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Integrated machine learning survival framework for consensus modeling in a large multicenter cohort of NSCLC

Xiao Wu1, Yang Lu2, Yongping Li3

  • 1The First Affiliated Hospital of Bengbu Medical University, Bengbu, China.

Scientific Reports
|October 29, 2025
PubMed

Insights

A new prognostic signature (ARRPS) predicts outcomes in non-small cell lung cancer (NSCLC) patients treated with aumolertinib (AUM). ARRPS may guide personalized treatments to overcome drug resistance.

Area of Science:

  • Oncology
  • Genomics
  • Pharmacology

Background:

  • Advanced non-small cell lung cancer (NSCLC) with EGFR mutations often responds to third-generation tyrosine kinase inhibitors (TKIs) like aumolertinib (AUM).
  • Acquired resistance to AUM limits its long-term clinical effectiveness in NSCLC patients.

Purpose of the Study:

  • To identify genes associated with AUM resistance in NSCLC.
  • To develop a machine learning-based prognostic signature for AUM resistance.
  • To explore potential combination therapies for overcoming AUM resistance.

Main Methods:

  • Established in vitro models of AUM-resistant NSCLC cell lines.
  • Performed RNA sequencing to identify differentially expressed genes.
  • Utilized machine learning to construct the AUM Resistance-Related Prognostic Signature (ARRPS).

Main Results:

  • Identified key differentially expressed genes linked to AUM resistance.
  • Developed ARRPS, demonstrating its efficacy in predicting patient prognostic risk.
  • Found that combining AUM with CD-437 or TPCA-1 may overcome resistance in high-ARRPS score patients.

Conclusions:

  • ARRPS is a valuable prognostic tool for NSCLC patients undergoing AUM treatment.
  • ARRPS can guide personalized therapeutic strategies to improve clinical outcomes.
  • Targeted combination therapies show promise for overcoming AUM resistance in specific patient subgroups.
Keywords:
AumolertinibNSCLCPrognostic signatureResistance

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