Level 4 TCGA RPPA data for each cancer type was downloaded from the TCPA Portal developed by the MD Anderson Cancer Center ⁴ . Across all proteins, individual values were scaled using Z-scores. A summary of information available for each cancer datasets is in Table 1. Additionally, uveal melanoma (UVM) was excluded from the datasets due to a low number of samples with RPPA quantification (n=12). Clinical and survival information for each cancer data set were downloaded from recently updated TCGA clinical data ⁵ . Overall survival, disease-specific survival, disease-free interval, and progression-free interval information was added to primary tumor RPPA quantifications for each cancer type. Unlike other cancer types, metastatic samples were kept in the skin cutaneous melanoma (SKCM) RPPA-based dataset due to the highly metastatic nature of the disease. SKCM in the TRGAted application consists of 96 primary tumor samples and 258 metastatic samples. Of the 8,167 samples available in the TCPA, overall survival (OS) data was available for 7,714 patients, disease-specific survival (DSS) data was available for 7,240 patients, disease-free interval (DFI) data was available for 3,887 patients, and progression-free interval (PFI) data was available for 7,315 patients ( Table 1).

The TRGAted application was written and tested using R v3.5.1. The interactive plots are made using shiny (v1.1.0) and ggplots2 (3.0.0). Plots can be downloaded as .png, .pdf, or .svg files. Data used to generate the individual plots can be downloaded as .csv files.

Operation: Minimum system requirements for running TRGAted locally are modest and include an Intel-compatible CPU and 1 gigabyte of RAM. Running TRGAted from the shiny server requires a modern browser and an internet connection.

Kaplan-Meier survival curves can be generated by selecting the cancer type, survival type and protein(s) of interest ( Figure 2). Kaplan-Meier curves are generated using the survival (v2.41-3) and the survminer (0.4-1) R packages. Multi-protein survival analysis utilizes mean values of protein probes, similar to gene-expression-based survival analysis platforms ⁶ . Hazard ratio for two-group comparisons, either median or optimal cut-off, utilize the Cox proportional hazards regression model in the survival R package; with the reported hazard ratio comparing high versus low protein groups. Optimal cut-off feature uses the surv_cutpoint function of the survminer package, calculating the minimal p-value based on the log-rank method. This function uses the maximally selected rank statistic ( maxstat, v0.7-25) R package, which finds the maximal standardized two-sample linear rank statistic ⁷ . In order to find clinically or biologically meaningful biomarkers, the minimal proportion cutpoint, or the maximal disparity comparison, was set at 15% versus 85% of samples. Clinical variables dependent on the cancer type selected, can be used to filter patients into user-defined groupings. Clinical information available across all types include: subtype, tumor stage, histological type, gender, age, response to primary therapy.

TRGAted also allows for Cox proportional hazard modeling across all proteins in each cancer type or for a single protein across all cancer types. Hazard ratios and P values are based on the Cox regression model. Values filtered from the volcano plots are proteins with –log10(p-values) less than 0.1 and hazard ratios greater than 20. These filters were implemented to improve visualization and to reduce artifacts of the analysis pipeline, respectively. The volcano plot can be graphed as linear or natural-log transformed, to assist in the visualization of good prognostic indicators. Visualizing the proportional comparison for the volcano plots is also available.

In order to demonstrate the functionality of TRGAted, we present a basic survival analysis of examining the aggressive, highly-metastatic subtype of breast cancer, known as basal-like breast cancer. We found in this cancer, RAD50, involved in homologous recombination of DNA, as a novel poor prognostic marker.

Survival curves: Survival curves can be generated by selecting the cancer type, survival type, and protein or proteins of interest ( Figure 2A). We also selected the subtype information to more closely examine basal-like breast cancer. Other survival types and clinical variables can be selected ( Figure 2B). The division of samples is available into quartiles, tertiles, median or optimum based on the protein of interest ( Figure 2C). Here we can see that the DNA repair protein, RAD50 is a poor prognostic marker for overall ( Figure 2A) and disease-specific survival ( Figure 2B) in basal-like breast cancer.

Across cancer: TRGAted can be used for biomarker discovery by examining the hazard ratios for all proteins available by cancer subtype, like basal-like breast cancer ( Figure 3A). The volcano plot displays good prognostic markers on the left in blue and poor prognostic markers on the right in red. Having selected the optimal cutoff feature, a bar chart can also be generated to examine the proportion of samples in the high and low proportion groups ( Figure 3B). Protein labeling is adaptive for both the volcano plot and bar chart and will only label significant proteins. Here we see the RAD50 is one of the most significant predictors of poor overall survival in basal-like breast cancer ( Figure 3A and B).

Across protein: TRGAted can also be used to examine the survival outcomes of a protein of interest across multiple cancers. Here, RAD50 predicts poor survival in only five cancer types, prostate, adrenocortical, breast cancer, low-grade glioma and head and neck cancers ( Figure 4A). A summary of the hazard ratios can also be visualized by selecting for the barplot function ( Figure 4B).