APOBEC3-mediated mutagenesis in cancer: causes, clinical significance and therapeutic potential

Butler, Kelly Jean; Banday, Abdul Rouf

doi:10.1186/s13045-023-01425-5

Cited by 23 publications

(10 citation statements)

References 209 publications

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“…24 The APOBEC3 family is highly expressed in macrophages, lymphoid cells, and dendritic cells, and dysregulated APOBEC3 activity has been implicated in genome mutagenesis in cancer. [24][25][26] Notably, high expression of APOBEC3G, one of the APOBEC3 genes, has shown a strong correlation with T-cell infiltration and improved outcomes in patients with high-grade serous ovarian carcinoma. 27 APOBEC3G also exhibited significant positive correlations with PD-L1, suggesting that APOBEC-driven mutagenesis may contribute to an active immune microenvironment in estrogen receptor-positive breast cancer.…”

Section: Discussionmentioning

confidence: 99%

“…This highlights the potential role of RNA modification processes in shaping the response to ICIs. APOBEC3, a member of the large apolipoprotein B mRNA editing enzyme catalytic polypeptide‐like family, plays a critical role in innate immunity by inducing mutations in viral DNA and restricting viral replication 24 . The APOBEC3 family is highly expressed in macrophages, lymphoid cells, and dendritic cells, and dysregulated APOBEC3 activity has been implicated in genome mutagenesis in cancer 24–26 .…”

Section: Discussionmentioning

confidence: 99%

“…APOBEC3, a member of the large apolipoprotein B mRNA editing enzyme catalytic polypeptide‐like family, plays a critical role in innate immunity by inducing mutations in viral DNA and restricting viral replication 24 . The APOBEC3 family is highly expressed in macrophages, lymphoid cells, and dendritic cells, and dysregulated APOBEC3 activity has been implicated in genome mutagenesis in cancer 24–26 . Notably, high expression of APOBEC3G, one of the APOBEC3 genes, has shown a strong correlation with T‐cell infiltration and improved outcomes in patients with high‐grade serous ovarian carcinoma 27 .…”

Section: Discussionmentioning

confidence: 99%

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Unraveling the role of M1 macrophage and CXCL9 in predicting immune checkpoint inhibitor efficacy through multicohort analysis and single‐cell RNA sequencing

Yu,

Chen,

Ouyang

et al. 2024

MedComm

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The exact function of M1 macrophages and CXCL9 in forecasting the effectiveness of immune checkpoint inhibitors (ICIs) is still not thoroughly investigated. We investigated the potential of M1 macrophage and C‐X‐C Motif Chemokine Ligand 9 (CXCL9) as predictive markers for ICI efficacy, employing a comprehensive approach integrating multicohort analysis and single‐cell RNA sequencing. A significant correlation between high M1 macrophage and improved overall survival (OS) and objective response rate (ORR) was found. M1 macrophage expression was most pronounced in the immune‐inflamed phenotype, aligning with increased expression of immune checkpoints. Furthermore, CXCL9 was identified as a key marker gene that positively correlated with M1 macrophage and response to ICIs, while also exhibiting associations with immune‐related pathways and immune cell infiltration. Additionally, through exploring RNA epigenetic modifications, we identified Apolipoprotein B MRNA Editing Enzyme Catalytic Subunit 3G (APOBEC3G) as linked to ICI response, with high expression correlating with improved OS and immune‐related pathways. Moreover, a novel model based on M1 macrophage, CXCL9, and APOBEC3G‐related genes was developed using multi‐level attention graph neural network, which showed promising predictive ability for ORR. This study illuminates the pivotal contributions of M1 macrophages and CXCL9 in shaping an immune‐active microenvironment, correlating with enhanced ICI efficacy. The combination of M1 macrophage, CXCL9, and APOBEC3G provides a novel model for predicting clinical outcomes of ICI therapy, facilitating personalized immunotherapy.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Unraveling the role of M1 macrophage and CXCL9 in predicting immune checkpoint inhibitor efficacy through multicohort analysis and single‐cell RNA sequencing

Yu,

Chen,

Ouyang

et al. 2024

MedComm

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“… 39 An example of stress mediated mutagenesis would be the erroneous activation of APOBEC3 enzyme, which mutagenizes by cytosine deamination and causes 75% of kataegis. 40 Another example of stress‐induced mutagenesis is presented by double strand break inducing cancer therapies, such as etoposide and doxorubicin, which are likely to provoke stress response–dependent error‐prone repair mechanisms. 41 Cetuximab treatment is an effective therapy against KRAS/NRAS CRC, however, resistance inevitably emerges.…”

Section: Discussionmentioning

confidence: 99%

Analysis of translesion polymerases in colorectal cancer cells following cetuximab treatment: A network perspective

