Inhibition of the DNA damage response phosphatase PPM1D reprograms neutrophils to enhance anti-tumor immune responses

Uyanik, Burhan; Goloudina, Anastasia R.; Akbarali, Aamir; Grigorash, Bogdan B.; Petukhov, Alexey; Singhal, Sunil; Eruslanov, Evgeniy; Chaloyard, Jeanne; Lagorgette, Lisa; Hadi, Tarik; Baidyuk, E. V.; Sakai, Hiroyasu; Tessarollo, Lino; Ryffel, Bernhard; Mazur, Sharlyn J.; Lirussi, Frédéric; Garrido, Carmen; Appella, Ettore; Demidov, Oleg N.

doi:10.1038/s41467-021-23330-6

Cited by 19 publications

(13 citation statements)

References 92 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, PPM1D/Wip1 is a negative regulator of the cancer suppressor p53 and is overexpressed in several human solid cancers. Ppm1d knockout or chemical inhibition of Wip1 in human or mouse neutrophils exacerbates anticancer phenotypes and increases p53-dependent expression of costimulatory ligands and the proliferation of cocultured cytotoxic T cells [ 178 ]. Another study showed that exposure to β-glucan [ 179 ], a fungal-derived prototype agonist of trained immunity, trained neutrophils in mice to enhance the anticancer activity of neutrophils.…”

Section: Neutrophil Plasticity and The Cancer Microenvironmentmentioning

confidence: 99%

Neutrophils in cancer carcinogenesis and metastasis

2021

View full text Add to dashboard Cite

In recent years, neutrophils have attracted increasing attention because of their cancer-promoting effects. An elevated neutrophil-to-lymphocyte ratio is considered a prognostic indicator for patients with cancer. Neutrophils are no longer regarded as innate immune cells with a single function, let alone bystanders in the pathological process of cancer. Their diversity and plasticity are being increasingly recognized. This review summarizes previous studies assessing the roles and mechanisms of neutrophils in cancer initiation, progression, metastasis and relapse. Although the findings are controversial, the fact that neutrophils play a dual role in promoting and suppressing cancer is undeniable. The plasticity of neutrophils allows them to adapt to different cancer microenvironments and exert different effects on cancer. Given the findings from our own research, we propose a reasonable hypothesis that neutrophils may be reprogrammed into a cancer-promoting state in the cancer microenvironment. This new perspective indicates that neutrophil reprogramming in the course of cancer treatment is a problem worthy of attention. Preventing or reversing the reprogramming of neutrophils may be a potential strategy for adjuvant cancer therapy.

show abstract

Section: Neutrophil Plasticity and The Cancer Microenvironmentmentioning

confidence: 99%

Neutrophils in cancer carcinogenesis and metastasis

2021

View full text Add to dashboard Cite

show abstract

“…Particularly, the highest cancer type of CCT3 expression in the tumor microenvironment was COAD. As is known to all, the immune cells in tumor microenvironment play a central part in the immunotherapy ( Uyanik et al, 2021 ). Therefore, we wondered the relevance between CCT3 expression and immune characteristics.…”

Section: Resultsmentioning

confidence: 99%

Insights into the roles and driving forces of CCT3 in human tumors

Song²,

Liu³

et al. 2022

Front. Pharmacol.

View full text Add to dashboard Cite

CCT3 played a key role in many cancers. This study aimed to further explore the characteristics of CCT3 from a pan-cancer perspective and reveal the driving forces for CCT3. By bioinformatic analysis, we found that the mRNA and protein levels of CCT3 were abnormally elevated in most tumor types and were correlated with poor prognosis. Single-cell sequencing data indicated an abnormal increase of CCT3 expression in both malignant cells and multiple immune cells. In the tumor microenvironment, CCT3 expression was negatively relevant with immune cell infiltration and immune checkpoint genes expression. In colon cancer, knockdown of CCT3 inhibited cell proliferation. Gene set enrichment analysis showed that CCT3 may be oncogenic by regulating amino acid metabolism. Furthermore, we predicted sensitive drugs for CCT3 by virtual screening and sensitivity analysis. Many driver genes such as TP53 and KRAS were essential for CCT3 overexpression. Epigenetic factors, enhancers in particular, were also critical for CCT3 expression. Additionally, we constructed the lncRNA/circRNA-miRNA-CCT3 regulatory network. Collectively, CCT3 had the potential to be a diagnostic and prognostic biomarker for multiple tumor types. CCT3 expression was relevant with an immunosuppressive tumor microenvironment. CCT3 could be a new molecular target for colon cancer. Both genetic and epigenetic factors were responsible for CCT3 expression in tumors.

