The Challenges Of Using Large-Scale Genomics Data To Identify Novel Drivers Of Lung Cancer

Lung cancer is one of the major causes of cancer deaths worldwide and only 30% of patients survive the disease for at least one year after diagnosis. Patients are often too frail to receive systemic chemotherapy and there is an urgent need for less toxic, efficacious, targeted therapies. Despite recent efforts with large-scale genomics data we still lack knowledge about driver mutations for the majority of lung cancers. Increasingly, cancer researchers are using online cancer genomic databases to identify novel targets to investigate. A comparison of two prominent databases from different institutes (CCLE and COSMIC) revealed marked discrepancies in the detection of missense mutations in identical cell lines (57.38% conformity). A major reason for this discrepancy is inadequate sequencing of GC-rich areas. This is a significant issue for lung cancer, with a mutation signature predominantly affecting guanine and cytosine nucleotides and therefore preferring GC-rich regions. We have therefore focused on GC-rich regions that next-generation-sequencing struggle to cover and discovered over 400 of these regions (cold-spots) in Cancer Consensus and kinase genes alone. We demonstrate how a PAK4 mutation, found in a GC-rich cold-spot in a lung adenocarcinoma cell line, activates the pERK pathway. This suggests that specific targeting of GC-rich regions may be required to uncover further oncogenes and tumour suppressors in lung cancer. The high mutational burden of lung cancer creates additional challenges in distinguishing driver mutations from a multitude of passenger mutations. One solution is to use siRNA knockdown screens on all genes that are mutated in a cell line and assess cell viability. However we demonstrate that inconsistencies in mutational profiling of cell lines and passaging effects have the potential to influence these types of studies. These limitations also offer new explanations for the discrepancies seen when comparing pharmacogenomics studies. Citation Format: Andrew M. Hudson, Tim Yates, Chris Wirth, Yaoyong Li, Wendy Trotter, Shameem Fawdar, Crispin Miller, John Brognard. The challenges of using large-scale genomics data to identify novel drivers of lung cancer. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A2-18.