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Discovery and Validation of Predictive Biomarkers of Survival for Non-small Cell Lung Cancer Patients Undergoing Radical Radiotherapy: Two Proteins With Predictive Value.

Walker MJ, Zhou C, Backen A, Pernemalm M, Williamson AJ, Priest LJ, Koh P, Faivre-Finn C, Blackhall FH, Dive C, Whetton AD - EBioMedicine (2015)

Bottom Line: Identification of such markers would allow treatment options to be considered for more effective therapy.Plasma samples from patients pre- and during radiotherapy who had survived > 18 mo were compared to the same time points from patients who survived < 14 mo using an 8 channel isobaric tagging tandem mass spectrometry discovery proteomics platform.Over 650 proteins were detected and relatively quantified.

View Article: PubMed Central - PubMed

Affiliation: Stoller Biomarker Discovery Centre, Manchester Academic Health Science Centre, The University of Manchester, Wolfson Molecular Imaging Centre, Manchester M20 3LJ, UK.

ABSTRACT
Lung cancer is the most frequent cause of cancer-related death world-wide. Radiotherapy alone or in conjunction with chemotherapy is the standard treatment for locally advanced non-small cell lung cancer (NSCLC). Currently there is no predictive marker with clinical utility to guide treatment decisions in NSCLC patients undergoing radiotherapy. Identification of such markers would allow treatment options to be considered for more effective therapy. To enable the identification of appropriate protein biomarkers, plasma samples were collected from patients with non-small cell lung cancer before and during radiotherapy for longitudinal comparison following a protocol that carries sufficient power for effective discovery proteomics. Plasma samples from patients pre- and during radiotherapy who had survived > 18 mo were compared to the same time points from patients who survived < 14 mo using an 8 channel isobaric tagging tandem mass spectrometry discovery proteomics platform. Over 650 proteins were detected and relatively quantified. Proteins which showed a change during radiotherapy were selected for validation using an orthogonal antibody-based approach. Two of these proteins were verified in a separate patient cohort: values of CRP and LRG1 combined gave a highly significant indication of extended survival post one week of radiotherapy treatment.

No MeSH data available.


Related in: MedlinePlus

Discovery proteomics using longitudinal samples from NSCLC patients undergoing radiotherapy can distinguish patients with better survival. Patient samples were analysed using an isobaric tagging 2D LC MS/MS method described in the Materials and Methods. (a) The levels of all proteins relative to a pooled reference, quantified in all isobaric tagging mass spectrometry experiments (157 proteins) were analysed by unsupervised principal component analysis of all patient sample and control pools. The first and second principle components of the data were plotted in the figure with each data point an individual patient and time point. Colour represents an individual. Red, orange and purple survival > 18 mo, blue, green and cyan survival < 14 mo. The shape represents the time point; circle T1, square T2 and cross t3. (b) The log ratio of T3 to average of T1 and T2 of each protein for each patient was clustered. This cluster was visualised by plotting the data on a heat map with colour indicating the degree of difference calculated (black is reduced and yellow is increased). (c–e) Scatter plots of the three proteins (LRG1, CRP and LBP) changing significantly in the < 14 mo survival patients (blue circle) when compared to the > 18 mo survival (red square). All significance tests were two tailed unpaired t-tests.
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f0010: Discovery proteomics using longitudinal samples from NSCLC patients undergoing radiotherapy can distinguish patients with better survival. Patient samples were analysed using an isobaric tagging 2D LC MS/MS method described in the Materials and Methods. (a) The levels of all proteins relative to a pooled reference, quantified in all isobaric tagging mass spectrometry experiments (157 proteins) were analysed by unsupervised principal component analysis of all patient sample and control pools. The first and second principle components of the data were plotted in the figure with each data point an individual patient and time point. Colour represents an individual. Red, orange and purple survival > 18 mo, blue, green and cyan survival < 14 mo. The shape represents the time point; circle T1, square T2 and cross t3. (b) The log ratio of T3 to average of T1 and T2 of each protein for each patient was clustered. This cluster was visualised by plotting the data on a heat map with colour indicating the degree of difference calculated (black is reduced and yellow is increased). (c–e) Scatter plots of the three proteins (LRG1, CRP and LBP) changing significantly in the < 14 mo survival patients (blue circle) when compared to the > 18 mo survival (red square). All significance tests were two tailed unpaired t-tests.

Mentions: Relative quantification on 658 proteins with a peptide FDR less the 0.05% on identification (Supplementary Table 3, Supplementary Table 4, Supplementary Table 5) was derived. The proteins identified were enriched for classical plasma protein pathways such as the complement cascade and acute phase response proteins as is standardly seen in such studies. The within and between person variation in plasma proteome during radiotherapy was investigated using unsupervised principal component analysis (PCA) of proteins quantified in all samples (Fig. 2a). By plotting the first and second principal component we showed that the largest variation in the dataset was between different patients; as was anticipated from our previous studies, validating our decision to use a workflow that only permits longitudinal analysis. This PCA also demonstrated that the two survival group (< 14 mo and > 18 mo) could be separated by their first two components, indicating that a proteomic biomarker could be used to discriminate between these patients. We observed that the largest intra-person variation after radiotherapy was observed in the three patients with survival < 14 mo (Fig. 2a). In order to investigate the plasma proteome dataset further we generated a heat map (Fig. 2b) taking the change during radiotherapy for each protein and patient. The clustering of protein did not show any pathways regulated in either patients group.


