Molecular predictors of outcome in non-small cell lung cancer

2012

Christopher A. Maher, PhD

Washington University in St. Louis

St. Louis

Dr. Maher is working to improve on the accuracy and usability of tests that identify lung cancer patients who are likely to relapse. He is using next-generation sequencing techniques to develop a signature set of key genetic changes and convert it to a clinical test that will be able to predict who is at high risk for relapse.

Research Summary

Currently, approximately 30% of non-small cell lung cancer (NSCLC) patients will die from recurrent disease despite undergoing surgery. Therefore, there is a critical need to develop accurate markers to distinguish high-risk patients who are more likely to have recurrent disease from patients who are at a lower risk for recurrent disease. To date several groups have applied high-throughput methods to identify a set of genes, or a “signature,” that will classify patients as high-risk or low-risk. However, there are two limitations to existing approaches. First, technological advances in sequencing technology offer an opportunity to identify additional markers, which would have eluded previous approaches, and therefore could result in a more accurate signature. Second, previous methods for developing a signature were developed on tumor material that is not routinely collected in clinics. This presents a critical need to develop an assay that can effectively apply a predictive signature on samples collected in the clinic. Therefore, our proposal focuses on using newer sequencing technologies to generate a refined signature for predicting which patients have more aggressive disease. We then proposed two goals for adapting this signature into a clinically useful assay. First, we intend to confirm that the predictive signature works on the same sample material collected in a clinic. Second, we intend to test our predictive signature on a completely independent cohort of patients to ensure it accurately stratifies patients with more aggressive disease. Overall, if the proposed aims are met, this research could lead to an improved assay that could be widely adopted in the clinic to stratify high-risk patients for more aggressive therapies and assign less aggressive treatments to patients at low risk for recurrence.

Technical Abstract

Approximately 30% of patients with stage 1 non-small cell lung cancer (NSCLC) will die from recurrent disease despite surgical resection . Currently, there is a lack of reliable molecular predictors being routinely used in the clinic for identifying patients at high risk for developing recurrent disease and therefore may benefit from more aggressive therapies. To date several groups have applied microarray-based methods for global gene expression profiling of frozen tumor tissues in an attempt to identify prognostic ‘signatures’ in patients with early stage NSCLC. Building upon this, through a meta-analysis of seven independent microarray studies Govindan et al. were able to identify a 64-gene signature that is highly predictive of survival in patients with stage I NSCLC. However, recent advances in high-throughput sequencing have enabled an unbiased view of the transcriptome including novel transcriptional events such as gene fusions, splicing variants, and novel transcripts. This offers an opportunity to incorporate additional markers, which may have eluded previous technologies, but could serve for improving a predictive signature of survival in non-small cell lung cancer (NSCLC). However, despite the potential of RNA-Seq to improve existing predictive signatures, we are still faced with the challenge of translating these findings into a platform where they can be reliably applied to formalin fixed paraffin embedded (FFPE) clinical tissue specimens routinely collected in tertiary referral centers. Therefore, this proposal focuses on refining the 64-gene predictive signature using transcriptome sequencing and developing a NanoString nCounter assay to reliably predict survival of patients with resected stage I NSCLC, using RNA derived from FFPE tumor blocks. To accomplish this we have proposed the following specific aims: (1) use transcriptome sequencing to detect molecular predictors of outcome in patients with stage I NSCLC, (2) validate the technical performance of the NanoString nCounter using an RNA-Seq predictive signature across FFPE tumor blocks of resected stage I NSCLC, and (3) clinically validate the prognostic power of the predictive signature across an independent sample set. Overall, if the proposed aims are met, this research could lead to an improved assay that could be translated into future prospective studies for risk-stratifying stage I NSCLC patients.

Key words

Anaplastic lymphoma kinase (ALK)

Biomarker or biomarker testing

KRAS

Molecular profile or molecular testing

Mutation profile

Non-small cell lung cancer (NSCLC)

Recurrent

Stage I

Molecular predictors of outcome in non-small cell lung cancer

LUNGevity Foundation

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