Curation of the Pancreatic Ductal Adenocarcinoma Subset of the Cancer Genome Atlas Is Essential for Accurate Conclusions about Survival-Related Molecular Mechanisms

Abstract

Publicly available databases, for example, The Cancer Genome Atlas (TCGA), containing clinical and molecular data from many patients are useful in validating the contribution of particular genes to disease mechanisms and in forming novel hypotheses relating to clinical outcomes. The impact of key drivers of cancer progression can be assessed by segregating a patient cohort by certain molecular features and constructing survival plots using the associated clinical data. However, conclusions drawn from this straightforward analysis are highly dependent on the quality and source of tissue samples, as demonstrated through the pancreatic ductal adenocarcinoma (PDAC) subset of TCGA. Analyses of the PDAC-TCGA database, which contains mainly resectable cancer samples from patients in stage IIB, reveal a difference from widely known historic median and 5-year survival rates of PDAC. A similar discrepancy was observed in lung, stomach, and liver cancer subsets of TCGA. The whole transcriptome expression patterns of PDAC-TCGA revealed a cluster of samples derived from neuroendocrine tumors, which have a distinctive biology and better disease prognosis than PDAC. Furthermore, PDAC-TCGA contains numerous pseudo-normal samples, as well as those that arose from tumors not classified as PDAC. Inclusion of misclassified samples in the bioinformatic analyses distorts the association of molecular biomarkers with clinical outcomes, altering multiple published conclusions used to support and motivate experimental research. Hence, the stringent scrutiny of type and origin of samples included in the bioinformatic analyses by researchers, databases, and web-tool developers is of crucial importance for generating accurate conclusions. .

A Transcriptomics-Based Meta-Analysis Combined With Machine Learning Identifies a Secretory Biomarker Panel for Diagnosis of Pancreatic Adenocarcinoma

“…The normalized and annotated genes for TCGA was obtained from Broad GDAC Firehose database 6 . We have removed 29 non-PDAC samples from TCGA during validation as our classifier was trained using PDAC samples ( Peran et al, 2018 ).…”

“…To determine the association of key genes with survival in PC, we performed survival analysis using the TCGA database 7 . The survival analysis was performed on PDAC mRNA of 150 patients [excluding samples related to normal tissues and non-PDAC tissues ( Peran et al, 2018 )]. Survival analysis was performed on the basis of individual mRNA expression using the Kaplan-Meier (K-M) approach ( Kaplan and Meier, 1958 ).…”

Development and validation of a cancer stem cell-related signature for prognostic prediction in pancreatic ductal adenocarcinoma

“…All of these models have robust predictive ability, but they are not accurate enough, as most of them are derived from the single TCGA-PAAD dataset. A study showed that the failure to exclude non-PDAC samples from the TCGA-PAAD cohort might lead to false conclusions regarding the prognostic value of biomarkers [ 23 ]. Furthermore, the inclusion a large number of genes (up to 36) hinders the translation of predictive models into clinical application.…”

Genomewide identification of a novel six‐LncRNA signature to improve prognosis prediction in resectable hepatocellular carcinoma

“…Second, most researchers did not perform preanalysis case selection, whereas using an uncurated TCGA dataset without attention to sample characteristics can lead to false associations and undermine the application of the conclusions . The patients with the diagnosis of non‐HCC liver cancer (cholangiocarcinoma), pathological metastasis, and postsurgical residual carcinoma, the history of neoadjuvant treatment, and too short survival time are considered to have distinct biological procedures and progression mechanisms, and should be excluded from the prognostic analysis . Third, most studies performed the analysis by mixing resectable and unresectable cancers .…”