chinese cohort overall. Whereas when comparing females alone, BRCAPRO is most accurate. Citation Information: Cancer Res 2010;70(24 Suppl):Abstract nr P2-10-01.