title: Optimizing of MALDI-ToF-based low-molecular-weight serum proteome pattern analysis in detection of breast cancer patients; the effect of albumin removal on classification performance.
abstract: Mass spectrometry-based analysis of the serum proteome allows identifying multi-peptide patterns/signatures specific for blood of cancer patients, thus having high potential value for cancer diagnostics. However, because of problems with optimization and standardization of experimental and computational design, none of identified proteome patterns/signatures was approved for diagnostics in clinical practice as yet. Here we compared two methods of serum sample preparation for mass spectrometry-based proteome pattern analysis aimed to identify biomarkers that could be used in early detection of breast cancer patients. Blood samples were collected in a group of 92 patients diagnosed at early (I and II) stages of the disease before the start of therapy, and in a group of age-matched healthy controls (104 women). Serum specimens were purified and analyzed using MALDI-ToF spectrometry, either directly or after membrane filtration (50 kDa cut-off) to remove albumin and other large serum proteins. Mass spectra of the low-molecular-weight fraction (2-10 kDa) of the serum proteome were resolved using the Gaussian mixture decomposition, and identified spectral components were used to build classifiers that differentiated samples from breast cancer patients and healthy persons. Mass spectra of complete serum and membrane-filtered albumin-depleted samples have apparently different structure and peaks specific for both types of samples could be identified. The optimal classifier built for the complete serum specimens consisted of 8 spectral components, and had 81% specificity and 72% sensitivity, while that built for the membrane-filtered samples consisted of 4 components, and had 80% specificity and 81% sensitivity. We concluded that pre-processing of samples to remove albumin might be recommended before MALDI-ToF mass spectrometric analysis of the low-molecular-weight components of human serum Keywords: albumin removal; breast cancer; clinical proteomics; mass spectrometry; pattern analysis; serum proteome.
authors.affiliation: Maria Sklodowska-Curie Memorial Cancer Center, Gliwice, Poland.
authors.forename: M
authors.initials: M
authors.lastname: Pietrowska
authors.forename: L
authors.initials: L
authors.lastname: Marczak
authors.forename: J
authors.initials: J
authors.lastname: Polanska
authors.forename: E
authors.initials: E
authors.lastname: Nowicka
authors.forename: K
authors.initials: K
authors.lastname: Behrent
authors.forename: R
authors.initials: R
authors.lastname: Tarnawski
authors.forename: M
authors.initials: M
authors.lastname: Stobiecki
authors.forename: A
authors.initials: A
authors.lastname: Polanski
authors.forename: P
authors.initials: P
authors.lastname: Widlak
mesh_terms: D000328:Adult; D000368:Aged; D001798:Blood Proteins / Q000032:analysis*; D001943:Breast Neoplasms / Q000097:blood / Q000145:classification / Q000175:diagnosis*; D005260:Female; D006801:Humans; D008875:Middle Aged; D008970:Molecular Weight; D020543:Proteome; D012680:Sensitivity and Specificity; D012709:Serum Albumin / Q000302:isolation & purification*; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods*
chemical_list: D001798:Blood Proteins; D020543:Proteome; D012709:Serum Albumin