Applications of MALDI-TOF mass spectrometry in clinical proteomics

New technologies for the detection of early-stage ovarian cancer are urgently needed. Pathological changes within an organ might be reflected in proteomic patterns in serum. We developed a bioinformatics tool and used it to identify proteomic patterns in serum that distinguish neoplastic from non-neoplastic disease within the ovary. Proteomic spectra were generated by mass spectroscopy (surface-enhanced laser desorption and ionisation). A preliminary "training" set of spectra derived from analysis of serum from 50 unaffected women and 50 patients with ovarian cancer were analysed by an iterative searching algorithm that identified a proteomic pattern that completely discriminated cancer from non-cancer. The discovered pattern was then used to classify an independent set of 116 masked serum samples: 50 from women with ovarian cancer, and 66 from unaffected women or those with non-malignant disorders. The algorithm identified a cluster pattern that, in the training set, completely segregated cancer from non-cancer. The discriminatory pattern correctly identified all 50 ovarian cancer cases in the masked set, including all 18 stage I cases. Of the 66 cases of non-malignant disease, 63 were recognised as not cancer. This result yielded a sensitivity of 100% (95% CI 93--100), specificity of 95% (87--99), and positive predictive value of 94% (84--99). These findings justify a prospective population-based assessment of proteomic pattern technology as a screening tool for all stages of ovarian cancer in high-risk and general populations.

Early diagnosis of gram-negative bloodstream infections, prompt identification of the infecting organism, and appropriate antibiotic therapy improve patient care outcomes and decrease health care expenditures. In an era of increasing antimicrobial resistance, methods to acquire and rapidly translate critical results into timely therapies for gram-negative bloodstream infections are needed. To determine whether mass spectrometry technology coupled with antimicrobial stewardship provides a substantially improved alternative to conventional laboratory methods. An evidence-based intervention that integrated matrix-assisted laser desorption and ionization time-of-flight mass spectrometry, rapid antimicrobial susceptibility testing, and near-real-time antimicrobial stewardship practices was implemented. Outcomes in patients hospitalized prior to initiation of the study intervention were compared to those in patients treated after implementation. Differences in length of hospitalization and hospital costs were assessed in survivors. The mean hospital length of stay in the preintervention group survivors (n = 100) was 11.9 versus 9.3 days in the intervention group (n = 101; P = .01). After multivariate analysis, factors independently associated with decreased length of hospitalization included the intervention (hazard ratio, 1.38; 95% confidence interval, 1.01-1.88) and active therapy at 48 hours (hazard ratio, 2.9; confidence interval, 1.15-7.33). Mean hospital costs per patient were $45 709 in the preintervention group and $26 162 in the intervention group (P = .009). Integration of rapid identification and susceptibility techniques with antimicrobial stewardship significantly improved time to optimal therapy, and it decreased hospital length of stay and total costs. This innovative strategy has ramifications for other areas of patient care.