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Identification of Variant-Specific Functions of PIK3CA by Rapid Phenotyping of Rare Mutations.

ABSTRACT: Large-scale sequencing efforts are uncovering the complexity of cancer genomes, which are composed of causal "driver" mutations that promote tumor progression along with many more pathologically neutral "passenger" events. The majority of mutations, both in known cancer drivers and uncharacterized genes, are generally of low occurrence, highlighting the need to functionally annotate the long tail of infrequent mutations present in heterogeneous cancers. Here we describe a mutation assessment pipeline enabled by high-throughput engineering of molecularly barcoded gene variant expression clones identified by tumor sequencing. We first used this platform to functionally assess tail mutations observed in PIK3CA, which encodes the catalytic subunit alpha of the phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K) frequently mutated in cancer. Orthogonal screening for PIK3CA variant activity using in vitro and in vivo cell growth and transformation assays differentiated driver from passenger mutations, revealing that PIK3CA variant activity correlates imperfectly with its mutation frequency across breast cancer populations. Although PIK3CA mutations with frequencies above 5% were significantly more oncogenic than wild-type in all assays, mutations occurring at 0.07% to 5.0% included those with and without oncogenic activities that ranged from weak to strong in at least one assay. Proteomic profiling coupled with therapeutic sensitivity assays on PIK3CA variant-expressing cell models revealed variant-specific activation of PI3K signaling as well as other pathways that include the MEK1/2 module of mitogen-activated protein kinase pathway. Our data indicate that cancer treatments will need to increasingly consider the functional relevance of specific mutations in driver genes rather than considering all mutations in drivers as equivalent.

SUBMITTER: Dogruluk T

PROVIDER: S-EPMC4681596 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

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Identification of Variant-Specific Functions of PIK3CA by Rapid Phenotyping of Rare Mutations.

Dogruluk Turgut T Tsang Yiu Huen YH Espitia Maribel M Chen Fengju F Chen Tenghui T Chong Zechen Z Appadurai Vivek V Dogruluk Armel A Eterovic Agna Karina AK Bonnen Penelope E PE Creighton Chad J CJ Chen Ken K Mills Gordon B GB Scott Kenneth L KL

Cancer research 20151201 24

Large-scale sequencing efforts are uncovering the complexity of cancer genomes, which are composed of causal "driver" mutations that promote tumor progression along with many more pathologically neutral "passenger" events. The majority of mutations, both in known cancer drivers and uncharacterized genes, are generally of low occurrence, highlighting the need to functionally annotate the long tail of infrequent mutations present in heterogeneous cancers. Here we describe a mutation assessment pip ...[more]

PMID: 26627007

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Project description:IntroductionStudies that examine the role of rare variants in both simple and complex disease are increasingly common. Though the usual approach of testing rare variants in aggregate sets is more powerful than testing individual variants, it is of interest to identify the variants that are plausible drivers of the association. We present a novel method for prioritization of rare variants after a significant aggregate test by quantifying the influence of the variant on the aggregate test of association.MethodsIn addition to providing a measure used to rank variants, we use outlier detection methods to present the computationally efficient Rare Variant Influential Filtering Tool (RIFT) to identify a subset of variants that influence the disease association. We evaluated several outlier detection methods that vary based on the underlying variance measure: interquartile range (Tukey fences), median absolute deviation, and SD. We performed 1,000 simulations for 50 regions of size 3 kb and compared the true and false positive rates. We compared RIFT using the Inner Tukey to 2 existing methods: adaptive combination of p values (ADA) and a Bayesian hierarchical model (BeviMed). Finally, we applied this method to data from our targeted resequencing study in idiopathic pulmonary fibrosis (IPF).ResultsAll outlier detection methods observed higher sensitivity to detect uncommon variants (0.001 < minor allele frequency, MAF > 0.03) compared to very rare variants (MAF <0.001). For uncommon variants, RIFT had a lower median false positive rate compared to the ADA. ADA and RIFT had significantly higher true positive rates than that observed for BeviMed. When applied to 2 regions found previously associated with IPF including 100 rare variants, we identified 6 polymorphisms with the greatest evidence for influencing the association with IPF.DiscussionIn summary, RIFT has a high true positive rate while maintaining a low false positive rate for identifying polymorphisms influencing rare variant association tests. This work provides an approach to obtain greater resolution of the rare variant signals within significant aggregate sets; this information can provide an objective measure to prioritize variants for follow-up experimental studies and insight into the biological pathways involved.

Dataset Information

Identification of Variant-Specific Functions of PIK3CA by Rapid Phenotyping of Rare Mutations.

Publications

Identification of Variant-Specific Functions of PIK3CA by Rapid Phenotyping of Rare Mutations.

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