Das,

Gkoutos,

Acharjee

2024

Cancer Medicine

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IntroductionAdaptive mutagenesis observed in colorectal cancer (CRC) cells upon exposure to EGFR inhibitors contributes to the development of resistance and recurrence. Multiple investigations have indicated a parallel between cancer cells and bacteria in terms of exhibiting adaptive mutagenesis. This phenomenon entails a transient and coordinated escalation of error‐prone translesion synthesis polymerases (TLS polymerases), resulting in mutagenesis of a magnitude sufficient to drive the selection of resistant phenotypes.MethodsIn this study, we conducted a comprehensive pan‐transcriptome analysis of the regulatory framework within CRC cells, with the objective of identifying potential transcriptome modules encompassing certain translesion polymerases and the associated transcription factors (TFs) that govern them. Our sampling strategy involved the collection of transcriptomic data from tumors treated with cetuximab, an EGFR inhibitor, untreated CRC tumors, and colorectal‐derived cell lines, resulting in a diverse dataset. Subsequently, we identified co‐regulated modules using weighted correlation network analysis with a minKMEtostay threshold set at 0.5 to minimize false‐positive module identifications and mapped the modules to STRING annotations. Furthermore, we explored the putative TFs influencing these modules using KBoost, a kernel PCA regression model.ResultsOur analysis did not reveal a distinct transcriptional profile specific to cetuximab treatment. Moreover, we elucidated co‐expression modules housing genes, for example, POLK, POLI, POLQ, REV1, POLN, and POLM. Specifically, POLK, POLI, and POLQ were assigned to the “blue” module, which also encompassed critical DNA damage response enzymes, for example. BRCA1, BRCA2, MSH6, and MSH2. To delineate the transcriptional control of this module, we investigated associated TFs, highlighting the roles of prominent cancer‐associated TFs, such as CENPA, HNF1A, and E2F7.ConclusionWe found that translesion polymerases are co‐regulated with DNA mismatch repair and cell cycle‐associated factors. We did not, however, identified any networks specific to cetuximab treatment indicating that the response to EGFR inhibitors relates to a general stress response mechanism.

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“…In addition, colocalization analyses of gene expression with in ammatory cytokines con rmed the presence of common genetic effects of these gene expressions (APOBEC3G, IFI27L2, TMEM50B, and SH2B3) with in ammatory cytokines. APOBEC3G (Apolipoprotein B MRNA Editing Enzyme Catalytic Subunit 3G), one of the APOBEC3 genes, is a member of the large apolipoprotein B mRNA editing enzyme catalytic polypeptide-like family which plays a critical role in innate immunity 90 . The APOBEC3 family is highly expressed in macrophages, lymphoid cells, and dendritic cells, and dysregulated APOBEC3 activity has been implicated in genome mutagenesis in cancer 90,91 .…”

Section: Discussionmentioning

confidence: 99%

Interplay of Interferon signalling gene expression, DNA Methylation, and inflammatory cytokines in Sjögren’s Syndrome: a multi-omics Mendelian randomization study

He,

Pang,

Shan

et al. 2024

Preprint

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Background Abnormal activation of the interferon (IFN) signalling plays a central role in the progression of Sjögren’s syndrome (SS). However, the causal relationship between IFN signalling and SS remains unclear, with complex interactions existing among genetic variants, epigenetic modifications, inflammatory cytokine levels, and the expression of IFN-associated genes. Thus, in order to reveal the potential causality and interaction mechanisms among IFN-associated gene expression, DNA methylation, inflammatory cytokines, and SS, our analysis was conducted using a multi-omics summary data-based Mendelian randomization (SMR) approach. Methods Genes associated with IFN signalling were extracted from the GeneCards database, and transcriptomic datasets for SS were obtained from the Gene Expression Omnibus (GEO) database. Linear regression models and meta-analysis identified IFN-associated differentially expressed genes (DEGs) in SS. Using a three-step SMR method, an integrated analysis of expression quantitative trait loci (eQTLs) and DNA methylation QTLs (mQTLs) with SS genome-wide association study (GWAS) from FinnGen was performed to reveal causal relationships between blood IFN-associated gene expression, DNA methylation, and SS pathogenesis. Then use SS GWAS data from UK Biobank for validation. Through colocalization analysis, integrating analysis of blood IFN-associated causal genes eQTLs with inflammatory cytokines GWAS was performed to identify potential interactions between blood IFN gene expression and inflammatory cytokines. Meanwhile, minor salivary gland (MSG) tissue eQTLs from GTEx V8 and SS GWAS were integrated by SMR to identify MSG IFN-associated causal genes. Through colocalization analysis, integrating analysis of MSG IFN-associated causal genes eQTLs with inflammatory cytokines GWAS was performed to identify potential interactions between IFN-associated causal gene expression in MSG and inflammatory cytokines. Results A total of 331 IFN-associated DEGs were identified by integrative analysis of three transcriptomic datasets and 711 IFN-associated genes. These DEGs are predominantly enriched in T-cells, macrophages, monocytes, and natural killer cells. Five blood IFN-associated genes: SH2B3, LGALS9, CD40, GRB2, and DTX3L, were identified as SS-causal genes using a three-step SMR approach. Three of these genes, LGALS9, SH2B3, and CD40, are involved in the interaction between gene expression and inflammatory cytokines through colocalization analysis. Furthermore, SMR and colocalization analysis also identified thirteen putative MSG IFN-associated genes, four of which were involved in gene–inflammatory cytokines interactions: APOBEC3G, IFI27L2, TMEM50B, and SH2B3. Conclusions This study uncovered a causal relationship between interferon signalling and SS, revealing complex interactions among IFN-associated causal gene expression, DNA methylation, and inflammatory cytokines in SS pathogenesis. This offers new evidence for the involvement of interferon signalling in the pathogenic process of SS and provides fresh insights into the interactions among epigenetic, genetic variants, and inflammatory cytokines for in-depth studies of pathogenesis and molecular mechanisms.

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APOBEC3-mediated mutagenesis in cancer: causes, clinical significance and therapeutic potential

Cited by 23 publications

References 209 publications

Unraveling the role of M1 macrophage and CXCL9 in predicting immune checkpoint inhibitor efficacy through multicohort analysis and single‐cell RNA sequencing

Unraveling the role of M1 macrophage and CXCL9 in predicting immune checkpoint inhibitor efficacy through multicohort analysis and single‐cell RNA sequencing

Analysis of translesion polymerases in colorectal cancer cells following cetuximab treatment: A network perspective

Interplay of Interferon signalling gene expression, DNA Methylation, and inflammatory cytokines in Sjögren’s Syndrome: a multi-omics Mendelian randomization study

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