Landscape of transcriptional deregulation in lung cancer

BACKGROUND

Lung cancer is a very heterogeneous disease that can be pathologically classified into different subtypes including small-cell lung carcinoma (SCLC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC) and large-cell carcinoma (LCC). Although much progress has been made towards the oncogenic mechanism of each subtype, transcriptional circuits mediating the upstream signaling pathways and downstream functional consequences remain to be systematically studied.

RESULTS

Here we trained a one-class support vector machine (OC-SVM) model to establish a general transcription factor (TF) regulatory network containing 325 TFs and 18724 target genes. We then applied this network to lung cancer subtypes and identified those deregulated TFs and downstream targets. We found that the TP63/SOX2/DMRT3 module was specific to LUSC, corresponding to squamous epithelial differentiation and/or survival. Moreover, the LEF1/MSC module was specifically activated in LUAD and likely to confer epithelial-to-mesenchymal transition, known important for cancer malignant progression and metastasis. The proneural factor, ASCL1, was specifically up-regulated in SCLC which is known to have a neuroendocrine phenotype. Also, ID2 was differentially regulated between SCLC and LUSC, with its up-regulation in SCLC linking to energy supply for fast mitosis and its down-regulation in LUSC linking to the attenuation of immune response. We further described the landscape of TF regulation among the three major subtypes of lung cancer, highlighting their functional commonalities and specificities.

CONCLUSIONS

Our approach uncovered the landscape of transcriptional deregulation in lung cancer, and provided a useful resource of TF regulatory network for future studies.

Modularized Perturbation of Alternative Splicing Across Human Cancers

Splicing perturbation in cancers contribute to different aspects of cancer cell progression. However, the complete functional impact of cancer-associated splicing have not been fully characterized. Comprehensive large-scale studies are essential to unravel the dominant patterns of cancer-associated splicing. Here we analyzed the genome-wide splicing data in 16 cancer types with normal samples, identified differential splicing events in each cancer type. Then we took a network-based and modularized approach to reconstruct cancer-associated splicing networks, determine the module structures, and evaluate their prognosis relevance. This approach in total identified 51 splicing modules, among which 10/51 modules are related to patient survival, 8/51 are related to progression-free interval, and 5/51 are significant in both. Most of the 51 modules show significant enrichment of important biological functions, such as stem cell proliferation, cell cycle, cell growth, DNA repair, receptor or kinase signaling, and VEGF vessel development. Module-based clustering grouped cancer types according to their tissue-of-origins, consistent with previous pan-cancer studies based on integrative clustering. Interestingly, 13/51 modules are highly common across different cancer types, suggesting the existence of pan-cancer splicing perturbations. Together, modularized perturbation of splicing represents an functionally important and common mechanism across cancer types.