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Poster Display session

105P - A retrospective study using machine learning to analyze the effects of VEGFR-TKIs and PD-1 inhibitors as third-line or later treatment in patients with MSS mCRC

Date

12 Dec 2024

Session

Poster Display session

Presenters

Shumei Han

Citation

Annals of Oncology (2024) 24 (suppl_1): 1-20. 10.1016/iotech/iotech100744

Authors

S. Han1, X. Li2, B. Liu2, J. Yang2, J. Hu3

Author affiliations

  • 1 Shandong Cancer Hospital and Institute, Jinan/CN
  • 2 Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan/CN
  • 3 Department of Radiotherapy, Zhucheng People's Hospital, Shandong Second Medical University, Zhucheng/CN

Resources

This content is available to ESMO members and event participants.

Abstract 105P

Background

Anti-angiogenesis inhibitors have shown enhanced antitumor activity when combined with PD-1 inhibitors in patients with metastatic colorectal cancer (mCRC). The study aimed to forecast the prognosis and identify the influential factors associated with the therapeutic combination of VEGFR-TKIs and PD-1 inhibitors in patients with microsatellite-stable (MSS) mCRC.

Methods

We conducted a retrospective study of MSS mCRC patients treated with VEGFR-TKI coupled with PD-1 inhibitors from January 2020 to April 2024. Kaplan-Meier curves were generated for OS. Boruta algorithm and LASSO-Cox regression model were applied to identify key features associated with OS in our clinical dataset, which comprises 34 features including age, sex, primary location, differentiation, RAS status, metastatic sites, treatment lines, the treatment duration of VEGFR-TKIs and the treatment cycles of PD-1 inhibitors, etc. Restricted cubic splines (RCS) were used to estimate the effects of key features on OS.

Results

The study enrolled 79 eligible patients, with a median age of 57 years (range, 24-81), and 65.8% were male. The most frequently utilized VEGFR-TKI was fruquintinib. The median number of previous treatment lines was two. Median OS was 27.1 months (95% CI 20.6–NA). Median PFS was 5.7 months (95% CI 4.3–7.9). LASSO-Cox regression identified four signature features: sex, primary location, differentiation, and the treatment duration of VEGFR-TKIs, which highly influenced the OS. Boruta algorithm identified the treatment duration of VEGFR-TKIs as the only key feature influencing the OS of patients. RCS results suggested an approximately linear relationship between the hazard ratio (HR) for OS and the treatment duration of VEGFR-TKIs (P = 0.0234, P for non-linearity = 0.630), and the treatment duration of fruquintinib longer than 2.8 months was significantly associated with longer OS.

Conclusions

The machine learning findings indicate that the duration of VEGFR-TKI treatment impacts OS in patients with MSS mCRC, a treatment duration of fruquintinib exceeding 2.8 months was found to have a significant positive influence on OS for patients with MSS mCRC.

Legal entity responsible for the study

Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences.

Funding

Has not received any funding.

Disclosure

All authors have declared no conflicts of interest.

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