show abstract

“…Numerous studies have demonstrated that inflammatory mediators, important promoters of genetic alterations, play an important role in tumor initiation and progression ( 41 ). Specifically, inflammatory immune cells, such as macrophages and neutrophils, produce a wide variety of reactive substances, which could directly trigger DNA damage of nonimmune cells to further increase mutation frequency and genomic instability ( 42 ). When DNA sustains damage, specific proteins and enzymes are activated to repair such damage ( 43 ).…”

Section: Discussionmentioning

confidence: 99%

Application of machine learning in the prediction of deficient mismatch repair in patients with colorectal cancer based on routine preoperative characterization

Chen

Jiang

et al. 2022

Front. Oncol.

View full text Add to dashboard Cite

Simple summaryDetecting deficient mismatch repair (dMMR) in patients with colorectal cancer is essential for clinical decision-making, including evaluation of prognosis, guidance of adjuvant chemotherapy and immunotherapy, and primary screening for Lynch syndrome. However, outside of tertiary care centers, existing detection methods are not widely disseminated and highly depend on the experienced pathologist. Therefore, it is of great clinical significance to develop a broadly accessible and low-cost tool for dMMR prediction, particularly prior to surgery. In this study, we developed a convenient and reliable model for predicting dMMR status in CRC patients on routine preoperative characterization utilizing multiple machine learning algorithms. This model will work as an automated screening tool for identifying patients suitable for mismatch repair testing and consequently for improving the detection rate of dMMR, while reducing unnecessary labor and cost in patients with proficient mismatch repair.BackgroundDeficient mismatch repair (dMMR) indicates a sustained anti-tumor immune response and has a favorable prognosis in patients with colorectal cancer (CRC). Although all CRC patients are recommended to undergo dMMR testing after surgery, current diagnostic approaches are not available for all country hospitals and patients. Therefore, efficient and low-cost predictive models for dMMR, especially for preoperative evaluations, are warranted.MethodsA large scale of 5596 CRC patients who underwent surgical resection and mismatch repair testing were enrolled and randomly divided into training and validation cohorts. The clinical features exploited for predicting dMMR comprised the demographic characteristics, preoperative laboratory data, and tumor burden information. Machine learning (ML) methods involving eight basic algorithms, ensemble learning methods, and fusion algorithms were adopted with 10-fold cross-validation, and their performance was evaluated based on the area under the receiver operating characteristic curve (AUC) and calibration curves. The clinical net benefits were assessed using a decision curve analysis (DCA), and a nomogram was developed to facilitate model clinical practicality.ResultsAll models achieved an AUC of nearly 0.80 in the validation cohort, with the stacking model exhibiting the best performance (AUC = 0.832). Logistical DCA revealed that the stacking model yielded more clinical net benefits than the conventional regression models. In the subgroup analysis, the stacking model also predicted dMMR regardless of the clinical stage. The nomogram showed a favorable consistence with the actual outcome in the calibration curve.ConclusionWith the aid of ML algorithms, we developed a novel and robust model for predicting dMMR in CRC patients with satisfactory discriminative performance and designed a user-friendly and convenient nomogram.

show abstract

Inhibition of the DNA damage response phosphatase PPM1D reprograms neutrophils to enhance anti-tumor immune responses

Cited by 19 publications

References 92 publications

Neutrophils in cancer carcinogenesis and metastasis

Neutrophils in cancer carcinogenesis and metastasis

Insights into the roles and driving forces of CCT3 in human tumors

Application of machine learning in the prediction of deficient mismatch repair in patients with colorectal cancer based on routine preoperative characterization

Contact Info

Product

Resources

About