Comparing Genetic Risk and Clinical Risk Classification in Luminal-like Breast Cancer Patients Using a 23-Gene Classifier.

Background: A 23-gene classifier has been developed based on gene expression profiles of Taiwanese luminal-like breast cancer. We aim to stratify risk of relapse and identify patients who may benefit from adjuvant chemotherapy based on genetic model among distinct clinical risk groups. Methods: There were 248 luminal (hormone receptor-positive and human epidermal growth factor receptor II-negative) breast cancer patients with 23-gene classifier results. Using the modified Adjuvant! Online definition, clinical high/low-risk groups were tabulated with the genetic model. The primary endpoint was a recurrence-free interval (RFI) at 5 years. Results: There was a significant difference between the high/low-risk groups defined by the 23-gene classifier for the 5-year prognosis of recurrence (16 recurrences in high-risk and 3 recurrences in low-risk; log-rank test: p < 0.0001). Among the clinically high-risk group, the 5-year RFI of high risk defined by the 23-gene classifier was significantly higher than that of the low-risk group (15 recurrences in high-risk and 2 recurrences in low-risk; log-rank test: p < 0.0001). Conclusion: This study showed that 23-gene classifier can be used to stratify clinically high-risk patients into distinct survival patterns based on genomic risks and displays the potentiality to guide adjuvant chemotherapy. The 23-gene classifier can provide a better estimation of breast cancer prognosis which can help physicians make a better treatment decision.

Correlation of ER, PR, and HER2 at the protein and mRNA levels in Asian patients with operable breast cancer.

Breast cancer is the most common cancer and the leading cause of cancer-related deaths in women. The estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) are the important biomarkers in the prognosis of breast cancer, and their expression is used to categorize breast cancer into subtypes. We aimed to analyze the concordance among ER, PR, and HER2 expression levels and breast cancer subtyping results obtained by immunohistochemistry (IHC, for protein) and reverse transcriptase-polymerase chain reaction (RT-PCR, for mRNA) and to assess the recurrence-free survival (RFS) of the different subtypes as determined by the two methods. We compared biomarker expression by IHC and RT-PCR in 397 operable breast cancer patients and categorized all patients into luminal, HER2, and triple-negative (TN) subtypes. The concordance of biomarker expression between the two methods was 81.6% (κ = 0.4075) for ER, 87.2% (κ = 0.5647) for PR, and 79.1% (κ = 0.2767) for HER2. The κ-statistic was 0.3624 for the resulting luminal, HER2, and TN subtypes. The probability of 5-year RFS was 0.78 for the luminal subtype versus 0.77 for HER2 and 0.51 for TN, when determined by IHC (P=0.007); and 0.80, 0.71, and 0.61, respectively, when determined by the RT-PCR method (P=0.008). Based on the current evidence, subtyping by RT-PCR performs similar to conventional IHC with regard to the 5-year prognosis. The PCR method may thus provide a complementary means of subtyping when IHC results are ambiguous.

A Clinicogenetic Prognostic Classifier for Prediction of Recurrence and Survival in Asian Breast Cancer Patients.

BACKGROUND: Several prognostic factors affect the recurrence of breast cancer in patients who undergo mastectomy. Assays of the expression profiles of multiple genes increase the probability of overexpression of certain genes and thus can potentially characterize the risk of metastasis. METHODS: We propose a 20-gene classifier for predicting patients with high/low risk of recurrence within 5 years. Gene expression levels from a quantitative PCR assay were used to screen 473 luminal breast cancer patients treated at Taiwan Hospital (positive for estrogen and progesterone receptors, negative for human epidermal growth factor receptor 2). Gene expression scores, along with clinical information (age, tumor stage, and nodal stage), were evaluated for risk prediction. The classifier could correctly predict patients with and without relapse (logistic regression, P<0.05). RESULTS: A Cox proportional hazards regression analysis showed that the 20-gene panel was prognostic with hazard ratios of 5.63 (95% confidence interval 2.77-11.5, univariate) and 5.56 (2.62-11.8, multivariate) for the "genetic" model, and of 8.02 (3.52-18.3, univariate) and 19.8 (5.96-65.87, multivariate) for the "clinicogenetic" model during a 5-year follow-up. CONCLUSIONS: The proposed 20-gene classifier can successfully separate the patients into two risk groups, and the two risk group had significantly different relapse rate and prognosis. This 20-gene classifier can provide better estimation of prognosis, which can help physicians to make better personalized treatment plans.

A 23-gene prognostic classifier for prediction of recurrence and survival for Asian breast cancer patients.

We report a 23- gene-classifier profiled from Asian women, with the primary purpose of assessing its clinical utility towards improved risk stratification for relapse for breast cancer patients from Asian cohorts within 10 years' following mastectomy. Four hundred and twenty-two breast cancer patients underwent mastectomy and were used to train the classifier on a logistic regression model. A subset of 197 patients were chosen to be entered into the follow-up studies post mastectomy who were examined to determine the patterns of recurrence and survival analysis based on gene expression of the gene classifier, age at diagnosis, tumor stage and lymph node status, over a 5 and 10 years follow-up period. Metastasis to lymph node (N2-N3) with N0 as the reference (N2 vs. N0 hazard ratio: 2.02 (1.05-8.70), N3 vs. N0 hazard ratio: 4.32 (1.41-13.22) for 5 years) and gene expression of the 23-gene panel (P=0.06, 5 years and 0.02, 10 years, log-rank test) were found to have significant discriminatory effects on the risk of relapse (HR (95%CI):2.50 (0.95-6.50)). Furthermore, survival curves for subgroup analysis with N0-N1 and T1-T2 predicted patients with higher risk scores. The study provides robust evidence of the effectiveness of the 23-gene-classifier and could be used to determine the risk of relapse event (locoregional and distant recurrence) in Asian patients, leading to a meaningful reduction in chemotherapy recommendations.

