An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

Abstract

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale.

Keywords: Cox proportional hazards regression model; TCGA; The Cancer Genome Atlas; clinical data resource; disease-free interval; disease-specific survival; follow-up time; overall survival; progression-free interval; translational research.

Figure 1. Clinical Data Analysis

(A–E) Flowchart (A) and K-M plots of the Pan-Cancer types for OS (B), PFI (C), DFI (D), and DSS (E) respectively. The tail of each K-M curve is truncated at the point when fewer than 10 patients remain at risk. See Figure S1 for plots of the 4 endpoints within each of the 33 tumor types and Tables S1, S2, S3 and S4 for more detailed information regarding endpoint derivation.

Figure 2. Validation and Application Examples

(A–D) Clinical survival outcomes of breast cancer patients with ER+ or ER− tumors using (A) OS, (B) PFI, (C) DFI, and (D) DSS as the endpoint, respectively. Plots were truncated at 10 years, but the analyses were conducted using all of the data. (E–G) Pairwise plots comparing natural logHRs for event development measured by three different clinical endpoints (OS, DSS, and PFI), with natural logHRs calculated for high-stage (III, IV) disease relative to low-stage (I, II) disease: (E) PFI versus OS, (F) PFI versus DSS, and (G) OS versus DSS. Here 14 diseases for which these three endpoints are recommended for use are analyzed. The blue diagonal lines illustrate equal logHRs measured by each endpoint pair. Error bars represents the standard errors of logHR. Red error bars show models that did not satisfy the PHs assumption.