Integrated analysis of single-cell and bulk RNA sequencing data reveals a pan-cancer stemness signature predicting immunotherapy response

Abstract

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.

Germline stem cells in human

“… 375 It is interesting that ZBTB43 expression is also involved in cancer stemness in humans. 376 In addition, male reproductive aging is associated with the capacity decline of SSC niche, 363 , 377 thus, older males are often accompanied by a decrease in reproductive function.…”

A Novel Classification Model for Lower-Grade Glioma Patients Based on Pyroptosis-Related Genes

“…The “Combat” method is an empirical Bayes method, which can estimate parameters for location and scale adjustment of each batch for each gene independently [ 11 ]. Numerous previous studies have proven its effectiveness [ 12 , 13 , 14 ]. Therefore, we used the “Combat” method to remove the batch effect for batch effects between TCG-LGG and GTEx datasets.…”

An immunotherapy response prediction model derived from proliferative CD4+ T cells and antigen-presenting monocytes in ccRCC

“…To further justify the predictive capability of ccRCC.Sig, we compared ccRCC.Sig with 13 other ICB response signatures previously reported (8,(48)(49)(50)(51)(52)(53)(54)(55)(56)(57)(58), including the recognized IMPRES signature (51). The details of these signatures are listed in Supplementary Table 3.…”