A Supervised Risk Predictor of Breast Cancer Based on Biological Subtypes
ABSTRACT: Purpose: The biological subtypes of breast cancer designated as Luminal A, Luminal B, HER2+/ER-, and Basal-like are clinically important for prognosis and planning treatment strategies. Recognizing that there is a continuum in both the spectrum of breast cancer disease and the risk of survival, we sought to develop a clinical test for the biological subtypes using a supervised risk classier.Methods: Microarray and real-time quantitative RT-PCR (qRT-PCR) data from 189 samples, procured as fresh-frozen and formalin-fixed, paraffin-embedded tissues, were used to statistically select prototypical samples and genes for the biological subtypes of breast cancer. Predictions for biological subtype and risk of recurrence were determined for different stages of disease, treatments, and across analytical platforms. Results: The biological subtype predictions on a large combined microarray test set showed prognostic significance across all patients (1244 subjects; p<0.0001), on node negative patients with no adjuvant systemic therapy (738 subjects; p<0.0001), and on patients treated with endocrine therapy (404 subjects; p=0.001). Analysis of a neoadjuvant chemotherapy study revealed a high pathologic complete response (pCR) rate in HER2+/ER- and Basal-like patients. The subtype and risk predications were also highly significant when using the qRT-PCR assay from archived FFPE breast cancers. Conclusion: Our risk predictor based on distance to biological subtype centroids provides a continuous risk score that applies to all stages of breast cancer given current therapies. The assay can be performed using archived breast tissues and a real-time qRT-PCR assay, thus facilitating application to retrospective cohorts and clinical samples. Keywords: reference x sample Overall design: Comparison of reference samples against treatment
INSTRUMENT(S): Agilent-011521 Human 1A Microarray G4110A (Feature Number version)
Project description:Purpose: The biological subtypes of breast cancer designated as Luminal A, Luminal B, HER2+/ER-, and Basal-like are clinically important for prognosis and planning treatment strategies. Recognizing that there is a continuum in both the spectrum of breast cancer disease and the risk of survival, we sought to develop a clinical test for the biological subtypes using a supervised risk classier.Methods: Microarray and real-time quantitative RT-PCR (qRT-PCR) data from 189 samples, procured as fresh-frozen and formalin-fixed, paraffin-embedded tissues, were used to statistically select prototypical samples and genes for the biological subtypes of breast cancer. Predictions for biological subtype and risk of recurrence were determined for different stages of disease, treatments, and across analytical platforms. Results: The biological subtype predictions on a large combined microarray test set showed prognostic significance across all patients (1244 subjects; p<0.0001), on node negative patients with no adjuvant systemic therapy (738 subjects; p<0.0001), and on patients treated with endocrine therapy (404 subjects; p=0.001). Analysis of a neoadjuvant chemotherapy study revealed a high pathologic complete response (pCR) rate in HER2+/ER- and Basal-like patients. The subtype and risk predications were also highly significant when using the qRT-PCR assay from archived FFPE breast cancers. Conclusion: Our risk predictor based on distance to biological subtype centroids provides a continuous risk score that applies to all stages of breast cancer given current therapies. The assay can be performed using archived breast tissues and a real-time qRT-PCR assay, thus facilitating application to retrospective cohorts and clinical samples. Keywords: reference x sample Comparison of reference samples against treatment
Project description:Predicting the clinical course of breast cancer is often difficult because it is a diverse disease comprised of many biological subtypes. Gene expression profiling by microarray analysis has identified breast cancer signatures that are important for prognosis and treatment. In the current article, we use microarray analysis and a real-time quantitative reverse-transcription (qRT)-PCR assay to risk-stratify breast cancers based on biological 'intrinsic' subtypes and proliferation.Gene sets were selected from microarray data to assess proliferation and to classify breast cancers into four different molecular subtypes, designated Luminal, Normal-like, HER2+/ER-, and Basal-like. One-hundred and twenty-three breast samples (117 invasive carcinomas, one fibroadenoma and five normal tissues) and three breast cancer cell lines were prospectively analyzed using a microarray (Agilent) and a qRT-PCR assay comprised of 53 genes. Biological subtypes were assigned from the microarray and qRT-PCR data by hierarchical clustering. A proliferation signature was used as a single meta-gene (log2 average of 14 genes) to predict outcome within the context of estrogen receptor status and biological 'intrinsic' subtype.We found that the qRT-PCR assay could determine the intrinsic subtype (93% concordance with microarray-based assignments) and that the intrinsic subtypes were predictive of outcome. The proliferation meta-gene provided additional prognostic information for patients with the Luminal subtype (P = 0.0012), and for patients with estrogen receptor-positive tumors (P = 3.4 x 10-6). High proliferation in the Luminal subtype conferred a 19-fold relative risk of relapse (confidence interval = 95%) compared with Luminal tumors with low proliferation.A real-time qRT-PCR assay can recapitulate microarray classifications of breast cancer and can risk-stratify patients using the intrinsic subtype and proliferation. The proliferation meta-gene offers an objective and quantitative measurement for grade and adds significant prognostic information to the biological subtypes.
