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Agreement in risk prediction between the 21-gene recurrence score assay (Oncotype DX®) and the PAM50 breast cancer intrinsic Classifier™ in early-stage estrogen receptor-positive breast cancer.

ABSTRACT: PURPOSE: To compare risk assignment by PAM50 Breast Cancer Intrinsic Classifier™ and Oncotype DX_Recurrence Score (RS) in the same population. METHODS: RNA was extracted from 151 estrogen receptor (ER)+ stage I-II breast cancers and gene expression profiled using PAM50 "intrinsic" subtyping test. RESULTS: One hundred eight cases had complete molecular information; 103 (95%) were classified as luminal A (n = 76) or luminal B (n = 27). Ninety-two percent (n = 98) had a low (n = 59) or intermediate (n = 39) RS. Among luminal A cancers, 70% had low (n = 53) and the remainder (n = 23) had an intermediate RS. Among luminal B cancers, nine were high (33%) and 13 were intermediate (48%) by the RS. Almost all cancers with a high RS were classified as luminal B (90%, n = 9). One high RS cancer was identified as basal-like and had low ER/ESR1 and low human epidermal growth factor receptor 2 (HER2) expression by quantitative polymerase chain reaction in both assays. The majority of low RS cases were luminal A (83%, n = 53). Importantly, half of the intermediate RS cancers were re-categorized as low risk luminal A subtype by PAM50. CONCLUSION: There is good agreement between the two assays for high (i.e., luminal B or RS > 31) and low (i.e., luminal B or RS < 18) prognostic risk assignment but PAM50 assigns more patients to the low risk category. About half of the intermediate RS group was reclassified as luminal A by PAM50.

SUBMITTER: Kelly CM

PROVIDER: S-EPMC3336833 | biostudies-other | 2012

REPOSITORIES: biostudies-other

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Agreement in risk prediction between the 21-gene recurrence score assay (Oncotype DX®) and the PAM50 breast cancer intrinsic Classifier™ in early-stage estrogen receptor-positive breast cancer.

Kelly Catherine M CM Bernard Philip S PS Krishnamurthy Savitri S Wang Bailiang B Ebbert Mark T W MT Bastien Roy R L RR Boucher Kenneth M KM Young Elliana E Iwamoto Takayuki T Pusztai Lajos L

The oncologist 20120314 4

<h4>Purpose</h4>To compare risk assignment by PAM50 Breast Cancer Intrinsic Classifier™ and Oncotype DX_Recurrence Score (RS) in the same population.<h4>Methods</h4>RNA was extracted from 151 estrogen receptor (ER)+ stage I-II breast cancers and gene expression profiled using PAM50 "intrinsic" subtyping test.<h4>Results</h4>One hundred eight cases had complete molecular information; 103 (95%) were classified as luminal A (n = 76) or luminal B (n = 27). Ninety-two percent (n = 98) had a low (n = ...[more]

PMID: 22418568

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Project description:Purpose To investigate relationships between computer-extracted breast magnetic resonance (MR) imaging phenotypes with multigene assays of MammaPrint, Oncotype DX, and PAM50 to assess the role of radiomics in evaluating the risk of breast cancer recurrence. Materials and Methods Analysis was conducted on an institutional review board-approved retrospective data set of 84 deidentified, multi-institutional breast MR examinations from the National Cancer Institute Cancer Imaging Archive, along with clinical, histopathologic, and genomic data from The Cancer Genome Atlas. The data set of biopsy-proven invasive breast cancers included 74 (88%) ductal, eight (10%) lobular, and two (2%) mixed cancers. Of these, 73 (87%) were estrogen receptor positive, 67 (80%) were progesterone receptor positive, and 19 (23%) were human epidermal growth factor receptor 2 positive. For each case, computerized radiomics of the MR images yielded computer-extracted tumor phenotypes of size, shape, margin morphology, enhancement texture, and kinetic assessment. Regression and receiver operating characteristic analysis were conducted to assess the predictive ability of the MR radiomics features relative to the multigene assay classifications. Results Multiple linear regression analyses demonstrated significant associations (R2 = 0.25-0.32, r = 0.5-0.56, P < .0001) between radiomics signatures and multigene assay recurrence scores. Important radiomics features included tumor size and enhancement texture, which indicated tumor heterogeneity. Use of radiomics in the task of distinguishing between good and poor prognosis yielded area under the receiver operating characteristic curve values of 0.88 (standard error, 0.05), 0.76 (standard error, 0.06), 0.68 (standard error, 0.08), and 0.55 (standard error, 0.09) for MammaPrint, Oncotype DX, PAM50 risk of relapse based on subtype, and PAM50 risk of relapse based on subtype and proliferation, respectively, with all but the latter showing statistical difference from chance. Conclusion Quantitative breast MR imaging radiomics shows promise for image-based phenotyping in assessing the risk of breast cancer recurrence. © RSNA, 2016 Online supplemental material is available for this article.

