Abstract PR04: High-throughput gene expression profiling as a generalizable assay for determination of mutation impact on gene function
Authors: Alice Berger, Angela Brooks, Xiaoyun Wu, Larson Hogstrom, Itay Tirosh, Federica Piccioni, Mukta Bagul, Cong Zhu, Yashaswi Shretha, David Root, Pablo Tamayo, Ryo Sakai, Bang Wong, Aravind Subramanian, Todd Golub, Matthew Meyerson, Jesse Boehm
Journal: Cancer Research (2015)

Abstract Recently, the decline in the cost of genome sequencing has led to the rapid identification of thousands of cancer-associated somatic mutations. However, progress in characterization of these genetic events has lagged significantly behind. Understanding mutation function is critical not only for research purposes but also for determining targeted treatment strategies based on individual tumor genetic profiles, yet determination of mutation impact remains a significant bottleneck. Here we describe a high-throughput approach to classify somatic mutations that is robust, scalable, and requires no prior information of gene function. We generated a lentiviral cDNA expression library of ~550 mutated and wild-type alleles of genes mutated in lung adenocarcinoma and introduced these alleles into four human lung cell lines. 96 hours post-infection, gene expression profiles were generated using Luminex-based L1000 profiling. In total, more than 2000 gene expression signatures were generated. We discovered that gain-of-function mutants induce expression signatures with a greater signal strength or different identity than the corresponding wild-type gene signature. In contrast, loss-of-function mutants could be identified by their incapability to induce strong signatures. Based on these features of signature strength and signature identity, we developed a decision-tree approach to classify mutations as either dominant, loss-of-function, or likely inert. An orthogonal functional approach, an EGFR inhibitor resistance screen, was used as validation. The gene expression approach correctly classified known gain-of-function mutations in KRAS (13/13), EGFR (6/7), and ARAF (2/2) and identified dozens of never-characterized gain-of-function and loss-of-function missense mutations. In addition to rare, dominant mutations in clinically-actionable oncogenes such as PIK3CA and AKT1, we identified unexpected dominant mutations in the transcription factor MAX and the phosphatase subunit PPP2R1A, among others. We also observed a substantial enrichment of loss-of-function mutations in tumor suppressor genes such as STK11, KEAP1, FBXW7, and CASP8 as well as in genes not previously connected to lung adenocarcinoma, including GPR137B and MAPK7. Most genes assayed also harbored variants that are likely inert, further underscoring the importance of characterizing individual variant alleles. The method developed here can, in principle, characterize any genetic variant, independent of prior knowledge of gene function, and should significantly advance the pace of functional characterization of mutations identified from genome sequencing. Citation Format: Alice Berger, Angela Brooks, Xiaoyun Wu, Larson Hogstrom, Itay Tirosh, Federica Piccioni, Mukta Bagul, Cong Zhu, Yashaswi Shretha, David Root, Pablo Tamayo, Ryo Sakai, Bang Wong, Aravind Subramanian, Todd Golub, Matthew Meyerson, Jesse Boehm. High-throughput gene expression profiling as a generalizable assay for determination of mutation impact on gene function. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr PR04.