Project description:To compare clinical, immunohistochemical (IHC), and gene expression models of prognosis applicable to formalin-fixed, paraffin-embedded blocks in a large series of estrogen receptor (ER)-positive breast cancers from patients uniformly treated with adjuvant tamoxifen.Quantitative real-time reverse transcription-PCR (qRT-PCR) assays for 50 genes identifying intrinsic breast cancer subtypes were completed on 786 specimens linked to clinical (median follow-up, 11.7 years) and IHC [ER, progesterone receptor (PR), HER2, and Ki67] data. Performance of predefined intrinsic subtype and risk-of-relapse scores was assessed using multivariable Cox models and Kaplan-Meier analysis. Harrell's C-index was used to compare fixed models trained in independent data sets, including proliferation signatures.Despite clinical ER positivity, 10% of cases were assigned to nonluminal subtypes. qRT-PCR signatures for proliferation genes gave more prognostic information than clinical assays for hormone receptors or Ki67. In Cox models incorporating standard prognostic variables, hazard ratios for breast cancer disease-specific survival over the first 5 years of follow-up, relative to the most common luminal A subtype, are 1.99 [95% confidence interval (CI), 1.09-3.64] for luminal B, 3.65 (95% CI, 1.64-8.16) for HER2-enriched subtype, and 17.71 (95% CI, 1.71-183.33) for the basal-like subtype. For node-negative disease, PAM50 qRT-PCR-based risk assignment weighted for tumor size and proliferation identifies a group with >95% 10-year survival without chemotherapy. In node-positive disease, PAM50-based prognostic models were also superior.The PAM50 gene expression test for intrinsic biological subtype can be applied to large series of formalin-fixed, paraffin-embedded breast cancers, and gives more prognostic information than clinical factors and IHC using standard cut points.
Project description:Gene expression profiling classifies breast cancer into intrinsic subtypes based on the biology of the underlying disease pathways. We have used material from a prospective randomized trial of tamoxifen versus placebo in premenopausal women with primary breast cancer (NCIC CTG MA.12) to evaluate the prognostic and predictive significance of intrinsic subtypes identified by both the PAM50 gene set and by immunohistochemistry.Total RNA from 398 of 672 (59%) patients was available for intrinsic subtyping with a quantitative reverse transcriptase PCR (qRT-PCR) 50-gene predictor (PAM50) for luminal A, luminal B, HER-2-enriched, and basal-like subtypes. A tissue microarray was also constructed from 492 of 672 (73%) of the study population to assess a panel of six immunohistochemical IHC antibodies to define the same intrinsic subtypes.Classification into intrinsic subtypes by the PAM50 assay was prognostic for both disease-free survival (DFS; P = 0.0003) and overall survival (OS; P = 0.0002), whereas classification by the IHC panel was not. Luminal subtype by PAM50 was predictive of tamoxifen benefit [DFS: HR, 0.52; 95% confidence interval (CI), 0.32-0.86 vs. HR, 0.80; 95% CI, 0.50-1.29 for nonluminal subtypes], although the interaction test was not significant (P = 0.24), whereas neither subtyping by central immunohistochemistry nor by local estrogen receptor (ER) or progesterone receptor (PR) status were predictive. Risk of relapse (ROR) modeling with the PAM50 assay produced a continuous risk score in both node-negative and node-positive disease.In the MA.12 study, intrinsic subtype classification by qRT-PCR with the PAM50 assay was superior to IHC profiling for both prognosis and prediction of benefit from adjuvant tamoxifen.
