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

Pietrowska M, Polanska J, Marczak L, Behrendt K, Nowicka E, Stobiecki M, Polanski A, Tarnawski R, Widlak P - J Transl Med (2010)

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Maria Skłodowska-Curie Memorial Cancer Center and Institute of Oncology, Gliwice, Poland.

ABSTRACT

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

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Related in: MedlinePlus

Example of time course-related changes in the abundances of spectral components. A - Individual time courses of changes in the abundance of spectral components registered at the approximate m/z value 9419 Da in samples collected from 70 patients at three time points (dots connected with black lines); blue lines represent the average for all patients. B - Box-plots represent quantification of differences in the abundance of the 9419 Da spectral component in samples collected for each of 70 patients between three time points; shown are minimum, lower quartile, median, upper quartile, maximum values and outlier marked with asterisk (q-values of the significance of differences were 0.856, 0.024 and 0.065 for B-A, C-B and C-A, respectively). C - Cluster that contained spectral components registered at approximate m/z values 4211, 6428 and 9419 Da. For each of three components shown are average intensities for samples collected from 70 patients at different time points (dots connected with black lines), the blue line represents averages for all components; the cluster represents [A≥B<C] type.
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Figure 3: Example of time course-related changes in the abundances of spectral components. A - Individual time courses of changes in the abundance of spectral components registered at the approximate m/z value 9419 Da in samples collected from 70 patients at three time points (dots connected with black lines); blue lines represent the average for all patients. B - Box-plots represent quantification of differences in the abundance of the 9419 Da spectral component in samples collected for each of 70 patients between three time points; shown are minimum, lower quartile, median, upper quartile, maximum values and outlier marked with asterisk (q-values of the significance of differences were 0.856, 0.024 and 0.065 for B-A, C-B and C-A, respectively). C - Cluster that contained spectral components registered at approximate m/z values 4211, 6428 and 9419 Da. For each of three components shown are average intensities for samples collected from 70 patients at different time points (dots connected with black lines), the blue line represents averages for all components; the cluster represents [A≥B<C] type.

Mentions: Based on the abundance of each spectral component registered in serum samples collected at different time points for each patient, individual "time courses" were established. Then, average time courses were computed for each spectral component, which characterized its general behavior in samples from a group of patients. Such average time courses were used in cluster analysis to extract spectral components whose abundance in samples changed in a specific way. We separated 30 clusters, which number described the dataset optimally according to Bayesian information criterion [39] (not shown). Figure 3 shows an example of individual time courses of changes in abundance of the spectral component registered at approximate m/z value 9419 Da (putatively fragment of apolipoprotein C3), which differentiated samples B and C, and the 3-element cluster that contained this particular component. The cluster analysis was performed for the whole group of patients (n = 70) and the group of patients subjected to adjuvant therapy (n = 39); characteristics of identified clusters are shown in Table 2. As expected, the majority of spectral components belonged to a few clusters where the average abundance of components did not change significantly between consecutive time points (i.e., t-test p-value > 0.05 or average abundance changed for less than 5% in clusters with a few components). Such [A = B = C] type of clusters contained 78% and 63% of the spectral components when the group of all patients and patients subjected to adjuvant therapy were analyzed, respectively. Average abundance of several spectral components increased between samples collected after surgery (samples B) and one year after the end of therapy (samples C); these components formed [A<B<C] or [A≥B<C] types of clusters. These types of clusters consisted of 16% and 25% of the components for the group of all patients and patients subjected to adjuvant therapy, respectively. Fewer spectral components decreased their average abundance between samples B and C. These formed [A>B>C] or [A≤B>C] types of clusters, which consisted of 5% and 3% of the components for the group of all patients and patients subjected to adjuvant therapy, respectively. In line with data presented on Figure 1, the minority of spectral components changed their abundance between samples A and B but not between samples B and C, and belonged to [A ≠ B = C] types of clusters. These data showed that a substantial number of spectral components changed their abundance when analyzed in consecutive samples collected after surgery and one year after the end of therapy, and confirmed that such time-related changes are expressed predominantly in group of patients subjected to adjuvant therapy.


