Abstract
The heterogeneity of cancer is a big obstacle for cancer diagnosis and treatment. Prioritizing combinations of driver genes that mutate in most patients of a specific cancer or a subtype of this cancer is a promising way to tackle this problem. Here, we developed an empirical algorithm, named PathMG, to identify common and subtype-specific mutated sub-pathways for a cancer. By analyzing mutation data of 408 samples (Lung-data1) for lung cancer, three sub-pathways each covering at least 90% of samples were identified as the common sub-pathways of lung cancer. These sub-pathways were enriched with mutated cancer genes and drug targets and were validated in two independent datasets (Lung-data2 and Lung-data3). Especially, applying PathMG to analyze two major subtypes of lung cancer, lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LSCC), we identified 13 subtype-specific sub-pathways with at least 0.25 mutation frequency difference between LUAD and LSCC samples in Lung-data1, and 12 of the 13 sub-pathways were reproducible in Lung-data2 and Lung-data3. Similar analyses were done for colorectal cancer. Together, PathMG provides us a novel tool to identify potential common and subtype-specific sub-pathways for a cancer, which can provide candidates for cancer diagnoses and sub-pathway targeted treatments.
Highlights
Thousands of mutations are detected for a cancer with the advances of DNA sequencing technologies
Using the 408 mutation profiles of Lung-data1, we firstly identified 116 significantly mutated pathways for lung cancer (FDR < 0.05)
The result indicated that the common mutated sub-pathways were highly reproducible in different sets of lung cancer samples, which suggests that the mutation genes within the common sub-pathways could be candidate panels of mutation genes for lung cancer diagnosis
Summary
Thousands of mutations are detected for a cancer with the advances of DNA sequencing technologies. Many algorithms have been developed to identify a panel of genes or pathways that mutate in a significantly high fraction of patients in a particular type of cancer. These identified mutation genes or pathways might be drivers contributing to cancer (Youn and Simon, 2011; Dees et al, 2012; Hua et al, 2013; Merid et al, 2014; Leiserson et al, 2015) or potential diagnosis biomarkers for a cancer (Ece Solmaz et al, 2015; Clifford et al, 2016; Li et al, 2016; Sato et al, 2016).
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