Project description:Objective: Epstein-Barr virus (EBV) has been found to be implicated in the development of breast cancer. The purpose of the present study was to identify the associations of EBV DNA and the subtypes in peripheral blood mononuclear cells (PBMCs) with the risk of breast cancer. Material and Methods: A case-control study with 671 breast cancer cases and 859 age-matched controls was conducted in Guangzhou, China. Face-to-face interviews were performed and blood samples were collected immediately after admission to the hospital for patients or after the interview for controls. EBV DNA in PBMCs and the subtypes were detected using Polymerase Chain Reaction (PCR) and restricted fragment length polymorphisms (RFLP). IgA antibodies against EBV VCA-p18 and EBNA-1 were examined using commercial enzyme-linked immunosorbent assay kits. Unconditional logistic regression analysis was applied to evaluate the associations of the DNA positivity and subtypes of EBV with the risk of breast cancer. Results: Among the 1530 subjects, 164 cases (24.4 %) and 206 controls (24.0 %) were positive for EBV DNA in PBMCs and no significant difference occurred between cases and controls. The presence of EBV DNA was related to the positivity of EBV IgA antibodies. Of the DNA positive samples, 71 cases and 109 controls for F/f subtype and 58 cases and 112 controls for C/D subtype were successfully obtained. The D subtype was associated with an increased breast cancer risk compared with the C subtype [OR (95% CI): 2.86 (1.25~6.53)]. We did not find an association of the F/f polymorphism with breast cancer risk. Conclusions: The present study suggested that the presence of EBV DNA in PBMCs may not be an appropriate biomarker for breast cancer risk. The subtype D of EBV was likely to be related to breast tumorigenesis.
Project description:PURPOSE:Multi-gene signatures provide biological insight and risk stratification in breast cancer. Intrinsic molecular subtypes defined by mRNA expression of 50 genes (PAM50) are prognostic in hormone-receptor positive postmenopausal breast cancer. Yet, for 25-40% in the PAM50 intermediate risk group, long-term risk remains uncertain. Our study aimed to (i) test the long-term prognostic value of the PAM50 signature in pre- and post-menopausal breast cancer; (ii) investigate if the PAM50 model could be improved by addition of other mRNAs implicated in oncogenesis. METHODS:We used archived FFPE samples from 1723 breast cancer survivors; high quality reads were obtained on 1253 samples. Transcript expression was quantified using a custom codeset with probes for >?100 targets. Cox models assessed gene signatures for breast cancer relapse and survival. RESULTS:Over 15?+ years of follow-up, PAM50 subtypes were (P?<?0.01) associated with breast cancer outcomes after accounting for tumor stage, grade and age at diagnosis. Results did not differ by menopausal status at diagnosis. Women with Luminal B (versus Luminal A) subtype had a >?60% higher hazard. Addition of a 13-gene hypoxia signature improved prognostication with >?40% higher hazard in the highest vs lowest hypoxia tertiles. CONCLUSIONS:PAM50 intrinsic subtypes were independently prognostic for long-term breast cancer survival, irrespective of menopausal status. Addition of hypoxia signatures improved risk prediction. If replicated, incorporating the 13-gene hypoxia signature into the existing PAM50 risk assessment tool, may refine risk stratification and further clarify treatment for breast cancer.
Project description:It has recently been proposed that a three-gene model (SCMGENE) that measures ESR1, ERBB2, and AURKA identifies the major breast cancer intrinsic subtypes and provides robust discrimination for clinical use in a manner very similar to a 50-gene subtype predictor (PAM50). However, the clinical relevance of both predictors was not fully explored, which is needed given that a ~30 % discordance rate between these two predictors was observed. Using the same datasets and subtype calls provided by Haibe-Kains and colleagues, we compared the SCMGENE assignments and the research-based PAM50 assignments in terms of their ability to (1) predict patient outcome, (2) predict pathological complete response (pCR) after anthracycline/taxane-based chemotherapy, and (3) capture the main biological diversity displayed by all genes from a microarray. In terms of survival predictions, both assays provided independent prognostic information from each other and beyond the data provided by standard clinical-pathological variables; however, the amount of prognostic information was found to be significantly greater with the PAM50 assay than the SCMGENE assay. In terms of chemotherapy response, the PAM50 assay was the only assay to provide independent predictive information of pCR in multivariate models. Finally, compared to the SCMGENE predictor, the PAM50 assay explained a significantly greater amount of gene expression diversity as captured by the two main principal components of the breast cancer microarray data. Our results show that classification of the major and clinically relevant molecular subtypes of breast cancer are best captured using larger gene panels.
