Liver Cancer

Second Leading Cancer

Increasing incidence

Increasing mortality

Aggravated by various factors

Heterogeneity is high

Makes prediction challenging

Has 2 to 6 subtypes

Identified by various data types

mRNA

miRNA

DNA methylation

Most studies are for identification

Lacked survival prediction

Proposal to use deep learning framworks

Autoencoders for multi-omics integration

The Cancer Genome Atlas (TCGA)

Dataset used

mRNA expression

miRNA expression

CPG methylation

Clinical information

Methods and Dataasets

TCGA Dataset

Used for label extraction

Used for training a Support Vector Machine (SVM)

Additional validations using multiple cohorts

Chinese cohort

E-TABM-36

NCI cohort

Hawaiian cohort

LIRI-JP Cohort

RNA-Seq

gene expression

miRNA expression

gene expression

DNA methylation array

Used three pre-processed datasets

Feature extraction using autoencoders

Feature Selection

Univariate Cox proportional hazards

log-rank < 0.05

K-means clustering

Metrics and other frameworks

Evaluation metrics

Log-rank P value of Cox-PH regression

Brier Score

Concordance index

Correct prediction model metric

Score around 0.70 = good model

Score around 0.5 = random background

For survival analysis

Measures accuracy of prediction

Other methods

Principal Component Analysis instead of autoencoders

Use of fewer features

Functional Analysis

Clinical covariate analysis

Differential expression

TP53 mutation analysis

Enriched pathways analysis

Frequency distribution analysis

Associations of subtypes

Note: Functional analysis employed may be used for future works

Race

Grade

Gender

Stage

Fisher Exact Tests

Note: Comparison across different cohorts

Results

Based on C-Index the model is robust

Low Brier score indicates low error rates

Significant log rank P scores

Achieved great results from different cohorts

Chaudhary, K., Poirion, O. B., Lu, L., and
Garmire, L. X. Deep learning–based multi-omics
integration robustly predicts survival in liver cancer.
Clinical Cancer Research 24, 6 (2018), 1248–1259.