title: Classifying patients for breast cancer by detection of autoantibodies against a panel of conformation-carrying antigens.
abstract: Patients with breast cancer elicit an autoantibody response against cancer proteins, which reflects and amplifies the cellular changes associated with tumorigenesis. Detection of autoantibodies in plasma may provide a minimally invasive mechanism for early detection of breast cancer. To identify cancer proteins that elicit a humoral response, we generated a cDNA library enriched for breast cancer genes that encode membrane and secreted proteins, which are more likely to induce an antibody response compared with intracellular proteins. To generate conformation-carrying antigens that are efficiently recognized by patients' antibodies, a eukaryotic expression strategy was established. Plasma from 200 patients with breast cancer and 200 age-matched healthy controls were measured for autoantibody activity against 20 different antigens designed to have conformational epitopes using ELISA. A conditional logistic regression model was used to select a combination of autoantibody responses against the 20 different antigens to classify patients with breast cancer from healthy controls. The best combination included ANGPTL4, DKK1, GAL1, MUC1, GFRA1, GRN, and LRRC15; however, autoantibody responses against GFRA1, GRN, and LRRC15 were inversely correlated with breast cancer. When the autoantibody responses against the 7 antigens were added to the base model, including age, BMI, race and current smoking status, the assay had the following diagnostic capabilities: c-stat (95% CI), 0.82 (0.78-0.86); sensitivity, 73%; specificity, 76%; and positive likelihood ratio (95% CI), 3.04 (2.34-3.94). The model was calibrated across risk deciles (Hosmer-Lemeshow, P = 0.13) and performed well in specific subtypes of breast cancer including estrogen receptor positive, HER-2 positive, invasive, in situ and tumor sizes >1 cm.
authors.affiliation: 2301 East 60th Street North, Sioux Falls, SD 57104. kristi.egland@sanfordhealth.org.
authors.forename: Rick L
authors.initials: RL
authors.lastname: Evans
authors.forename: James V
authors.initials: JV
authors.lastname: Pottala
authors.forename: Kristi A
authors.initials: KA
authors.lastname: Egland
mesh_terms: D065311:Adenocarcinoma in Situ / Q000097:blood / Q000145:classification / Q000175:diagnosis / Q000473:pathology; D000368:Aged; D000951:Antigens, Neoplasm / Q000097:blood* / Q000737:chemistry* / Q000276:immunology; D001323:Autoantibodies / Q000097:blood* / Q000276:immunology; D014408:Biomarkers, Tumor / Q000097:blood; D001943:Breast Neoplasms / Q000097:blood / Q000145:classification / Q000175:diagnosis*; D018270:Carcinoma, Ductal, Breast / Q000097:blood / Q000145:classification / Q000175:diagnosis / Q000473:pathology; D016022:Case-Control Studies; D002460:Cell Line; D055088:Early Detection of Cancer / Q000379:methods*; D000939:Epitopes / Q000097:blood / Q000276:immunology; D005260:Female; D057809:HEK293 Cells; D006801:Humans; D008875:Middle Aged; D011487:Protein Conformation; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000737:chemistry / Q000276:immunology; D014218:Triage / Q000379:methods*
chemical_list: D000951:Antigens, Neoplasm; D001323:Autoantibodies; D014408:Biomarkers, Tumor; D000939:Epitopes; D018719:Erb-b2 Receptor Tyrosine Kinases