Project description:Current biomarkers for breast cancer have little potential for detection. We determined whether breast cancer subtypes influence circulating protein biomarkers.A sandwich ELISA microarray platform was used to evaluate 23 candidate biomarkers in plasma samples that were obtained from subjects with either benign breast disease or invasive breast cancer. All plasma samples were collected at the time of biopsy, after a referral due to a suspicious screen (e.g., mammography). Cancer samples were evaluated on the basis of breast cancer subtypes, as defined by the HER2 and estrogen receptor statuses.Ten proteins were statistically altered in at least one breast cancer subtype, including four epidermal growth factor receptor ligands, two matrix metalloproteases, two cytokines, and two angiogenic factors. Only one cytokine, RANTES, was significantly increased (P < 0.01 for each analysis) in all four subtypes, with areas under the curve (AUC) for receiver operating characteristic values that ranged from 0.76 to 0.82, depending on cancer subtype. The best AUC values were observed for analyses that combined data from multiple biomarkers, with values ranging from 0.70 to 0.99, depending on the cancer subtype. Although the results for RANTES are consistent with previous publications, the multi-assay results need to be validated in independent sample sets.Different breast cancer subtypes produce distinct biomarker profiles, and circulating protein biomarkers have potential to differentiate between true- and false-positive screens for breast cancer.Subtype-specific biomarker panels may be useful for detecting breast cancer or as an adjunct assay to improve the accuracy of current screening methods.
Project description:PURPOSE To improve on current standards for breast cancer prognosis and prediction of chemotherapy benefit by developing a risk model that incorporates the gene expression-based "intrinsic" subtypes luminal A, luminal B, HER2-enriched, and basal-like. METHODS A 50-gene subtype predictor was developed using microarray and quantitative reverse transcriptase polymerase chain reaction data from 189 prototype samples. Test sets from 761 patients (no systemic therapy) were evaluated for prognosis, and 133 patients were evaluated for prediction of pathologic complete response (pCR) to a taxane and anthracycline regimen.The intrinsic subtypes as discrete entities showed prognostic significance (P = 2.26E-12) and remained significant in multivariable analyses that incorporated standard parameters (estrogen receptor status, histologic grade, tumor size, and node status). A prognostic model for node-negative breast cancer was built using intrinsic subtype and clinical information. The C-index estimate for the combined model (subtype and tumor size) was a significant improvement on either the clinicopathologic model or subtype model alone. The intrinsic subtype model predicted neoadjuvant chemotherapy efficacy with a negative predictive value for pCR of 97%. CONCLUSION Diagnosis by intrinsic subtype adds significant prognostic and predictive information to standard parameters for patients with breast cancer. The prognostic properties of the continuous risk score will be of value for the management of node-negative breast cancers. The subtypes and risk score can also be used to assess the likelihood of efficacy from neoadjuvant chemotherapy.
Project description:Serum circulating microRNAs (c-miRNAs) are serving as useful biomarkers for cancer diagnosis. Here, we describe the development of a one-step branched rolling circle amplification (BRCA) method to measure serum c-miRNAs levels for early diagnosis of breast cancer. Four c-miRNAs, c-miRNA16 (c-miR-16), c-miRNA21 (c-miR-21), c-miRNA155 (c-miR-155), and c-miRNA195 (c-miR-195) were isolated from the serum of 49 breast cancer patients (stages I-IV) and 19 healthy controls, and analyzed using one-step BRCA. The serum levels of c-miR16, c-miR21, c-miR155, and c-miR195 were higher (P < 0.0001) in stage I breast cancer patients than healthy controls. These levels were also higher in several breast cancer molecular subtypes (HER-2 over-expression, Luminal A, Luminal B, and triple negative breast cancer) than in healthy control subjects. The diagnostic accuracy of c-miR16, c-miR21, c-miR155, and c-miR195 for early diagnosis of breast cancer was confirmed by receiver operating characteristic (ROC) curve assay. These results show that the BRCA method can be used to measure serum c-miRNAs levels, and that this method has high accuracy, sensitivity, and specificity. Moreover, both BRCA approach and quantitative real-time PCR (qRT-PCR) method show that the serum levels of c-miR16, c-miR21, c-miR155, and c-miR195 could be used as biomarkers to improve the early diagnosis of breast cancer, and distinguish different breast cancer molecular subtypes.