Lung cancer-enriched p53 mutants occupy canonical p53 target genes without activating transcription, revealing a distinct loss-of-function behavior

Lung cancer is the most common cause of cancer-related death in the U.S. and globally. Cigarette smoking remains the leading risk factor for lung cancer, in part by inducing loss-of-function mutations in tumor suppressor genes, including <i>TP53</i>. While most cancers share a set of common "hotspot" mutations in p53, lung cancer exhibits an additional, distinct cluster of hotspot mutations. This cluster is typified by the missense mutations <i>TP53</i>:p.V157F and <i>TP53</i>:p.R158L. While canonical hotspot mutations cause broad misfolding of p53 or eliminate specific DNA contact residues, mechanistic studies of the lung cancer mutants reported here demonstrate that they retain the ability to bind the same genomic sites as wild-type p53. Despite actively binding to traditional p53 target genes, the lung cancer mutants are defective in activating transcription. To our knowledge, this represents the first demonstration of functional inactivation of the p53 tumor suppressor at a point after DNA binding, but prior to target gene activation. Relevant to the sequential inactivation of each p53 allele during cancer progression, the lung cancer mutants block the activity of a wild-type p53 allele when co-expressed in a dominant negative manner. Identification of this loss-of-function mechanism has key implications for therapeutic strategies aimed at restoring p53 function in lung cancer.

Authors

Institutions

Publication Details

Journal
bioRxiv (Cold Spring Harbor Laboratory)
Published
2026-02-17
DOI
https://doi.org/10.64898/2026.02.14.705936
Primary Topic
Cancer-related Molecular Pathways
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Lung cancer-enriched p53 mutants occupy canonical p53 target genes without activating transcription, revealing a distinct loss-of-function behavior

Steven B. McMahon, Julie A. Barta, Mason Tracewell, K. M. Vyas et al.
bioRxiv (Cold Spring Harbor Laboratory)
Cancer-related Molecular Pathways
article

Lung cancer-enriched p53 mutants occupy canonical p53 target genes without activating transcription, revealing a distinct loss-of-function behavior

Steven B. McMahon, Julie A. Barta, Mason Tracewell, K. M. Vyas, Samantha M. Barnada, Theodhora Qyshkollari, Kevin M. Kim, Jonathan Karlin, Hailey Shankle
article en

Abstract

Lung cancer is the most common cause of cancer-related death in the U.S. and globally. Cigarette smoking remains the leading risk factor for lung cancer, in part by inducing loss-of-function mutations in tumor suppressor genes, including <i>TP53</i>. While most cancers share a set of common "hotspot" mutations in p53, lung cancer exhibits an additional, distinct cluster of hotspot mutations. This cluster is typified by the missense mutations <i>TP53</i>:p.V157F and <i>TP53</i>:p.R158L. While canonical hotspot mutations cause broad misfolding of p53 or eliminate specific DNA contact residues, mechanistic studies of the lung cancer mutants reported here demonstrate that they retain the ability to bind the same genomic sites as wild-type p53. Despite actively binding to traditional p53 target genes, the lung cancer mutants are defective in activating transcription. To our knowledge, this represents the first demonstration of functional inactivation of the p53 tumor suppressor at a point after DNA binding, but prior to target gene activation. Relevant to the sequential inactivation of each p53 allele during cancer progression, the lung cancer mutants block the activity of a wild-type p53 allele when co-expressed in a dominant negative manner. Identification of this loss-of-function mechanism has key implications for therapeutic strategies aimed at restoring p53 function in lung cancer.

bioRxiv (Cold Spring Harbor Laboratory)
Thomas Jefferson University (US)
National Cancer Institute
Good health and well-being
Openalex Percentile: Top 52%
Cancer-related Molecular Pathways
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.