Evaluating predictors of kinase activity of STK11 variants identified in primary human non-small cell lung cancers

TL;DR

This study evaluates computational tools for predicting the impact of STK11 gene variants in lung cancer, showing their potential for clinical and research use.

Contribution

The study provides a critical evaluation of computational tools using experimentally validated STK11 variants from lung cancer biopsies.

Findings

Computational predictors showed high performance in separating loss-of-function from wildtype-like variants.

3Cnet and three public tools approached the performance of experimental replicates in variant classification.

Combining computational and experimental data led to 16 new clinically actionable variant classifications.

Abstract

Critical evaluation of computational tools for predicting variant effects is important considering their increased use in disease diagnosis and driving molecular discoveries. In the sixth edition of the Critical Assessment of Genome Interpretation (CAGI) challenge, a dataset of 28 STK11 rare variants (27 missense, 1 single amino acid deletion), identified in primary non-small cell lung cancer biopsies, was experimentally assayed to characterize computational methods from four participating teams and five publicly available tools. Predictors demonstrated a high level of performance on key evaluation metrics, measuring correlation with the assay outputs and separating loss-of-function (LoF) variants from wildtype-like (WT-like) variants. The best participant model, 3Cnet, performed competitively with well-known tools. Unique to this challenge was that the functional data was generated with both biological and technical replicates, thus allowing the assessors to realistically establish maximum predictive performance based on experimental variability. Three out of the five publicly available tools and 3Cnet approached the performance of the assay replicates in separating LoF variants from WT-like variants. Surprisingly, REVEL, an often-used model, achieved a comparable correlation with the real-valued assay output as that seen for the experimental replicates. Performing variant interpretation by combining the new functional evidence with computational and population data evidence led to 16 new variants receiving a clinically actionable classification of likely pathogenic (LP) or likely benign (LB). Overall, the STK11 challenge highlights the utility of variant effect predictors in biomedical sciences and provides encouraging results for driving research in the field of computational genome interpretation.

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