Highlights
- •The Cancer Genome Atlas (TCGA) analysis proposed multi-omics classifications for hepato-pancreato-biliary (HPB) cancers.
- •Status of 649 genes from whole-exome sequencing of about 2700 patients was analysed.
- •Artificial neural networks (ANNs) were developed to identify TCGA classifications.
- •ANNs proved to precisely predict TCGA cell-of-origin patterns and molecular subtypes.
- •TCGA classifications improved the ability to predict the prognosis of patients with HPB.
Abstract
Purpose
Several multi-omics classifications have been proposed for hepato-pancreato-biliary
(HPB) cancers, but these classifications have not proven their role in the clinical
practice and been validated in external cohorts.
Patients and methods
Data from whole-exome sequencing (WES) of The Cancer Genome Atlas (TCGA) patients
were used as an input for the artificial neural network (ANN) to predict the anatomical
site, iClusters (cell-of-origin patterns) and molecular subtype classifications. The
Ohio State University (OSU) and the International Cancer Genome Consortium (ICGC)
patients with HPB cancer were included in external validation cohorts. TCGA, OSU and
ICGC data were merged, and survival analyses were performed using both the ‘classic’
survival analysis and a machine learning algorithm (random survival forest).
Results
Although the ANN predicting the anatomical site of the tumour (i.e. cholangiocarcinoma,
hepatocellular carcinoma of the liver, pancreatic ductal adenocarcinoma) demonstrated
a low accuracy in TCGA test cohort, the ANNs predicting the iClusters (cell-of-origin
patterns) and molecular subtype classifications demonstrated a good accuracy of 75%
and 82% in TCGA test cohort, respectively. The random survival forest analysis and
Cox’ multivariable survival models demonstrated that models for HPB cancers that integrated
clinical data with molecular classifications (iClusters, molecular subtypes) had an
increased prognostic accuracy compared with standard staging systems.
Conclusion
The analyses of genetic status (i.e. WES, gene panels) of patients with HPB cancers
might predict the classifications proposed by TCGA project and help to select patients
suitable to targeted therapies. The molecular classifications of HPB cancers when
integrated with clinical information could improve the ability to predict the prognosis
of patients with HPB cancer.
Keywords
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Article info
Publication history
Published online: March 26, 2021
Accepted:
January 29,
2021
Received in revised form:
January 20,
2021
Received:
October 23,
2020
Identification
Copyright
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