title: Discovery and preclinical validation of salivary transcriptomic and proteomic biomarkers for the non-invasive detection of breast cancer.
abstract: BACKGROUND
  A sensitive assay to identify biomarkers using non-invasively collected clinical specimens is ideal for breast cancer detection. While there are other studies showing disease biomarkers in saliva for breast cancer, our study tests the hypothesis that there are breast cancer discriminatory biomarkers in saliva using de novo discovery and validation approaches. This is the first study of this kind and no other study has engaged a de novo biomarker discovery approach in saliva for breast cancer detection. In this study, a case-control discovery and independent preclinical validations were conducted to evaluate the performance and translational utilities of salivary transcriptomic and proteomic biomarkers for breast cancer detection.
  
  
  RESULTS
  Salivary transcriptomes and proteomes of 10 breast cancer patients and 10 matched controls were profiled using Affymetrix HG-U133-Plus-2.0 Array and two-dimensional difference gel electrophoresis (2D-DIGE), respectively. Preclinical validations were performed to evaluate the discovered biomarkers in an independent sample cohort of 30 breast cancer patients and 63 controls using RT-qPCR (transcriptomic biomarkers) and quantitative protein immunoblot (proteomic biomarkers). Transcriptomic and proteomic profiling revealed significant variations in salivary molecular biomarkers between breast cancer patients and matched controls. Eight mRNA biomarkers and one protein biomarker, which were not affected by the confounding factors, were pre-validated, yielding an accuracy of 92% (83% sensitive, 97% specific) on the preclinical validation sample set.
  
  
  CONCLUSIONS
  Our findings support that transcriptomic and proteomic signatures in saliva can serve as biomarkers for the non-invasive detection of breast cancer. The salivary biomarkers possess discriminatory power for the detection of breast cancer, with high specificity and sensitivity, which paves the way for prediction model validation study followed by pivotal clinical validation.
authors.affiliation: School of Dentistry and Dental Research Institute, University of California Los Angeles, Los Angeles, California, United States of America. leizhang@ucla.edu
authors.forename: Lei
authors.initials: L
authors.lastname: Zhang
authors.forename: Hua
authors.initials: H
authors.lastname: Xiao
authors.forename: Scott
authors.initials: S
authors.lastname: Karlan
authors.forename: Hui
authors.initials: H
authors.lastname: Zhou
authors.forename: Jenny
authors.initials: J
authors.lastname: Gross
authors.forename: David
authors.initials: D
authors.lastname: Elashoff
authors.forename: David
authors.initials: D
authors.lastname: Akin
authors.forename: Xinmin
authors.initials: X
authors.lastname: Yan
authors.forename: David
authors.initials: D
authors.lastname: Chia
authors.forename: Beth
authors.initials: B
authors.lastname: Karlan
authors.forename: David T
authors.initials: DT
authors.lastname: Wong
mesh_terms: D015415:Biomarkers / Q000378:metabolism*; D014408:Biomarkers, Tumor / Q000378:metabolism*; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000378:metabolism*; D016022:Case-Control Studies; D017931:DNA Primers / Q000235:genetics; D005260:Female; D006801:Humans; D015151:Immunoblotting; D020543:Proteome; D040901:Proteomics / Q000379:methods*; D012333:RNA, Messenger / Q000378:metabolism; D020133:Reverse Transcriptase Polymerase Chain Reaction; D012463:Saliva / Q000378:metabolism; D012680:Sensitivity and Specificity; D014158:Transcription, Genetic
chemical_list: D015415:Biomarkers; D014408:Biomarkers, Tumor; D017931:DNA Primers; D020543:Proteome; D012333:RNA, Messenger