Cell cycle activity correlates with increased anti‐tumor immunity in diverse cancers


 Objectives The cell cycle pathway regulating cell proliferation is overactivated in various cancers. Immune evasion is another important mechanism for tumor cell hyperproliferation. Nevertheless, the relationship between cell cycle and tumor immunity remains not fully understood. Materials and Methods Using the cancer genomics datasets for 10 cancer cohorts from the Cancer Genome Atlas (TCGA) program, we investigated the association between cell cycle activity (CCA) and anti-tumor immune signatures. We also explored the association between CCA and PD-L1 expression in these cancer cohorts. Moreover, we investigated the association between CCA and immunotherapy response in several cancer cohorts receiving immunotherapy. Results CCA likely exhibited positive associations with anti-tumor immune signatures (CD8+ T cell infiltration and immune cytolytic activity) in these cancer cohorts. The strong positive associations of CCA with DNA damage repair pathways and with tumor mutation load may explain the positive associations between CCA and anti-tumor immune signatures. Moreover, CCA displayed significant positive correlations with PD-L1 expression. Finally, we found that the enhanced CCA tended to be associated with unfavorable clinical outcomes in the TCGA cancer cohorts, though such association was not observed in the cancer cohorts receiving immune checkpoint blockade therapy. Conclusions CCA has significant positive associations with both anti-tumor immune signatures and tumor immune-suppressive signatures in diverse cancer types. Our findings provide new insights into cancer biology and potential clinical implications for cancer immunotherapy.

results indicate a positive correlation between CCA and anti-tumor immunity in cancer.
To investigate the predictability of CCA for anti-tumor immune signatures, we performed logistic regression analyses to estimate the contributions of CCA in predicting CD8+ T-cell infiltration levels and immune cytolytic activity. Because both tumor mutation burden (TMB) 2 and tumor aneuploidy level (TAL) 3,4 have been associated with anti-tumor immunity, we utilized three predictors (CCA, TMB, and TAL) to predict the tumor samples with high (upper third) versus low (bottom third) immune signature scores in logistic regression models. In pan-cancer, CCA (β coefficient: β = 0.41, P = 4.06 × 10 −6 ), TMB (β = 0.39, P = 0.001), and TAL (β = -0.73, P = 1.52 × 10 −13 ) displayed significant contributions in predicting CD8+ T-cell infiltration levels ( Figure 2A). The similar results were observed in predicting immune cytolytic activity. These results indicate that anti-tumor immunity has a significant positive association with CCA and TMB, while it has a significant negative association with TAL. Among the 10 individual cancer types, CCA was a significant positive predictor for CD8+ T-cell infiltration levels and immune cytolytic activity in seven and six cancer types, respectively ( Figure 2A). In these cancer types, although TMB also displayed the potential as a positive predictor, few of the contributions were statistically significant ( Figure 2A).In contrast, TAL was a significant negative predictor for CD8+ T-cell infiltration levels and immune cytolytic activity in six and five cancer types, respectively ( Figure 2A). These results suggest that CCA has stronger predictability for anti-tumor immunity than TMB and TAL in these cancer types.
Interestingly, we observed significant positive correlations between PD-L1 expression levels and CCA in pancancer and in nine individual cancer types ( Figure 2B). Moreover, 11 cell cycle pathway genes showed significant positive expression correlations with PD-L1 in at least five cancer types ( Figure 2C). Since the PD-L1 expression is a F I G U R E 1 Association of cell cycle activity with anti-tumor immune signatures in pan-cancer and in individual cancer types. A, Association of CCA with CD8+ T cell infiltration levels and immune cytolytic activity. Pearson's correlation test R and FDR are shown. CCA, cell cycle activity. It also applies to following figures. B, Comparison of the ratios of immune-stimulatory to immune-inhibitory signatures between higher-and lower-CCA tumors. The Mann-Whitney U test one-sided FDR are indicated. The higher-and lower-CCA tumors are the tumors whose CCA scores lie in the upper and bottom third, respectively. A ratio is the log2-transformed ratio of the geometric mean expression level of all marker genes of an immune-stimulatory signature to that of an immune-inhibitory signature in a tumor sample. C, Association of CCA with the expression levels of human leukocyte antigen (HLA) genes. D, Immune-related pathways which are highly enriched in higher-CCA tumors in at least 5 individual cancer types identified by KEGG. E, Association of the expression of cell cycle pathway genes with anti-tumor immune signatures. Pearson's correlation R values are indicated. FDR: false discovery rate, is the adjusted P values computed by the Benjamini and Hochberg method (*, FDR < 0.05; **, FDR < 0.01; ***, FDR < 0.001).