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

Pietrowska M, Polanska J, Marczak L, Behrendt K, Nowicka E, Stobiecki M, Polanski A, Tarnawski R, Widlak P - J Transl Med (2010)

Example of time course-related changes in the abundances of spectral components. A - Individual time courses of changes in the abundance of spectral components registered at the approximate m/z value 9419 Da in samples collected from 70 patients at three time points (dots connected with black lines); blue lines represent the average for all patients. B - Box-plots represent quantification of differences in the abundance of the 9419 Da spectral component in samples collected for each of 70 patients between three time points; shown are minimum, lower quartile, median, upper quartile, maximum values and outlier marked with asterisk (q-values of the significance of differences were 0.856, 0.024 and 0.065 for B-A, C-B and C-A, respectively). C - Cluster that contained spectral components registered at approximate m/z values 4211, 6428 and 9419 Da. For each of three components shown are average intensities for samples collected from 70 patients at different time points (dots connected with black lines), the blue line represents averages for all components; the cluster represents [A≥B<C] type.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2908576&req=5

Figure 3: Example of time course-related changes in the abundances of spectral components. A - Individual time courses of changes in the abundance of spectral components registered at the approximate m/z value 9419 Da in samples collected from 70 patients at three time points (dots connected with black lines); blue lines represent the average for all patients. B - Box-plots represent quantification of differences in the abundance of the 9419 Da spectral component in samples collected for each of 70 patients between three time points; shown are minimum, lower quartile, median, upper quartile, maximum values and outlier marked with asterisk (q-values of the significance of differences were 0.856, 0.024 and 0.065 for B-A, C-B and C-A, respectively). C - Cluster that contained spectral components registered at approximate m/z values 4211, 6428 and 9419 Da. For each of three components shown are average intensities for samples collected from 70 patients at different time points (dots connected with black lines), the blue line represents averages for all components; the cluster represents [A≥B<C] type.
Mentions: Based on the abundance of each spectral component registered in serum samples collected at different time points for each patient, individual "time courses" were established. Then, average time courses were computed for each spectral component, which characterized its general behavior in samples from a group of patients. Such average time courses were used in cluster analysis to extract spectral components whose abundance in samples changed in a specific way. We separated 30 clusters, which number described the dataset optimally according to Bayesian information criterion [39] (not shown). Figure 3 shows an example of individual time courses of changes in abundance of the spectral component registered at approximate m/z value 9419 Da (putatively fragment of apolipoprotein C3), which differentiated samples B and C, and the 3-element cluster that contained this particular component. The cluster analysis was performed for the whole group of patients (n = 70) and the group of patients subjected to adjuvant therapy (n = 39); characteristics of identified clusters are shown in Table 2. As expected, the majority of spectral components belonged to a few clusters where the average abundance of components did not change significantly between consecutive time points (i.e., t-test p-value > 0.05 or average abundance changed for less than 5% in clusters with a few components). Such [A = B = C] type of clusters contained 78% and 63% of the spectral components when the group of all patients and patients subjected to adjuvant therapy were analyzed, respectively. Average abundance of several spectral components increased between samples collected after surgery (samples B) and one year after the end of therapy (samples C); these components formed [A<B<C] or [A≥B<C] types of clusters. These types of clusters consisted of 16% and 25% of the components for the group of all patients and patients subjected to adjuvant therapy, respectively. Fewer spectral components decreased their average abundance between samples B and C. These formed [A>B>C] or [A≤B>C] types of clusters, which consisted of 5% and 3% of the components for the group of all patients and patients subjected to adjuvant therapy, respectively. In line with data presented on Figure 1, the minority of spectral components changed their abundance between samples A and B but not between samples B and C, and belonged to [A ≠ B = C] types of clusters. These data showed that a substantial number of spectral components changed their abundance when analyzed in consecutive samples collected after surgery and one year after the end of therapy, and confirmed that such time-related changes are expressed predominantly in group of patients subjected to adjuvant therapy.

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Maria Skłodowska-Curie Memorial Cancer Center and Institute of Oncology, Gliwice, Poland.

ABSTRACT

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Show MeSH
Related in: MedlinePlus