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Application of classifying limited sampling biopsies by matrix-assisted laser desorption/ionization time-of-flight (MALDI-ToF) based mass spectral profiling prior to liquid chromatography–tandem mass spectrometry (LC–MS/MS) analysis was studied. The trifluoroethanol (TFE) isolates from core needle biopsies from stage II (nonmetastatic) and stage IV (metastatic) breast tumors were subjected to trypsin digestion and the peptides were profiled on MALDI-ToF. The pvalue for TFE-based isolation method induced, intra- and inter-day variability (range 0.69–1.00) indicated reproducible protein isolation. MALDI-ToF mass spectrometry profiling of peptides showed five discriminant peaks with fold change > 2 or < 0.5 and pvalue < 0.05, between stage II and stage IV primary tumors. The supervised neural network (SNN) model showed the best recognition capability and cross validation with 91.67% and 69.69%, respectively. The models were validated using an independent sample set and found that genetic algorithm (GA) had the highest sensitivity and SNN had the highest specificity. LC–MS/MS was performed on the classified sample sets. One of the distinguishing peaks in the MALDI analysis (m/z1570.99) coincided with the peptide derived from vimentin, a known metastatic marker. Using western blotting, along with mimecan, the two markers showed differential levels between the groups validating the classification. MALDI-ToF/ToF-based peptide profiling of TFE lysates from core needle breast biopsy may serve as a screening tool for further deep proteomic analysis.

AbstractObjective: To differentiate plasma from ovarian cancer and healthy individuals using MALDI-TOF mass spectroscopy.Materials and methods: MALDI-TOF was used to generate profiles of immuno-depleted plasma samples (89 cancers and 199 healthy individuals) that were fractionated using three types of magnetic beads (HIC8, WCX and IMAC-Cu).Results: Differentially expressed mass ranges showing >1.5–2-fold change in expression from HIC8 (30), WCX (12) and IMAC-Cu (6) fractions were identified. Cross validation and recognition capability scores for the models indicated discrimination between the classes.Conclusions: Spectral profiles can differentiate plasma samples of ovarian cancer patients from healthy individuals.