Evaluating Predictors of Kinase Activity of STK11 Variants Identified in Primary Human Non-Small Cell Lung Cancers

Authors

Yile Chen
Kyoungyeul Lee
Junwoo Woo
Dong-Wook Kim
Changwon Keum
Giulia Babbi
Rita Casadio
Pier Luigi Martelli
Castrense Savojardo
Matteo Manfredi
Yang Shen
Yuanfei Sun
Panagiotis Katsonis
Olivier Lichtarge
Vikas Pejaver
David J Seward
Akash Kamandula
Constantina Bakolitsa
Steven E Brenner
Predrag Radivojac
Anne O'Donnell-Luria
Sean D Mooney
Shantanu Jain

Language

English

Publication Date

3-1-2025

Journal

Human Genetics

DOI

10.1007/s00439-025-02726-0

PMID

39934475

PMCID

PMC11976797

PubMedCentral® Posted Date

2-12-2025

PubMedCentral® Full Text Version

Post-print

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.

Keywords

Humans, Carcinoma, Non-Small-Cell Lung, AMP-Activated Protein Kinase Kinases, Protein Serine-Threonine Kinases, Lung Neoplasms, Computational Biology, Mutation, Missense

Published Open-Access

yes

Recommended Citation

Chen, Yile; Lee, Kyoungyeul; Woo, Junwoo; et al., "Evaluating Predictors of Kinase Activity of STK11 Variants Identified in Primary Human Non-Small Cell Lung Cancers" (2025). Faculty and Staff Publications. 5047.
https://digitalcommons.library.tmc.edu/baylor_docs/5047