Inferring evolutionary trajectories from cross-sectional transcriptomic data to mirror lung adenocarcinoma progression

Abstract

Lung adenocarcinoma (LUAD) is a deadly tumor with dynamic evolutionary process. Although much endeavors have been made in identifying the temporal patterns of cancer progression, it remains challenging to infer and interpret the molecular alterations associated with cancer development and progression. To this end, we developed a computational approach to infer the progression trajectory based on cross-sectional transcriptomic data. Analysis of the LUAD data using our approach revealed a linear trajectory with three different branches for malignant progression, and the results showed consistency in three independent cohorts. We used the progression model to elucidate the potential molecular events in LUAD progression. Further analysis showed that overexpression of BUB1B, BUB1 and BUB3 promoted tumor cell proliferation and metastases by disturbing the spindle assembly checkpoint (SAC) in the mitosis. Aberrant mitotic spindle checkpoint signaling appeared to be one of the key factors promoting LUAD progression. We found the inferred cancer trajectory allows to identify LUAD susceptibility genetic variations using genome-wide association analysis. This result shows the opportunity for combining analysis of candidate genetic factors with disease progression. Furthermore, the trajectory showed clear evident mutation accumulation and clonal expansion along with the LUAD progression. Understanding how tumors evolve and identifying mutated genes will help guide cancer management. We investigated the clonal architectures and identified distinct clones and subclones in different LUAD branches. Validation of the model in multiple independent data sets and correlation analysis with clinical results demonstrate that our method is effective and unbiased. Lung adenocarcinoma (LUAD) is a deadly tumor that remains approximately 15% survival rate in 5 years. The diverse reasons that made cancer progression and metastasis include genetic mutation, gene expression alteration, and so on. Understanding this dynamic process and identifying pivotal molecular events driving tumor progression is essential for improving LUAD diagnosis and treatment. Studying time-series data can simulate cancer evolution and determine the temporal patterns of molecular alterations. Unfortunately, it is difficult to collect complete time-series data from individual patients due to various reasons. In this study, we developed a trajectory model based on gene expression of LUAD patients and identified distinct progression branches. Moreover, we found a key gene BUB1B that could lead to aberrant spindle assembly checkpoint (SAC) signaling, and this appeared to be one of the key factors promoting LUAD progression. The trajectory shows clear evident mutation accumulation and clonal expansion along with the LUAD progression. We also investigated the clonal architectures and identified distinct clones and subclones in different LUAD branches. This clones and subclones can be used to identify combination therapy for LUAD. Our results demonstrate the biological utility and clinical application prospects of this progression model.