Discriminating subtypes in advanced breast cancer with ctDNA methylation profiling.
Authors: Pasha Nida , Cutts Ros , Amenssag Aya , Hrebien Sarah , Gulati Aditi , Gao Qiong , Haider Syed , Roylance Rebecca , Tutt Andrew N. J. , Baird Richard D. , MacPherson Iain R. , Ring Alistair E. , Johnston Stephen R. D. , Okines Alicia Frances Clare , Garcia-Murillas Isaac , Filippi Sarah , Turner Nicholas C. 
Published: June 2024

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Background: Immunohistochemical subtyping of advanced breast cancer is critical to direct appropriate therapy. Yet, biopsy of recurrent cancer may not be technically possible, single-site tumour biopsies sample only a single metastasis and subtype may change through therapy. We developed a non-invasive blood test to subtype tumours based on circulating tumor DNA (ctDNA) methylation profiling. Methods: We interrogated TCGA-BRCA (level 3) methylation data from 783 tumours to identify regions with aberrant CpG methylation that differed between breast cancers and normal tissues, and between different breast cancer subtypes. A capture based enzymatic ctDNA based methylation profile was developed targeting 1257 differentially methylated regions (TWIST Methyl custom panel, TWIST Bioscience, panel size 0.4Mb). Plasma samples from patients with breast cancer, and healthy donors, were sequenced with the capture assay. A machine learning (ML) classifier was built to detect the presence of breast cancer, and a second classifier was built to discriminate ER positive v ER negative, and HER2 positive v HER2 negative from ctDNA methylation profiles. A 10-fold stratified nested cross-validation was used to assess classifier characteristics. Results: Plasma samples from 427 people (126 health donor, 27 early breast cancer patients and 291 metastatic breast cancer patients), along with 133 tumor tissue samples, were sequenced and a ML classifier built to detect the presence of breast cancer. In cross-validation, a random forest classifier yielded a median AUC of 0.996 (SD 0.020), with sensitivity of 94.9% at 99% specificity. A total of 231 ER positive and 63 ER negative plasma samples from patients were used for IHC ER status differentiation. In cross-validation a LGBM classifier yielded a median AUC of 0.928 (SD 0.051). Median sensitivity was 76.7% at specificity 86-99%. A total of 11% (25/231) ER positive and 8% (5/63) ER negative samples were misclassified. A total of 41 HER2 positive and 253 HER2 negative plasma samples were used for IHC HER2 status differentiation. In cross-validation a LGBM classifier yielded a median AUC of 0.934 (SD 0.075). Median sensitivity was 77.5% at specificity 85-92%, A total of 7% (3/41) HER-2 positive and 13% (33/253) HER2 negative samples were misclassified. In both IHC classifiers, subtype misclassifications were likely due to multiple concurrent BC tumours of differing IHC subtypes, biopsy proven subtype transitions and low purity. Effects of tumor purity on IHC-based classifiers will also be presented. Conclusions: Non-invasivemethylation based ctDNA subtyping can detect the presence of breast cancer, and discriminate IHC subtypes, with high accuracy. Further assessment is warranted to assess whether methylation-based subtyping has the potential to report IHC subtypes when repeat biopsy is not possible, detect subtype transitions and direct advanced breast cancer treatment.