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

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. 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.

We 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.

Here, 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.

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. 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. Conclusions: We 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.