title: Computer assisted quantitative immunofluorescence of tumor tissue marker expression and clinical outcome to chemotherapy in advanced breast cancer patients.
abstract: Chemotherapy is frequently used in the treatment of advanced breast cancer. The identification of patient-specific tumor characteristics that can improve the ability to predict response to chemotherapy would help optimize advanced breast cancer treatment approaches. Quantitative immunofluorescence (QIF) may be applied to the standardization of protein analysis, resulting in increased sensitivity and reproducibility. In the current pilot study, QIF was used to correlate the expression of beta tubulin III and thymidylate synthase with clinical outcome associated with taxane and capecitabine treatment, respectively. QIF analysis is based on fluorescent dye-labeled monoclonal antibody staining followed by computer-assisted microscopy to measure the expression of molecular markers in tumor samples derived from a retrospective database. The interpretation of the tumor marker expression levels results in classification of breast tumors as sensitive or resistant to a mechanistically related drug. Overall diagnostic accuracy of QIF for taxane based therapy was 88% (CI 75.0 - 95.3) with a positive predictive value of 86% and a negative predictive value of 100%, while diagnostic accuracy QIF for capecitabine therapy was 86% (CI 88.0-96.0) with a positive predictive value of 80% and a negative predictive value of 100%. In this study, QIF showed retrospectively a potential for predictive value when analyzing chemotherapeutic treatments for individual advanced stage breast cancer patients. The predictive power of the QIF for chemotherapy confirms that further studies utilizing larger clinical cohorts are warranted.
authors.affiliation: Greenebaum Cancer Center, University of Maryland School of Medicine, Baltimore, USA.
authors.forename: Katherine H Rak
authors.initials: KH
authors.lastname: Tkaczuk
authors.forename: Nancy S
authors.initials: NS
authors.lastname: Tait
authors.forename: Olga
authors.initials: O
authors.lastname: Ioffe
authors.forename: Ming
authors.initials: M
authors.lastname: Tan
authors.forename: Olga G
authors.initials: OG
authors.lastname: Goloubeva
authors.forename: Stephen A
authors.initials: SA
authors.lastname: Lesko
authors.forename: Scott F
authors.initials: SF
authors.lastname: Deamond
authors.forename: Daobiao
authors.initials: D
authors.lastname: Zhou
authors.forename: Zheng P
authors.initials: ZP
authors.lastname: Lum
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Sutula
authors.forename: David
authors.initials: D
authors.lastname: Van Echo
authors.forename: Paul O P
authors.initials: PO
authors.lastname: Ts'o
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000970:Antineoplastic Agents / Q000627:therapeutic use*; D014408:Biomarkers, Tumor / Q000378:metabolism*; D001943:Breast Neoplasms / Q000145:classification / Q000188:drug therapy* / Q000473:pathology*; D005260:Female; D005455:Fluorescent Antibody Technique / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008875:Middle Aged; D009367:Neoplasm Staging; D016896:Treatment Outcome
chemical_list: D000970:Antineoplastic Agents; D014408:Biomarkers, Tumor