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Thyroid disease and cancer diagnoses are common conditions likely to coexist. Optimal management requires appropriate diagnostic testing and consideration of a number of factors, including overall health status and prognosis. Hypothyroidism and hyperthyroidism can lead to a number of symptoms that may affect not only quality of life but can interfere with the patient’s ability to tolerate cancer treatment. Imaging studies performed for cancer staging can identify incidental structural abnormalities in the thyroid, which should be assessed with dedicated neck ultrasonography and possibly fine-needle aspiration. Incidental thyroid cancer is most often less urgent than the patient’s presenting malignancy and can be addressed surgically when appropriate in the context of other treatments (i.e., chemotherapy). Providers working in an oncology setting, as well as primary care providers, should be aware of medications that are associated with hormonal abnormalities. Any patient with a history of neck or brain radiation therapy is at risk of developing hypothyroidism and possibly other endocrinopathies. Complex or very ill patients may benefit from a multidisciplinary approach that utilizes the experience of a knowledgeable endocrinologist.

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Background: Recent results from the prospective validation of the Oncotype DX® recurrence score (RS) have underlined the clinical validity of the assay for the prediction of chemotherapy benefit in ER+/HER2- early stage breast cancer patients. Due to health economic restrictions, some patients have no easy access to the test. A pre-selection of tumor samples may help identify patients with a high likelihood to be spared chemotherapy. Histology and semi-quantitative IHC are hence used to select samples for Oncotype testing, but these suffer from intra- and inter-observer variability, especially for Ki-67 which is a main factor in most RS prediction algorithms. We have established and validated a tool for the prediction of RS risk classes (TAILORx cutoff RS ≤25) based on highly standardized, reproducible and locally performed RT-qPCR measurements of ERBB2, ESR1, PGR and MKI67 mRNA using the CE-marked IVD MammaTyper®. Methods: Total RNA was extracted from whole surface 10μm sections from FFPE breast cancer samples with a known RS result and a tumor cell content ≥20%. ERBB2, ESR1, PGR and MKI67 mRNA expression was measured by RT-qPCR on a CFX96 qPCR cycler using the MammaTyper® kit. A prediction model for an RS ≤25 result was established using multivariable logistic regression. Based on this model and the training data two cutoffs for confident prediction of low chemotherapy benefit patients in a clinical setting were established at 95% and 97.5% specificity. The model and the cutoffs were then fixed and validated in a second, separate set of breast cancer samples. ROC analysis was used to characterize predictive power of the continuous values resulting from the prediction model. Positive and negative predictive values for detection of an RS ≤25 result were also determined on the validation samples using the two pre-defined cutoffs. Results: The sample set for training of the prediction model encompassed 202 samples including 29 samples (14.4%) with an RS >25. In an initial multivariable model with all four markers, PGR and MKI67 were the strongest predictors while the influence of ESR in the model was lower, but still significant. ERBB2 was no significant predictor in this set of ERBB2 negative samples and was therefore excluded from the final model which was based on three markers only. This three marker model achieved an AUC of 0.920 (95% CI: 0.871-0.968) in the training samples. When applying the fixed model from the training dataset to a second separately collected set of 104 samples containing 20 samples (19.2%) with an RS >25, an AUC of 0.883 (95% CI: 0.810-0.955) was documented. When further applying the two predefined cutoffs established in the training set, 45 and 36 of the 104 validation samples (43.3% and 34.6%) had a predicted low chemotherapy benefit result (RS ≤25). Even with the less stringent cutoff, not a single one of the RS >25 cases from the validation cohort was falsely predicted as RS ≤25 sample. Conclusion: We have established a highly reliable method for prediction of Oncotype DX® low chemotherapy benefit results based on local and cost effective mRNA measurements. This method enables local pathologies to pre-assess routine samples using a highly precise molecular tool and thereby reserve the Oncotype DX® test for cases with ambiguous cancer biology. Citation Format: Lehr H-A, Aulmann S, Etzrodt A, Laible M, Hartmann K, Gürtler C, Wirtz RM, Sahin U, Varga Z. Standardized prediction of Oncotype DX® risk classes by local RT-qPCR [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P2-07-08.

Background: Oncotype DX® recurrence score (RS) has emerged as a recommended risk classifier for patients with ER+/HER2- early-stage breast cancer. While RS is one of the most rigorously studied risk scores, it is also one of the most expensive tests, thus remaining beyond reach for a many patients. The necessity for an affordable method for estimating risk of recurrence has motivated investigations on the correlation between RS and traditional parameters such as IHC for ER, PR and Ki67. However, semi-quantitative IHC lacks standardization across different laboratories especially for Ki67. In this study we therefore investigated whether the standardized assessment of HER2, ER, PR, and Ki67 on mRNA level could better serve for prediction of low risk RS cases. Methods: ERBB2, ESR1, PGR and MKI67 mRNA expression was measured by RT-qPCR in extracts from FFPE breast cancer samples using the MammaTyper® test. Complete data for RS, IHC, grading and mRNA measurement was available for 198 samples. Tumor subtypes according to St Gallen surrogate definition from 2013 were assigned based on binary mRNA marker classification (pos/neg) according to pre-defined cut-offs. Subtype results were compared to RS risk classes based on commercial and TAILORx-trial cut-offs. RS low risk classification (RS ≤25) based on four IHC markers and grading was estimated using the online tool breastrecurrenceestimator.onc.jhmi.edu and compared to observed RS classes. Finally, the prediction of continuous RS values by mRNA or semi-quantitative IHC measurement was compared by linear regression and subsequent ROC analysis of prediction models. Results: The distribution of RS risk classes in the set of samples with full data was 21% RS 0-10, 39% RS 11-17, 27% RS 18-25, 7% RS 26-30 and 7% RS >30. MammaTyper® called 38% (76) of the samples as Luminal A-like. From these samples 70% and 99% had RS values below 18 and 25 respectively. Only 1 MammaTyper® Luminal A-like sample had an RS >30. Estimation of RS according to the online tool resulted in classification of 61% (121) of the samples as low risk (RS ≤25). Of these 74% and 98% of samples had observed RS values below 18 and between 18 and 25 respectively. 2 and 1 samples called as low risk by the online tool had an RS of 26-30 and >30 and, respectively. In linear regression analysis of IHC against RS only PR and Ki67 were significant predictors (p-values <0.0001 and 0.0128) while when using mRNA values ESR1, PGR and MKI67 were found as predictors of RS in the multivariate model (all p-values <0.0001). On a training set (67% of samples) the IHC based prediction model was correlated to the observed RS with an R2 of 0.305 whereas the mRNA based model achieved an R2 of 0.489. When the models were applied to training and validation dataset (33% of samples) for prediction of an RS >25 result, the IHC based model had AUCs of 0.887 and 0.836, respectively, while the mRNA based model achieved AUCs of 0.909 and 0.899, respectively. Conclusion: mRNA based prediction of RS was considerably better than prediction based on IHC. As Ki67 IHC standardization is reaching its limits, local gene expression measurements with their high degree of standardization could serve as a safer way for prediction of Oncotype low risk results. Citation Format: Lehr H-A, Aulmann S, Laible M, Etzrodt A, Hartmann K, Gürtler C, Sahin U, Varga Z. Prediction of oncotype DX® results based on local gene expression measurements by MammaTyper® [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P1-06-11.