Project description:The Oncotype DX 21-gene test can be used to predict chemotherapy efficacy in patients with estrogen receptor (ER)-positive and HER2-negative breast cancer; however, the data on the 21-gene recurrence score (RS) for mucinous breast carcinoma (MBC) are limited. The present study aimed to evaluate the distribution pattern and clinical value of the 21-gene RS in patients with MBC. A total of 38 pure MBC (PMBC) and 11 mixed MBC (MMBC) cases were retrospectively analyzed, and a total of 29 ER-positive and HER2-negative MBCs underwent the Oncotype DX 21-gene test. There were no statistically significant differences between the PMBCs and MMBCs in age, tumor size and molecular subtype; however, patients with MMBC showed a significantly higher incidence rate of nodal metastases compared with that in patients with PMBC (72.7 vs. 16.2%, respectively). Following surgery, 87.8 and 59.2% of the enrolled patients received endocrine therapy and chemotherapy, respectively. With a median follow-up of 65.6 months, the 5-year disease-free survival and overall survival rates were 97.0 and 100.0%, respectively. The 21-gene test revealed that the proportions of patients with MBC categorized into low (RS <18), intermediate (RS ≥18-30) and high (RS ≥30) risk groups were 51.7, 44.8 and 3.5%, respectively, and there was no statistically significant difference between the PMBC and MMBC cases. Notably, among the genes in the 21-gene RS testing, the expression levels of cathepsin V, progesterone receptor (PR) and CD68 were significantly higher in the PMBC group compared with that in the MMBC group. In conclusion, the current study demonstrated that patients with MBC had a favorable prognosis, and both PMBC and MMBC cases had a low- and intermediate-risk RS, which suggests that a considerable proportion of patients may be able to avoid chemotherapy. In addition, the high expression level of PR, based on the 21-gene test in PMBCs, indicated that they may have a more favorable response to endocrine therapy than MMBCs.

Project description:BackgroundCurrent clinical criteria do not discriminate well between women who will or those who will not develop ipsilateral invasive breast cancer (IBC), or a DCIS recurrence after a ductal carcinoma in situ (DCIS) diagnosis. The 12-gene Oncotype DX® DCIS assay (RT qPCR gene-based scoring system) was established and shown to predict the risk of subsequent ipsilateral IBC or DCIS recurrence. Recent studies have shown that microRNA (miRNA) expression deregulation can contribute to the development of IBC, but very few have evaluated miRNA deregulation in DCIS lesions. In this study, we sought to determine whether specific miRNA expression changes may correlate with Oncotype DX® DCIS scores.MethodsFor this study, we used archived formalin-fixed, paraffin-embedded (FFPE) specimens from 41 women diagnosed with DCIS between 2012 and 2018. The DCIS lesions were stratified into low (n = 26), intermediate (n = 10), and high (n = 5) risk score groups using the Oncotype DX® DCIS assay. Total RNA was extracted from DCIS lesions by macro-dissection of unstained FFPE sections, and next-generation small-RNA sequencing was performed. We evaluated the correlation between miRNA expression data and Oncotype score, as well as patient age. RT-qPCR validations were performed to validate the topmost differentially expressed miRNAs identified between the different risk score groups.ResultsMiRNA sequencing of 32 FFPE DCIS specimens from the three different risk group scores identified a correlation between expression deregulation of 17 miRNAs and Oncotype scores. Our analyses also revealed a correlation between the expression deregulation of 9 miRNAs and the patient's age. Based on these results, a total of 15 miRNAs were selected for RT-qPCR validation. Of these, miR-190b (p = 0.043), miR-135a (p = 0.05), miR-205 (p = 0.00056), miR-30c (p = 0.011), and miR-744 (p = 0.038) showed a decreased expression in the intermediate/high Oncotype group when compared to the low-risk score group. A composite risk score was established using these 5 miRNAs and indicated a significant association between miRNA expression deregulation and the Oncotype DX® DCIS Score (p < 0.0021), between high/intermediate and low risk groups.ConclusionsOur analyses identified a subset of 5 miRNAs able to discriminate between Oncotype DX® DCIS score subgroups. Together, our data suggest that miRNA expression analysis may add value to the predictive and prognostic evaluation of DCIS lesions.

Project description:BACKGROUND:Gene-expression companion diagnostic tests, such as the Oncotype DX test, assess the risk of early stage Estrogen receptor (ER) positive (+) breast cancers, and guide clinicians in the decision of whether or not to use chemotherapy. However, these tests are typically expensive, time consuming, and tissue-destructive. METHODS:In this paper, we evaluate the ability of computer-extracted nuclear morphology features from routine hematoxylin and eosin (H&E) stained images of 178 early stage ER+ breast cancer patients to predict corresponding risk categories derived using the Oncotype DX test. A total of 216 features corresponding to the nuclear shape and architecture categories from each of the pathologic images were extracted and four feature selection schemes: Ranksum, Principal Component Analysis with Variable Importance on Projection (PCA-VIP), Maximum-Relevance, Minimum Redundancy Mutual Information Difference (MRMR MID), and Maximum-Relevance, Minimum Redundancy - Mutual Information Quotient (MRMR MIQ), were employed to identify the most discriminating features. These features were employed to train 4 machine learning classifiers: Random Forest, Neural Network, Support Vector Machine, and Linear Discriminant Analysis, via 3-fold cross validation. RESULTS:The four sets of risk categories, and the top Area Under the receiver operating characteristic Curve (AUC) machine classifier performances were: 1) Low ODx and Low mBR grade vs. High ODx and High mBR grade (Low-Low vs. High-High) (AUC?=?0.83), 2) Low ODx vs. High ODx (AUC?=?0.72), 3) Low ODx vs. Intermediate and High ODx (AUC?=?0.58), and 4) Low and Intermediate ODx vs. High ODx (AUC?=?0.65). Trained models were tested independent validation set of 53 cases which comprised of Low and High ODx risk, and demonstrated per-patient accuracies ranging from 75 to 86%. CONCLUSION:Our results suggest that computerized image analysis of digitized H&E pathology images of early stage ER+ breast cancer might be able predict the corresponding Oncotype DX risk categories.

Dataset Information

Agreement in risk prediction between the 21-gene recurrence score assay (Oncotype DX®) and the PAM50 breast cancer intrinsic Classifier™ in early-stage estrogen receptor-positive breast cancer.

Publications

Agreement in risk prediction between the 21-gene recurrence score assay (Oncotype DX®) and the PAM50 breast cancer intrinsic Classifier™ in early-stage estrogen receptor-positive breast cancer.

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