Integrating thousands of PTEN variant activity and abundance measurements reveals variant subgroups and new dominant negatives in cancers

Kenneth A Matreyek,Jason J Stephany,Douglas M Fowler,Ethan Ahler

doi:10.1186/s13073-021-00984-x

Abstract

BackgroundPTEN is a multi-functional tumor suppressor protein regulating cell growth, immune signaling, neuronal function, and genome stability. Experimental characterization can help guide the clinical interpretation of the thousands of germline or somatic PTEN variants observed in patients. Two large-scale mutational datasets, one for PTEN variant intracellular abundance encompassing 4112 missense variants and one for lipid phosphatase activity encompassing 7244 variants, were recently published. The combined information from these datasets can reveal variant-specific phenotypes that may underlie various clinical presentations, but this has not been comprehensively examined, particularly for somatic PTEN variants observed in cancers.MethodsHere, we add to these efforts by measuring the intracellular abundance of 764 new PTEN variants and refining abundance measurements for 3351 previously studied variants. We use this expanded and refined PTEN abundance dataset to explore the mutational patterns governing PTEN intracellular abundance, and then incorporate the phosphatase activity data to subdivide PTEN variants into four functionally distinct groups.ResultsThis analysis revealed a set of highly abundant but lipid phosphatase defective variants that could act in a dominant-negative fashion to suppress PTEN activity. Two of these variants were, indeed, capable of dysregulating Akt signaling in cells harboring a WT PTEN allele. Both variants were observed in multiple breast or uterine tumors, demonstrating the disease relevance of these high abundance, inactive variants.ConclusionsWe show that multidimensional, large-scale variant functional data, when paired with public cancer genomics datasets and follow-up assays, can improve understanding of uncharacterized cancer-associated variants, and provide better insights into how they contribute to oncogenesis.

Highlights

PTEN is a multi-functional tumor suppressor protein regulating cell growth, immune signaling, neuronal function, and genome stability
We use VAMP-seq to measure 764 additional variant abundance scores that were not previously measured, and add additional data to 3351 variants that were. We use this more comprehensive PTEN abundance dataset to explore the mutational patterns governing PTEN intracellular abundance and incorporate the phosphatase scores to subdivide PTEN variants into four functionally distinct groups. This analysis revealed a set of highly abundant but lipid phosphatase defective variants that could act in a dominant-negative fashion to suppress PTEN activity
Mutational tolerance patterns for PTEN abundance We previously developed VAMP-seq, a generalizable method to simultaneously measure the effects of thousands of missense variants of a protein on intracellular abundance [9]

Summary

Introduction

PTEN is a multi-functional tumor suppressor protein regulating cell growth, immune signaling, neuronal function, and genome stability. Experimental characterization can help guide the clinical interpretation of the thousands of germline or somatic PTEN variants observed in patients. The combined information from these datasets can reveal variant-specific phenotypes that may underlie various clinical presentations, but this has not been comprehensively examined, for somatic PTEN variants observed in cancers. Interpretation of the functional consequence of each observed variant is a major bottleneck for personalized genomic medicine. Computational approaches are useful, but lack the high accuracy needed to confidently interpret the impacts of a given protein variant in a clinical setting. Traditional experimental assays that measure variant effects one at a time lack the throughput needed to characterize the thousands of missense variants that are possible within each disease-relevant protein. Many disease-related proteins have multiple functions, and, generally, no single large-scale variant effect dataset can capture all of these functions. Multiple distinct largescale variant effect datasets may be needed to accurately phenotype variants in such proteins

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