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Updated: Sep 25, 2025
Author Spotlight: Exploring Strategies for Successful Immune Response Against Tumors
Published on: August 16, 2024
Integrated analysis of single-cell and bulk RNA sequencing data reveals a pan-cancer stemness signature predicting
Zhen Zhang1,2, Zi-Xian Wang1,2,3, Yan-Xing Chen1,2
1Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou, 510060, P. R. China.
Background:
Although immune checkpoint inhibitor (ICI) is regarded as a breakthrough in cancer therapy, only a limited fraction of patients benefit from it. Cancer stemness can be the potential culprit in ICI resistance, but direct clinical evidence is lacking.
Methods:
Publicly available scRNA-Seq datasets derived from ICI-treated patients were collected and analyzed to elucidate the association between cancer stemness and ICI response. A novel stemness signature (Stem.Sig) was developed and validated using large-scale pan-cancer data, including 34 scRNA-Seq datasets, The Cancer Genome Atlas (TCGA) pan-cancer cohort, and 10 ICI transcriptomic cohorts. The therapeutic value of Stem.Sig genes was further explored using 17 CRISPR datasets that screened potential immunotherapy targets.
Results:
Cancer stemness, as evaluated by CytoTRACE, was found to be significantly associated with ICI resistance in melanoma and basal cell carcinoma (both P < 0.001). Significantly negative association was found between Stem.Sig and anti-tumor immunity, while positive correlations were detected between Stem.Sig and intra-tumoral heterogenicity (ITH) / total mutational burden (TMB). Based on this signature, machine learning model predicted ICI response with an AUC of 0.71 in both validation and testing set. Remarkably, compared with previous well-established signatures, Stem.Sig achieved better predictive performance across multiple cancers. Moreover, we generated a gene list ranked by the average effect of each gene to enhance tumor immune response after genetic knockout across different CRISPR datasets. Then we matched Stem.Sig to this gene list and found Stem.Sig significantly enriched 3% top-ranked genes from the list (P = 0.03), including EMC3, BECN1, VPS35, PCBP2, VPS29, PSMF1, GCLC, KXD1, SPRR1B, PTMA, YBX1, CYP27B1, NACA, PPP1CA, TCEB2, PIGC, NR0B2, PEX13, SERF2, and ZBTB43, which were potential therapeutic targets.
Conclusions:
We revealed a robust link between cancer stemness and immunotherapy resistance and developed a promising signature, Stem.Sig, which showed increased performance in comparison to other signatures regarding ICI response prediction. This signature could serve as a competitive tool for patient selection of immunotherapy. Meanwhile, our study potentially paves the way for overcoming immune resistance by targeting stemness-associated genes.
Insights
Cancer stemness is linked to resistance against immune checkpoint inhibitors (ICI). A new stemness signature (Stem.Sig) accurately predicts ICI response and identifies potential therapeutic targets for overcoming resistance.
Area of Science:
- Oncology
- Immunotherapy
- Genomics
Background:
- Immune checkpoint inhibitors (ICIs) offer breakthrough cancer therapy, but limited patient response necessitates understanding resistance mechanisms.
- Cancer stemness is a suspected driver of ICI resistance, yet direct clinical validation is lacking.
Purpose of the Study:
- To investigate the association between cancer stemness and ICI response.
- To develop and validate a novel stemness signature (Stem.Sig) for predicting ICI efficacy.
- To identify potential therapeutic targets for overcoming ICI resistance by analyzing stemness-associated genes.
Main Methods:
- Analysis of publicly available single-cell RNA sequencing (scRNA-Seq) datasets from ICI-treated patients.
- Development and pan-cancer validation of the Stem.Sig using TCGA and ICI transcriptomic cohorts.
- Exploration of therapeutic value of Stem.Sig genes using CRISPR screening datasets.
Main Results:
- Cancer stemness positively correlates with ICI resistance and intra-tumoral heterogeneity.
- The developed Stem.Sig accurately predicts ICI response (AUC=0.71) and outperforms existing signatures.
- Stem.Sig identified specific stemness-associated genes (e.g., EMC3, BECN1) as potential targets for enhancing immunotherapy.
Conclusions:
- A strong link exists between cancer stemness and immunotherapy resistance.
- The Stem.Sig is a promising tool for patient stratification in immunotherapy.
- Targeting stemness-associated genes may offer a strategy to overcome immune resistance.
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