RBM10 Mutation as a Potential Negative Prognostic/Predictive Biomarker to Therapy in Non-Small-Cell Lung Cancer.

Alternative splicing EGFR KRAS RBM10 TKI resistance

Journal

Clinical lung cancer
ISSN: 1938-0690
Titre abrégé: Clin Lung Cancer
Pays: United States
ID NLM: 100893225

Informations de publication

Date de publication:
23 Jul 2024
Historique:
received: 04 04 2024
revised: 05 07 2024
accepted: 13 07 2024
medline: 14 8 2024
pubmed: 14 8 2024
entrez: 13 8 2024
Statut: aheadofprint

Résumé

According to WHO, lung cancer is the leading cause of cancer-related death worldwide, but treatment has advanced in the last decade. The widespread use of Next Generation Sequencing has led to the discovery of several pathogenic mutations including RNA binding motif 10 (RBM10), a part of the spliceosome complex that regulates splicing of pre-mRNA. Electronic medical records were utilized to create a database of patients (50 patients) seen from 2018-2023 with NSCLC and RBM10 mutations, with appropriate IRB approval. For subgroup analysis, we separated into groups by rapid progression vs stable disease defined as progression-free survival earlier than respective clinical trials. From the analysis of treatment response the mutated RBM10 population had a median PFS was 6.7 months compared to 13.9 in the wild-type RBM10 population controlled for driver mutations TP53 mutation had a higher representation in the RBM10 mutated rapid progression group than the stable disease group. The ZFHX3 mutation had a higher representation in the RBM10 mutated stable disease group. RBM10 mutations were associated with aggressive disease with treatment progression faster than median durations of response. RBM10 mutations with concurrent ZFHX3 and EGFR mutations were associated with more stable disease, while concurrent KRAS and TP53 predicted even more aggressive disease.

Sections du résumé

BACKGROUND BACKGROUND
According to WHO, lung cancer is the leading cause of cancer-related death worldwide, but treatment has advanced in the last decade. The widespread use of Next Generation Sequencing has led to the discovery of several pathogenic mutations including RNA binding motif 10 (RBM10), a part of the spliceosome complex that regulates splicing of pre-mRNA.
PATIENTS AND METHODS METHODS
Electronic medical records were utilized to create a database of patients (50 patients) seen from 2018-2023 with NSCLC and RBM10 mutations, with appropriate IRB approval. For subgroup analysis, we separated into groups by rapid progression vs stable disease defined as progression-free survival earlier than respective clinical trials.
RESULTS RESULTS
From the analysis of treatment response the mutated RBM10 population had a median PFS was 6.7 months compared to 13.9 in the wild-type RBM10 population controlled for driver mutations TP53 mutation had a higher representation in the RBM10 mutated rapid progression group than the stable disease group. The ZFHX3 mutation had a higher representation in the RBM10 mutated stable disease group.
CONCLUSIONS CONCLUSIONS
RBM10 mutations were associated with aggressive disease with treatment progression faster than median durations of response. RBM10 mutations with concurrent ZFHX3 and EGFR mutations were associated with more stable disease, while concurrent KRAS and TP53 predicted even more aggressive disease.

Identifiants

pubmed: 39138107
pii: S1525-7304(24)00148-7
doi: 10.1016/j.cllc.2024.07.010
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Disclosure The authors have no conflicts to declare.

Auteurs

Amanda Reyes (A)

Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA.

Michelle Afkhami (M)

Department of Pathology, City of Hope National Medical Center, Duarte, CA.

Erminia Massarelli (E)

Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA.

Jeremy Fricke (J)

Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA.

Isa Mambetsariev (I)

Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA.

Xiaochen Li (X)

Division of Biostatistics, Department of Computational and Quantitative Medicine, City of Hope National Medical Center, Duarte, CA.

Giovanny Velasquez (G)

Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA.

Ravi Salgia (R)

Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA. Electronic address: rsalgia@coh.org.

Classifications MeSH