Mass spectrometry-based analysis of therapy-related changes in serum proteome patterns of patients with early-stage breast cancer

<p>Abstract</p> <p>Background</p> <p>The proteomics approach termed proteome pattern analysis has been shown previously to have potential in the detection and classification of breast cancer. Here we aimed to identify changes in serum proteome patterns related to therapy of breast cancer patients.</p> <p>Methods</p> <p>Blood samples were collected before the start of therapy, after the surgical resection of tumors and one year after the end of therapy in a group of 70 patients diagnosed at early stages of the disease. Patients were treated with surgery either independently (26) or in combination with neoadjuvant chemotherapy (5) or adjuvant radio/chemotherapy (39). The low-molecular-weight fraction of serum proteome was examined using MALDI-ToF mass spectrometry, and then changes in intensities of peptide ions registered in a mass range between 2,000 and 14,000 Da were identified and correlated with clinical data.</p> <p>Results</p> <p>We found that surgical resection of tumors did not have an immediate effect on the mass profiles of the serum proteome. On the other hand, significant long-term effects were observed in serum proteome patterns one year after the end of basic treatment (we found that about 20 peptides exhibited significant changes in their abundances). Moreover, the significant differences were found primarily in the subgroup of patients treated with adjuvant therapy, but not in the subgroup subjected only to surgery. This suggests that the observed changes reflect overall responses of the patients to the toxic effects of adjuvant radio/chemotherapy. In line with this hypothesis we detected two serum peptides (registered m/z values 2,184 and 5,403 Da) whose changes correlated significantly with the type of treatment employed (their abundances decreased after adjuvant therapy, but increased in patients treated only with surgery). On the other hand, no significant correlation was found between changes in the abundance of any spectral component or clinical features of patients, including staging and grading of tumors.</p> <p>Conclusions</p> <p>The study establishes a high potential of MALDI-ToF-based analyses for the detection of dynamic changes in the serum proteome related to therapy of breast cancer patients, which revealed the potential applicability of serum proteome patterns analyses in monitoring the toxicity of therapy.</p

Sparse Proteomics Analysis - A compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data

Background: High-throughput proteomics techniques, such as mass spectrometry (MS)-based approaches, produce very high-dimensional data-sets. In a clinical setting one is often interested in how mass spectra differ between patients of different classes, for example spectra from healthy patients vs. spectra from patients having a particular disease. Machine learning algorithms are needed to (a) identify these discriminating features and (b) classify unknown spectra based on this feature set. Since the acquired data is usually noisy, the algorithms should be robust against noise and outliers, while the identified feature set should be as small as possible. Results: We present a new algorithm, Sparse Proteomics Analysis (SPA), based on the theory of compressed sensing that allows us to identify a minimal discriminating set of features from mass spectrometry data-sets. We show (1) how our method performs on artificial and real-world data-sets, (2) that its performance is competitive with standard (and widely used) algorithms for analyzing proteomics data, and (3) that it is robust against random and systematic noise. We further demonstrate the applicability of our algorithm to two previously published clinical data-sets

Classification of Maldi-tof Mass Spectrometry Data in the Analysis of Cancer Patients

The article presents a case study of Maldi-Tof (Matrix-Assisted Laser Desorption Ionization – Time Of Flight) data analysis and classification. Row mass spectrometry data are preprocessed and decomposed with Gaussian Mixture Model. Gaussian mask is calculated and put at all spectra separately. In further dimension reduction RFE, PLS and T test are used. The classification is done with Support Vector Machine (SVM) method with Gaussian Radial Basis Function kernel.Запропонована класифікація мас-спектрометричних даних медичних досліджень Maldi-Tof, алгоритми та програми комп’ютерного моделювання, які використовуються в проблематиці діагностики і лікування ракових захворювань.Предложена классификация масс-спектрометрических данных медицинских исследований Maldi-Tof, алгоритмы и программы компьютерного моделирования, которые используются в проблемах диагностики и лечения раковых заболеваний