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LETTER TO EDITOR F I G U R E 1 Continued predictive biomarker for the active response to anti-PD-1/PD-L1 immunotherapy, 5 we anticipated that high-CCA cancer patients would have a more favorable response to immunotherapy. Indeed, in a urothelial cancer cohort 6 receiving anti-PD-L1 therapy, higher CCA cancer patients showed more favorable overall and disease-free survival trends than lower CCA patients ( Figure 2D). Moreover, the upregulation of CDK7, a member of the cyclin-dependent protein kinase family, was associated with a better survival in this cancer cohort ( Figure 2E). The positive cor- relation between CDK7 expression and survival prognosis could be attributed to the elevated anti-PD-L1 response rate in the cancers with higher CDK7 expression levels ( Figure 2E). Moreover, in another cancer cohort receiving anti-PD-1/PD-L1/CTLA-4 therapy, 7 we found a number of cell cycle pathway genes whose mutations were associated with better overall survival ( Figure 2F).
One possible reason why CCA can promote anti-tumor immunity could be that CCA enhances TMB. 8 Indeed, we observed significant positive correlations between CCA and TMB in pan-cancer and in five cancer types (Figure 2G). Moreover, we found several DNA damage repairassociated pathways, which were highly enriched in higher CCA tumors in at least five cancer types, including homologous recombination, DNA replication, p53 signaling, mismatch repair, base excision repair, nucleotide excision repair, and spliceosome. CCA displayed a strong positive correlation with the activity of these pathways in pan-cancer and in most individual cancer types (Figure 2H), indicating that the positive association between CCA and anti-tumor immunity is DNA damage repair mediated.
The tumors with higher CCA likely have more unfavorable clinical phenotypes, although they were associated with increased anti-tumor immunity. A possible explanation could be that CCA promotes tumor immunosuppressive signatures (such as PD-L1) as well, thereby counteracting the effect of elevated anti-tumor immunity. In fact, we found that the ratios of the mean expression levels of CD8A (CD8+ T cell marker) to PD-L1were significantly lower in higher CCA tumors than in lower CCA tumors in pan-cancer and in four cancer types ( Figure 2I). This indicates that CCA has a stronger positive correlation with the tumor immune-suppressive signature (PD-L1) than with the anti-tumor immune signature (CD8+ T cells) in these cancer cohorts.
In conclusion, CCA has significant positive associations with anti-tumor immunity in diverse cancer types. The combination of cell cycle inhibitors and immunotherapy should be cautious since CCA is positively associated with the response to immunotherapy.

A C K N O W L E D G M E N T
This work was supported by the China Pharmaceutical University (grant number 3150120001to X.W.).

C O N F L I C T O F I N T E R E S T
The authors declare no conflict of interest.

D ATA AVA I L A B I L I T Y S TAT E M E N T
All the data and materials are available upon reasonable request from the authors.

A U T H O R C O N T R I B U T I O N S
Shanmei Jiang performed data analyses and helped prepare for the manuscript. Yin He performed data analyses and helped prepare for the manuscript. Mengyuan Li performed data analyses. Xiaosheng Wang conceived this study, designed analysis strategies, and wrote the manuscript. All the authors read and approved the final manuscript.