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Updated: Oct 2, 2025
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
A mutation-based gene set predicts survival benefit after immunotherapy across multiple cancers and reveals the
Junyu Long1, Dongxu Wang2, Anqiang Wang3
1Department of Liver Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, China.
Background:
Immune checkpoint inhibitor (ICI) therapy has revolutionized the treatment of many cancers. However, the limited population that benefits from ICI therapy makes it necessary to screen predictive biomarkers for stratifying patients. Currently, many biomarkers, such as tumor mutational burden (TMB), have been used in the clinic as indicative biomarkers. However, some high-TMB patients with mutations in genes that are closely related to immunotherapeutic resistance are not sensitive to ICI therapy. Thus, there is a need to move beyond TMB and identify specific genetic determinants of the response to ICI therapy. In this study, we established a comprehensive mutation-based gene set across different tumor types to predict the efficacy of ICI therapy.
Methods:
We constructed and validated a mutational signature to predict the prognosis of patients treated with ICI therapy. Then, the underlying immune response landscapes of different subtypes were investigated with multidimensional data.
Results:
This study included genomic and clinical data for 12,647 patients. An eleven-gene mutation-based gene set was generated to divide patients into a high-risk group and a low-risk group in a training cohort (1572 patients with 9 types of cancers who were treated with ICI therapy). Validation was performed in a validation cohort (932 patients with 5 types of cancers who were treated with ICI therapy). Mutations in these 11 genes were associated with a better response to ICI therapy. In addition, the mutation-based gene set was demonstrated to be an independent prognostic factor after ICI therapy. We further explored the role of the immune context in determining the benefits of immunotherapy in 10,143 patients with 33 types of cancers and found distinct immune landscapes for the high- and low-risk groups.
Conclusions:
The mutation-based gene set developed in this study can be used to reliably predict survival benefit across cancers in patients receiving ICI therapy. The close interplay between the extrinsic and intrinsic immune landscapes in the identified patient subgroups and the subgroups' differing responses to ICI therapy could guide immunotherapy treatment decisions for cancer patients.
Insights
A new eleven-gene mutation signature predicts survival in cancer patients receiving immune checkpoint inhibitor (ICI) therapy. This biomarker offers a more precise way to guide immunotherapy decisions beyond tumor mutational burden.
Area of Science:
- Oncology
- Immunotherapy
- Genomics
Background:
- Immune checkpoint inhibitor (ICI) therapy has transformed cancer treatment, but patient response varies.
- Predictive biomarkers are crucial for stratifying patients, yet current markers like tumor mutational burden (TMB) have limitations.
- Identifying specific genetic determinants is essential to improve ICI therapy efficacy.
Purpose of the Study:
- To develop and validate a comprehensive mutation-based gene set for predicting ICI therapy efficacy.
- To move beyond traditional biomarkers like TMB for more accurate patient stratification.
Main Methods:
- Constructed and validated a mutational signature using genomic and clinical data from 12,647 cancer patients.
- Generated an eleven-gene mutation-based gene set to stratify patients into high- and low-risk groups.
- Investigated immune response landscapes using multidimensional data.
Main Results:
- An eleven-gene mutation set effectively divided patients into distinct risk groups in training and validation cohorts.
- Mutations in these 11 genes correlated with better response to ICI therapy and served as an independent prognostic factor.
- Distinct immune landscapes were identified between high- and low-risk groups across 33 cancer types.
Conclusions:
- The developed mutation-based gene set reliably predicts survival benefits for cancer patients undergoing ICI therapy.
- Understanding the interplay between immune landscapes and genetic profiles can guide personalized immunotherapy treatment decisions.
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