Figure 1.
Proteomic computational pipeline synopsis.
The 3-stage computational pipeline enables an initial untargeted exploration of the plasma proteome resulting in a list of potential biomarkers, followed by the validation of a set of candidate biomarkers that emphasizes the combination of candidate protein biomarkers into a classifier score with clinical utility. The bottom panel outlines the main steps of the computational pipeline that provide a systematic process from discovery to validation to clinical implementation of plasma protein biomarkers.
Table 1.
Proteomics biomarker study schematic.
Table 2.
Panel of plasma proteins with differential relative levels between acute rejection and non-rejection samples.
Table 3.
Identification of protein groups in the panel.
Table 4.
Confounding factors.
Figure 2.
Average Linear Discriminant Analysis classifier score (Classifier score) for all available acute rejection (AR) samples (pink solid point), and non-rejection (NR) samples from NR patients (green solid triangle), at each time point. The score was centered at the LDA cut-off point so that samples with positive and negative scores are classified as “rejection” or “non-rejection”, respectively. Vertical lines represent standard errors. Means and standard error bars can be used to assess differences of the score between groups at any of the studied time points. Sample sizes available at each time point are shown in the bottom table.
Figure 3.
Linear Discriminant Analysis classifier score (Classifier score) when patients transitioned between non-rejection (NR) and acute rejection (AR) episodes. The first consecutive AR time points were averaged (AR, pink solid point) from 7 AR patients. Non-rejection samples from the same patients, before and after AR (“NR before AR” and “NR after AR”) were averaged (pink solid triangle). The average time trend for these samples is represented with a pink solid line. A control curve (dashed green line) was constructed from 9 NR patients matched to AR patients by available time points (green solid triangle). Vertical lines represent standard errors. The asterisk (*) means that the two-sided t-test p value<0.001.
Figure 4.
A. For both ELISA/INA and MRM-MS corroboration analysis, p values were calculated by robust eBayes (12 acute rejection (AR) versus 18 non-rejection (NR) samples in ELISA/INA, and 6 AR versus 11 NR in MRM-MS, two-sided test). The correlations among platforms are based on all available common samples, i.e., 25 samples measured by iTRAQ and ELISA/INA, and 23 samples measured by iTRAQ, ELISA/INA, and MRM-MS (Figure S2). B. Validation performance (y-axis) estimated by a 6-fold cross-validation: sensitivity (blue diamond), specificity (brown square), and area under the receiver operating curve (AUC) (red star) for incremental classifier panels. The sensitivity and specificity estimates were calculated using a probability cut-off of 0.5. The x-axis shows three nested classifier panels based on a single candidate marker (B2M), 2 markers (B2M&ADIPOQ) and 3 markers (B2M&ADIPOQ&CP), respectively, measured by MRM-MS. As F10 was not validated in either ELISA/INA or MRM-MS, it was not included in any MRM-based classifier. C. MRM-MS classifier score generated by a 6-fold cross-validation using Linear Discriminant Analysis. Samples with a positive proteomic classifier score are classified as “rejection” and those with a negative score are classified as “non-rejection”.