10 research outputs found
MOESM2 of Residual tissue repositories as a resource for population-based cancer proteomic studies
Additional file 2: Table S2. Ă‚Â TMT labeling scheme for phosphoproteomics
MOESM7 of Residual tissue repositories as a resource for population-based cancer proteomic studies
Additional file 7: Table S6. Significantly affected GO terms
MOESM5 of Residual tissue repositories as a resource for population-based cancer proteomic studies
Additional file 5: Table S5. Ă‚Â Identified peptides, proteins, and phosphopeptides
MOESM4 of Residual tissue repositories as a resource for population-based cancer proteomic studies
Additional file 4: Table S4. Ă‚Â Table of specimen age and RTR
MOESM1 of Residual tissue repositories as a resource for population-based cancer proteomic studies
Additional file 1: Table S1. Ă‚Â TMT labeling scheme for expression proteomics
MOESM6 of Residual tissue repositories as a resource for population-based cancer proteomic studies
Additional file 6. Ă‚Â Supplementary figures
Comprehensive Quantitative Analysis of Ovarian and Breast Cancer Tumor Peptidomes
Aberrant degradation of proteins
is associated with many pathological
states, including cancers. Mass spectrometric analysis of tumor peptidomes,
the intracellular and intercellular products of protein degradation,
has the potential to provide biological insights on proteolytic processing
in cancer. However, attempts to use the information on these smaller
protein degradation products from tumors for biomarker discovery and
cancer biology studies have been fairly limited to date, largely due
to the lack of effective approaches for robust peptidomics identification
and quantification and the prevalence of confounding factors and biases
associated with sample handling and processing. Herein, we have developed
an effective and robust analytical platform for comprehensive analyses
of tissue peptidomes, which is suitable for high-throughput quantitative
studies. The reproducibility and coverage of the platform, as well
as the suitability of clinical ovarian tumor and patient-derived breast
tumor xenograft samples with postexcision delay of up to 60 min before
freezing for peptidomics analysis, have been demonstrated. Moreover,
our data also show that the peptidomics profiles can effectively separate
breast cancer subtypes, reflecting tumor-associated protease activities.
Peptidomics complements results obtainable from conventional bottom-up
proteomics and provides insights not readily obtainable from such
approaches
Comprehensive Quantitative Analysis of Ovarian and Breast Cancer Tumor Peptidomes
Aberrant degradation of proteins
is associated with many pathological
states, including cancers. Mass spectrometric analysis of tumor peptidomes,
the intracellular and intercellular products of protein degradation,
has the potential to provide biological insights on proteolytic processing
in cancer. However, attempts to use the information on these smaller
protein degradation products from tumors for biomarker discovery and
cancer biology studies have been fairly limited to date, largely due
to the lack of effective approaches for robust peptidomics identification
and quantification and the prevalence of confounding factors and biases
associated with sample handling and processing. Herein, we have developed
an effective and robust analytical platform for comprehensive analyses
of tissue peptidomes, which is suitable for high-throughput quantitative
studies. The reproducibility and coverage of the platform, as well
as the suitability of clinical ovarian tumor and patient-derived breast
tumor xenograft samples with postexcision delay of up to 60 min before
freezing for peptidomics analysis, have been demonstrated. Moreover,
our data also show that the peptidomics profiles can effectively separate
breast cancer subtypes, reflecting tumor-associated protease activities.
Peptidomics complements results obtainable from conventional bottom-up
proteomics and provides insights not readily obtainable from such
approaches
Comprehensive Quantitative Analysis of Ovarian and Breast Cancer Tumor Peptidomes
Aberrant degradation of proteins
is associated with many pathological
states, including cancers. Mass spectrometric analysis of tumor peptidomes,
the intracellular and intercellular products of protein degradation,
has the potential to provide biological insights on proteolytic processing
in cancer. However, attempts to use the information on these smaller
protein degradation products from tumors for biomarker discovery and
cancer biology studies have been fairly limited to date, largely due
to the lack of effective approaches for robust peptidomics identification
and quantification and the prevalence of confounding factors and biases
associated with sample handling and processing. Herein, we have developed
an effective and robust analytical platform for comprehensive analyses
of tissue peptidomes, which is suitable for high-throughput quantitative
studies. The reproducibility and coverage of the platform, as well
as the suitability of clinical ovarian tumor and patient-derived breast
tumor xenograft samples with postexcision delay of up to 60 min before
freezing for peptidomics analysis, have been demonstrated. Moreover,
our data also show that the peptidomics profiles can effectively separate
breast cancer subtypes, reflecting tumor-associated protease activities.
Peptidomics complements results obtainable from conventional bottom-up
proteomics and provides insights not readily obtainable from such
approaches
Quality Assessments of Long-Term Quantitative Proteomic Analysis of Breast Cancer Xenograft Tissues
Clinical
proteomics requires large-scale analysis of human specimens
to achieve statistical significance. We evaluated the long-term reproducibility
of an iTRAQ (isobaric tags for relative and absolute quantification)-based
quantitative proteomics strategy using one channel for reference across
all samples in different iTRAQ sets. A total of 148 liquid chromatography
tandem mass spectrometric (LC–MS/MS) analyses were completed,
generating six 2D LC–MS/MS data sets for human-in-mouse breast
cancer xenograft tissues representative of basal and luminal subtypes.
Such large-scale studies require the implementation of robust metrics
to assess the contributions of technical and biological variability
in the qualitative and quantitative data. Accordingly, we derived
a quantification confidence score based on the quality of each peptide-spectrum
match to remove quantification outliers from each analysis. After
combining confidence score filtering and statistical analysis, reproducible
protein identification and quantitative results were achieved from
LC–MS/MS data sets collected over a 7-month period. This study
provides the first quality assessment on long-term stability and technical
considerations for study design of a large-scale clinical proteomics
project