17 research outputs found
APOSTL: An Interactive Galaxy Pipeline for Reproducible Analysis of Affinity Proteomics Data
With
continuously increasing scale and depth of coverage in affinity
proteomics (AP–MS) data, the analysis and visualization is
becoming more challenging. A number of tools have been developed to
identify high-confidence interactions; however, a cohesive and intuitive
pipeline for analysis and visualization is still needed. Here we present
Automated Processing of SAINT Templated Layouts (APOSTL), a freely
available Galaxy-integrated software suite and analysis pipeline for
reproducible, interactive analysis of AP–MS data. APOSTL contains
a number of tools woven together using Galaxy workflows, which are
intuitive for the user to move from raw data to publication-quality
figures within a single interface. APOSTL is an evolving software
project with the potential to customize individual analyses with additional
Galaxy tools and widgets using the R web application framework, Shiny.
The source code, data, and documentation are freely available from
GitHub (https://github.com/bornea/APOSTL) and other sources
Multivariable Cox Proportional Hazards Models for Progression Free and Overall Survival for the Screen-Detected Cohorts.
<p>Multivariable Cox Proportional Hazards Models for Progression Free and Overall Survival for the Screen-Detected Cohorts.</p
Baseline Demographics and Clinical Characteristics of Four Screen-Detected Lung Cancer Case Cohorts.
<p>Baseline Demographics and Clinical Characteristics of Four Screen-Detected Lung Cancer Case Cohorts.</p
A Pilot Proteogenomic Study with Data Integration Identifies MCT1 and GLUT1 as Prognostic Markers in Lung Adenocarcinoma
<div><p>We performed a pilot proteogenomic study to compare lung adenocarcinoma to lung squamous cell carcinoma using quantitative proteomics (6-plex TMT) combined with a customized Affymetrix GeneChip. Using MaxQuant software, we identified 51,001 unique peptides that mapped to 7,241 unique proteins and from these identified 6,373 genes with matching protein expression for further analysis. We found a minor correlation between gene expression and protein expression; both datasets were able to independently recapitulate known differences between the adenocarcinoma and squamous cell carcinoma subtypes. We found 565 proteins and 629 genes to be differentially expressed between adenocarcinoma and squamous cell carcinoma, with 113 of these consistently differentially expressed at both the gene and protein levels. We then compared our results to published adenocarcinoma versus squamous cell carcinoma proteomic data that we also processed with MaxQuant. We selected two proteins consistently overexpressed in squamous cell carcinoma in all studies, MCT1 (SLC16A1) and GLUT1 (SLC2A1), for further investigation. We found differential expression of these same proteins at the gene level in our study as well as in other public gene expression datasets. These findings combined with survival analysis of public datasets suggest that MCT1 and GLUT1 may be potential prognostic markers in adenocarcinoma and druggable targets in squamous cell carcinoma. Data are available via ProteomeXchange with identifier PXD002622.</p></div
Schema for the lung cancer case cohorts in the NLST at prevalence (baseline) and incidence screening (follow-up) rounds.
<p>Abbreviations: T0 = baseline screen; T1 = first screen; T2 = second screen; [+] = positive screen; [–] = negative screen. Prevalence lung cancers diagnosed at T0 are shaded green. Screen-detected lung cancers diagnosed at T1 and T2 rounds in which T0 screens were positive are shaded blue. Screen-detected lung cancers diagnosed at T1 and T2 in which T0 screens were negative are shaded red. Interval cancers (beige boxes) were diagnosed following a negative screen. Participants were excluded if their screening results were inadequate or not compliant. At baseline we excluded 413 screens, 1,471 screens at T1, and 713 screens at T2.</p
Kaplan-Meier estimates and number of subjects at risk for (A) progression free survival and (B) overall survival in the prevalence-, interval-, and grouped screen-detected lung cancers.
<p>Abbreviations: SDLC1/2 = Combined screen-detected lung cancers following positive screens at T1 and T2 rounds in cohorts with T0 positive screens; SDLC3/4 = Combined screen-detected lung cancers following positive screens at T1 and T2 rounds among cohorts with T0 or T0 and T1 negative screens; PC = Prevalence lung cancer cohort; IC = Interval cancer cohort.</p
Clinical Characteristics and Outcomes of the Grouped Screen-Detected, Prevalence, and Interval Cancer Cohorts.
<p>Clinical Characteristics and Outcomes of the Grouped Screen-Detected, Prevalence, and Interval Cancer Cohorts.</p
Baseline Demographic of the Grouped Screen-Detected, Prevalence, and Interval Cancer Cohorts.
<p>Baseline Demographic of the Grouped Screen-Detected, Prevalence, and Interval Cancer Cohorts.</p
Comparison with Existing NSCLC Proteomic Datasets.
<p>Mean intensities are given in log<sub>2</sub> scale. (A) The correlations between reporter ion intensities and peptide intensities from Kikuchi et al. were low (R = 0.3, <i>P</i> < 2.2E-16; ρ = 0.26, <i>P</i> < 2.2E-16). (B) As with the Kikuchi et al. data, correlations between reporter ion intensities and peptide intensities from Li et al. were also low (R = 0.23, <i>P</i> < 2.2E-16; ρ = 0.21, <i>P</i> < 2.2E-16).</p