A preliminary report of head-to-head comparison of 18-gene-based clinical-genomic model and oncotype DX 21-gene assay for predicting recurrence of early-stage breast cancer.

BACKGROUND: The information of Oncotype DX applied in Asian breast cancer patients is limited. A recurrence index for distant recurrence (RI-DR) has been developed for early-stage breast cancer (EBC) from tumor samples in Chinese patients. In this study, we compared the prognostic performance of the Oncotype DX (ODx) recurrence score (RS) with the RI-DR for any recurrence risk type. MATERIALS AND METHODS: One hundred thirty-eight (138) patients with hormone receptor-positive and human epidermal growth factor receptor 2-negative EBC who were previously tested with ODx were included for testing with the RI-DR. The cutoff score to partition the low- and high-risk patients was 26 for RS and 36 for RI-DR. The primary endpoint was recurrence-free survival (RFS). RESULTS: The concordance between the RI-DR and RS was 83% in N0 patients and 81% in node-positive patients when the RS score cutoff was set at 26. With a median follow-up interval of 36.8 months, the 4-year RFS for the high- and low-risk groups categorized by the RS were 61.9% and 95.0%, respectively (hazard ratio: 10.6, 95.0% confidence interval [CI]: 1.8-62.9). The 4-year RFS in the high- and low-risk groups categorized by the RI-DR were 72.6% and 98.5%, respectively (hazard ratio: 18.9, 95% CI: 1.8-138.8). CONCLUSION: This paper illustrated the performance of RI-DR and ODx RS in breast cancer women in Taiwan. There was high concordance between the RI-DR and RS. The RI-DR is not inferior to the RS in predicting RFS in EBC patients. This study will fill the gap between the current and best practice in Chinese patients.

Visible-absorption spectroscopy as a biomarker to predict treatment response and prognosis of surgically resected esophageal cancer.

The application of optical absorption spectra in prognostic prediction has hardly been investigated. We developed and evaluated a novel two dimensional absorption spectrum measurement system (TDAS) for use in early diagnosis, evaluating response to chemoradiation, and making prognostic prediction. The absorption spectra of 120 sets of normal and tumor tissues from esophageal cancer patients were analyzed with TDAS ex-vivo. We demonstrated the cancerous tissue, the tissue from patients with a poor concurrent chemoradiotherapy (CCRT) response, and the tissue from patients with an early disease progression each had a readily identifiable common spectral signature. Principal component analysis (PCA) classified tissue spectra into distinct groups, demonstrating the feasibility of using absorption spectra in differentiating normal and tumor tissues, and in predicting CCRT response, poor survival and tumor recurrence (efficiencies of 75%, 100% and 85.7% respectively). Multivariate analysis revealed that patients identified as having poor-response, poor-survival and recurrence spectral signatures were correlated with increased risk of poor response to CCRT (P = 0.012), increased risk of death (P = 0.111) and increased risk of recurrence (P = 0.030) respectively. Our findings suggest that optical absorption microscopy has great potential to be a useful tool for pre-operative diagnosis and prognostic prediction of esophageal cancer.

A protein interaction based model for schizophrenia study.

BACKGROUND: Schizophrenia is a complex disease with multiple factors contributing to its pathogenesis. In addition to environmental factors, genetic factors may also increase susceptibility. In other words, schizophrenia is a highly heritable disease. Some candidate genes have been deduced on the basis of their known function with others found on the basis of chromosomal location. Individuals with multiple candidate genes may have increased risk. However it is not clear what kind of gene combinations may produce the disease phenotype. Their collective effect remains to be studied. RESULTS: Most pathways except metabolic pathways are rich in protein-protein interactions (PPIs). Thus, the PPI network contains pathway information, even though the upstream-downstream relation of PPI is yet to be explored. Here we have constructed a PPI sub-network by extracting the nearest neighbour of the 36 reported candidate genes described in the literature. Although these candidate genes were discovered by different approaches, most of the proteins formed a cluster. Two major protein interaction modules were identified on the basis of the pairwise distance among the proteins in this sub-network. The large and small clusters might play roles in synaptic transmission and signal transduction, respectively, based on gene ontology annotation. The protein interactions in the synaptic transmission cluster were used to explain the interaction between the NRG1 and CACNG2 genes, which was found by both linkage and association studies. This working hypothesis is supported by the co-expression analysis based on public microarray gene expression. CONCLUSION: On the basis of the protein interaction network, it appears that the NRG1-triggered NMDAR protein internalization and the CACNG2 mediated AMPA receptor recruiting may act together in the glutamatergic signalling process. Since both the NMDA and AMPA receptors are calcium channels, this process may regulate the influx of Ca2+. Reducing the cation influx might be one of the disease mechanisms for schizophrenia. This PPI network analysis approach combined with the support from co-expression analysis may provide an efficient way to propose pathogenetic mechanisms for various highly heritable diseases.