Abstract PS17-01: A metabolomic signature as screening method for breast cancer diagnosis
Authors: Onesti Concetta Elisa , Boemer François , Josse Claire , Debit Ahmed , Poulet Christophe , Bours Vincent , Jerusalem Guy 
Published: February 2021

Introduction: There is a close relationship between metabolism and cancer, which modifies its metabolic network to support cell survival. This may be reflected in a release of metabolites into the circulating blood, which may allow the identification of a signature associated with a tumor. Here we analyze a metabolomic profile of non-metastatic breast cancer patients and healthy controls to identify a diagnostic signature. Materials and methods: We prospectively enrolled 350 subjects in our study. A blood sample withdrawal at breast cancer diagnosis or the day of the screening mammography for the control group was done. After centrifugation, plasma was collected and stored at -80 °C. A panel of 61 metabolites was tested on a TQ5500 tandem mass spectrometer in triplicate for each sample and with internal standard and inter-run calibrators. ComBat tool from GenePattern platform was used to remove the batch effect. After outliers removal with Tukey’s method and mean value calculation for each replicate, a Z-standardization was done. A 10-fold cross-validation (CV) was used to find the best representative validation set containing 106 subjects (78 cancerous and 28 healthy), which represents 30% of the dataset. The remaining 70% was used as a training set, containing 244 subjects (126 cancerous and 118 healthy). After feature selection, the best signatures were identified on the training set with Random Forest method and validated on the validation set. Statistical analysis was performed with R-studio software. Results: We enrolled in our study 350 subjects, 204 breast cancer patients and 146 healthy controls. The median age in the breast cancer group was 56 years (range 26-86), and in the healthy controls group was 53 years (range 40-74). Breast cancer patients were all at an early stage: 44 at stage I (21.5%), 111 at stage II (54.4%), and 49 at stage III (24%). The breast cancer patients were of all subtypes: 61 luminal A (29.9%), 90 luminal B (44.1%), 14 hormone receptor-negative/HER2-positive (6.9%), and 39 triples negative (19.1%). A feature selection was performed on the training set using Random Forest method, and 10 metabolites were identified as the most important in discriminating cancerous from healthy subjects. From this reduced set, 1023 combinations were generated and evaluated for their AUC performance using 10-CV on the same training set. A total of 512 combinations were identified with an AUC ≧ 0.90. To predict breast cancers, the best signature comprised 4 variables (C6-Carnitine, C3/C2, C2-Carnitine, C8/C2), with an AUC of 0.996 (SD 0.0073) in the training set and of 0.998 (SD 0.0002) for the validation set, at a specificity of 99.4% and a sensitivity of 98.7%. Conclusions: With our work, we identified a metabolite-based predictive signature of breast cancer with a validation performance of AUC 0.99 (specificity of 99.4% and sensitivity of 98.7%), thus outperforming the mammography screening test. Furthermore, the signature-based test is fast, cheap, and does not expose patients to ionizing radiation. Our study’s limitation is a difficult application to clinical practices due to the statistical technique used. Thus, a refinement of the analysis technique and a validation on a larger and independent cohort are mandatory. Also, there are some differences in metabolism related to genetic, environmental factors, and feeding. Therefore, this result should be confirmed on different ethnicities, geographical regions, and the timing of blood withdrawal should be standardized.

Citation Format: Concetta Elisa Onesti, François Boemer, Claire Josse, Ahmed Debit, Christophe Poulet, Vincent Bours, Guy Jerusalem. A metabolomic signature as screening method for breast cancer diagnosis [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS17-01.