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Related Experiment Video

Updated: Jul 5, 2025

Predictive Immune Modeling of Solid Tumors

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

Whether specific genetic feature predicted immunotherapy efficacy: A case report.

Jun Chen¹, Linrong Pang¹, Lianxiang He²

¹Cancer Chemoradiotherapy Center, The Affiliated People's Hospital of Ningbo University, Ningbo, China.

|January 12, 2024

Summary

This summary is machine-generated.

A patient with recurrent cervical cancer, lacking standard biomarkers, responded well to PD-1 inhibitor zimberelimab. This suggests PIK3CA, ErbB2, and SMAD4 mutations may predict immunotherapy response in cervical cancer.

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Area of Science:

Oncology
Immunotherapy
Biomarker Discovery

Background:

Programmed death protein 1 (PD-1) blockade shows efficacy in recurrent and metastatic cervical cancer.
Established biomarkers for PD-1 inhibitors include PD-L1 expression, tumor mutational burden, and microsatellite instability.
The current case suggests potential for novel biomarkers beyond established ones.

Observation:

A 51-year-old patient with cervical adenocarcinoma was treated with third-line zimberelimab monotherapy.
The patient's tumor was negative for programmed cell death-ligand 1 (PD-L1) and had low tumor mutational burden.
Next-generation sequencing identified PIK3CA E545K, SMAD4 1309-1G, ALK E717K mutations, and ErbB-2 amplification.

Findings:

The patient achieved a partial response (PR) to zimberelimab monotherapy, with treatment lasting nearly 10 months.
No significant adverse reactions were observed during treatment.
The patient's tumor exhibited microsatellite stability and low tumor mutational burden (6.3 mutations/Mb).

Implications:

PIK3CA, ErbB2 amplification, and SMAD4 mutations are proposed as potential predictive biomarkers for PD-1 inhibitors in cervical cancer.
Further research with larger patient cohorts is required to validate these genetic mutations as reliable biomarkers.
This case highlights the need to explore novel biomarkers for predicting immunotherapy response in patients with cervical cancer.