Discovery and Validation of Predictive Biomarkers of Survival for Non-small Cell Lung Cancer Patients Undergoing Radical Radiotherapy: Two Proteins With Predictive Value.

Walker MJ, Zhou C, Backen A, Pernemalm M, Williamson AJ, Priest LJ, Koh P, Faivre-Finn C, Blackhall FH, Dive C, Whetton AD - EBioMedicine (2015)

Discovery proteomics using longitudinal samples from NSCLC patients undergoing radiotherapy can distinguish patients with better survival. Patient samples were analysed using an isobaric tagging 2D LC MS/MS method described in the Materials and Methods. (a) The levels of all proteins relative to a pooled reference, quantified in all isobaric tagging mass spectrometry experiments (157 proteins) were analysed by unsupervised principal component analysis of all patient sample and control pools. The first and second principle components of the data were plotted in the figure with each data point an individual patient and time point. Colour represents an individual. Red, orange and purple survival > 18 mo, blue, green and cyan survival < 14 mo. The shape represents the time point; circle T1, square T2 and cross t3. (b) The log ratio of T3 to average of T1 and T2 of each protein for each patient was clustered. This cluster was visualised by plotting the data on a heat map with colour indicating the degree of difference calculated (black is reduced and yellow is increased). (c–e) Scatter plots of the three proteins (LRG1, CRP and LBP) changing significantly in the < 14 mo survival patients (blue circle) when compared to the > 18 mo survival (red square). All significance tests were two tailed unpaired t-tests.
© Copyright Policy - CC BY
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4563120&req=5

f0010: Discovery proteomics using longitudinal samples from NSCLC patients undergoing radiotherapy can distinguish patients with better survival. Patient samples were analysed using an isobaric tagging 2D LC MS/MS method described in the Materials and Methods. (a) The levels of all proteins relative to a pooled reference, quantified in all isobaric tagging mass spectrometry experiments (157 proteins) were analysed by unsupervised principal component analysis of all patient sample and control pools. The first and second principle components of the data were plotted in the figure with each data point an individual patient and time point. Colour represents an individual. Red, orange and purple survival > 18 mo, blue, green and cyan survival < 14 mo. The shape represents the time point; circle T1, square T2 and cross t3. (b) The log ratio of T3 to average of T1 and T2 of each protein for each patient was clustered. This cluster was visualised by plotting the data on a heat map with colour indicating the degree of difference calculated (black is reduced and yellow is increased). (c–e) Scatter plots of the three proteins (LRG1, CRP and LBP) changing significantly in the < 14 mo survival patients (blue circle) when compared to the > 18 mo survival (red square). All significance tests were two tailed unpaired t-tests.
Mentions: Relative quantification on 658 proteins with a peptide FDR less the 0.05% on identification (Supplementary Table 3, Supplementary Table 4, Supplementary Table 5) was derived. The proteins identified were enriched for classical plasma protein pathways such as the complement cascade and acute phase response proteins as is standardly seen in such studies. The within and between person variation in plasma proteome during radiotherapy was investigated using unsupervised principal component analysis (PCA) of proteins quantified in all samples (Fig. 2a). By plotting the first and second principal component we showed that the largest variation in the dataset was between different patients; as was anticipated from our previous studies, validating our decision to use a workflow that only permits longitudinal analysis. This PCA also demonstrated that the two survival group (< 14 mo and > 18 mo) could be separated by their first two components, indicating that a proteomic biomarker could be used to discriminate between these patients. We observed that the largest intra-person variation after radiotherapy was observed in the three patients with survival < 14 mo (Fig. 2a). In order to investigate the plasma proteome dataset further we generated a heat map (Fig. 2b) taking the change during radiotherapy for each protein and patient. The clustering of protein did not show any pathways regulated in either patients group.

Bottom Line: Identification of such markers would allow treatment options to be considered for more effective therapy.Plasma samples from patients pre- and during radiotherapy who had survived > 18 mo were compared to the same time points from patients who survived < 14 mo using an 8 channel isobaric tagging tandem mass spectrometry discovery proteomics platform.Over 650 proteins were detected and relatively quantified.

View Article: PubMed Central - PubMed

Affiliation: Stoller Biomarker Discovery Centre, Manchester Academic Health Science Centre, The University of Manchester, Wolfson Molecular Imaging Centre, Manchester M20 3LJ, UK.

ABSTRACT
Lung cancer is the most frequent cause of cancer-related death world-wide. Radiotherapy alone or in conjunction with chemotherapy is the standard treatment for locally advanced non-small cell lung cancer (NSCLC). Currently there is no predictive marker with clinical utility to guide treatment decisions in NSCLC patients undergoing radiotherapy. Identification of such markers would allow treatment options to be considered for more effective therapy. To enable the identification of appropriate protein biomarkers, plasma samples were collected from patients with non-small cell lung cancer before and during radiotherapy for longitudinal comparison following a protocol that carries sufficient power for effective discovery proteomics. Plasma samples from patients pre- and during radiotherapy who had survived > 18 mo were compared to the same time points from patients who survived < 14 mo using an 8 channel isobaric tagging tandem mass spectrometry discovery proteomics platform. Over 650 proteins were detected and relatively quantified. Proteins which showed a change during radiotherapy were selected for validation using an orthogonal antibody-based approach. Two of these proteins were verified in a separate patient cohort: values of CRP and LRG1 combined gave a highly significant indication of extended survival post one week of radiotherapy treatment.

No MeSH data available.


Related in: MedlinePlus