Abstract P3-07-08: Comparison of MammaTyper® RT-qPCR based subtyping with simulated breast cancer prognostic signatures
Authors: Laible Mark , Aulmann Sebastian , Etzrodt Alfred , Schmidt Marcus , Crozier Cheryl , Wirtz Ralph , Bayani Jane , Sahin Ugur , Bartlett John MS 
Published: March 2020

Background: Current prognostic signatures are based on measurements of numerous targets and require specialized equipment and complex calculations. While the overall prognostic capabilities of the tests may be comparable in ER+/HER2- breast cancer, side by side comparisons of different tests have shown that results differ on the individual patient level (Bartlett et al., JNCI, 2016). Here we determined risk scores and classifications in 160 tumors to the following commercial and academic prognostic tests using a custom gene panel for which the publically available algorithms were trained against the actual commercial test scores: Mammaprint®, Oncotype DX®, Prosigna® including PAM50 subtyping; and academic risk scores: IHC4-like, 95-gene score and the Genomic Grade Index. These trained risk classifiers were compared to the results of the MammaTyper® IVD-kit. Methods: RNA was extracted from FFPE tissue sections and analyzed on the Nanostring nCounter® using a probe set containing all targets and reference genes used in the original signatures (N=199). Nanostring measurements and calculations of simulated risk classifiers were carried out as described previously (Bayani et al., npj Breast Cancer, 2017) and retrained against actual commercial test scores (Bartlett et al., in prep.). MammaTyper® RT-qPCR of ERBB2, ESR1, PGR and MKI67 (normalized to CALM2) was carried out on a separate aliquot of the same RNA sample. St. Gallen surrogate subtypes were assigned based on binary mRNA expression according to pre-defined cutoffs. For calculating pairwise agreements between test categorizations, simulated test results with ternary classification were transformed to binary classifications (IHC4-like: low + int vs. high, Oncotype DX-trained: RS≤25 vs. RS&gt;25, Prosigna-trained ROR-P: low vs. int + high). Comparison of MammaTyper® with simulated risk scores was done on the level of surrogate subtypes and on the level of continuous MKI67 and LRP score values. The MammaTyper® LRP score is a previously developed signature to predict RS ≤25 results with high specificity. Results: Marker positivity rate by RT-qPCR was 83% for ESR1, 72% for PGR, 9% for ERBB2 and 58% for MKI67 based on the pre-specified MammaTyper® cutoffs. The highest rate of low risk results was found for binary RS classification with 68.1% of RS ≤25 samples. MammaTyper® Luminal A-like result was found in 30.6% of samples a rate similar to ROR-P, Genomic Grade Index or MammaPrint-trained low risk results (35.0%, 38.1% and 38.8% respectively). The highest agreement between the simulated risk classifications was observed between binary OncotypeDX-trained RS and IHC4-like classifications with 93.1%. Prosigna-trained (ROR-P) and IHC4-like score had the lowest pairwise agreement with only 65.0% agreement. Oncotype DX-trained and the 95 gene score had the highest concordance to MammaTyper® Luminal A-like subtype with 100% and 96% of Luminal A-like samples classified as RS ≤25 and low risk also by these signatures, respectively. ROC analyses of MammaTyper®MKI67 against binary 95-gene score risk classifier resulted in an AUC of 0.949. The lowest AUC for MKI67 was achieved against simulated MammaPrint-trained classification (AUC=0.874). When applying the LRP score to ROC analysis, almost identical AUCs were achieved for IHC4-like and binary Oncotype DX-trained classifications with AUCs of 0.976 and 0.969 respectively. Conclusion: Low cost local RT-qPCR measurements by MammaTyper® show a high agreement with the scores generated by our Oncotype DX-trained scores. The academic and well validated 95-gene signature agrees well with MammaTyper®MKI67 single gene measurement. Therefore, the locally performed MammaTyper® test may serve as cost effective surrogate for complex prognostic signatures or at least enables a pre-screening for molecularly “obviously” low risk samples.

Citation Format: Mark Laible, Sebastian Aulmann, Alfred Etzrodt, Marcus Schmidt, Cheryl Crozier, Ralph Wirtz, Jane Bayani, Ugur Sahin, John MS Bartlett. Comparison of MammaTyper® RT-qPCR based subtyping with simulated breast cancer prognostic signatures [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P3-07-08.