KRAS-IRF2 Axis Drives Immune Suppression and Immune Therapy Resistance in Colorectal Cancer

Liao, Wen Ting; Overman, Michael J.; Boutin, Adam T.; Shang, Xiaoying; Zhao, Di; Dey, Prasenjit; Li, Jiexi; Wang, Guocan; Lan, Zhengdao; Li, Jun; Tang, Ming; Jiang, Shan; Ma, Xingdi; Chen, Peiwen; Katkhuda, Riham; Korphaisarn, Krittiya; Chakravarti, Deepavali; Chang, Andrew; Spring, Denise J.; Chang, Qing; Zhang, Jianhua; Maru, Dipen M.; Maeda, Dean Y.; Zebala, John A.; Kopetz, Scott; Wang, Y. Alan; DePinho, Ronald A.

doi:10.1016/j.ccell.2019.02.008

Cited by 411 publications

(375 citation statements)

References 64 publications

(85 reference statements)

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“…That is, a high expression of ELR (Glu–Leu–Arg) ‐positive CXC chemokines is not linked to tumor progression and worse prognosis in colorectal carcinoma in a linear fashion (Doll et al, ). In addition, W. Liao et al () found that CXCL3 was enhanced by KRAS‐IRF2 (interferon regulatory factor 2) axis, sequentially mediating immune suppression and immune therapy resistance in CRC.…”

Section: Discussionmentioning

confidence: 99%

“…That is, a high expression of ELR (Glu-Leu-Arg)positive CXC chemokines is not linked to tumor progression and worse prognosis in colorectal carcinoma in a linear fashion (Doll et al, 2010). In addition, W. Liao et al (2019) found that CXCL3 was enhanced by KRAS-IRF2 (interferon regulatory factor 2) axis, CXCL8 (also known as IL-8), like CXCL3, also a member of the CXC chemokine family and a major mediator of the inflammatory response, primarily serves as a chemotactic factor by guiding the neutrophils to the site of infection. In the studies of cancer, many researchers considered that CXCL8 plays an extremely crucial part in tumor proliferation, invasion, migration, and tumor microenvironment in an autocrine or paracrine manner (Ha, Debnath, & Neamati, 2017;Liu et al, 2016;Matsuo et al, 2009).…”

Section: Discussionmentioning

confidence: 99%

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Gene expression and methylation profiles identified CXCL3 and CXCL8 as key genes for diagnosis and prognosis of colon adenocarcinoma

Zhao

Jiang

Gao

et al. 2019

Journal Cellular Physiology

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Colon adenocarcinoma (COAD) is one of the most common malignant tumors with high morbidity and mortality rates worldwide. Due to the poor clinical outcomes, it is indispensable to investigate novel biomarkers for the diagnosis and prognosis of COAD. The aim of this study is to explore key genes as potential biomarkers for the diagnosis and prognosis of COAD for clinical utility. Gene expression profiles (GSE44076 and GSE44861) and gene methylation profile (GSE29490) were analyzed to identify the aberrantly methylated-differentially expressed genes by R language and Perl software. Function enrichments were performed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. Moreover, hub genes were identified through protein-protein interaction (PPI) network. Besides, key genes were found by the module analysis and The Cancer Genome Atlas (TCGA) survival analysis. Finally, TCGA data and quantitative real-time polymerase chain reaction (RT-qPCR) was used to validate the key genes involved in COAD. Our study found two hypomethylation-high-expression genes (CXCL3 and CXCL8) in COAD tissues compared with the adjacent normal tissues. These results were also confirmed by RT-qPCR with 25 pairs of COAD and adjacent normal tissues. Meanwhile, low expression of the two genes was associated with poor survival in patients with COAD. CXCL3 and CXCL8 may serve as key genes in the diagnosis and prognosis for COAD. K E Y W O R D Sbioinformatics, colon adenocarcinoma, CXCL3, CXCL8, key genes

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Gene expression and methylation profiles identified CXCL3 and CXCL8 as key genes for diagnosis and prognosis of colon adenocarcinoma

Zhao

Jiang

Gao

et al. 2019

Journal Cellular Physiology

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“…Although KRAS mutations do not predict response to anti-PD-1, they are associated with reduced T cell infiltration, hinting that downstream pathways could regulate immunotherapy and may become relevant as additional combinations of immune checkpoint inhibitors enter the clinic. In this issue of Cancer Cell, Liao et al (2019) demonstrate that Kras G12D suppresses expression of interferon regulatory factor 2 (IRF2), a negative regulator of the CXCL3 chemokine expression ( Figure 1). This leads to enhanced recruitment of myeloid-derived suppressor cells (MDSCs) and restricted T cell accumulation, a resistance mechanism that limits response to anti-PD-1 therapy.…”

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confidence: 99%

Oncogenic KRAS Drives Immune Suppression in Colorectal Cancer

Hänggi

Ruffell

2019

Cancer Cell

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“…Alterations of some top genes in our prediction, such as B2M, JAK1, JAK2, HSP90 , and IFNG , are known to be associated with poor response to anti-PD-1 therapy [7, 33-36]. Several genes that were recently found to be associated with anti-PD-1 response were also identified in the top list of our prediciton, such as KRAS [37], STK11 [38], DDR2 [39], and ATR [40].…”

Section: Resultsmentioning

confidence: 99%

A computational network approach to identify predictive biomarkers and therapeutic combinations for anti-PD-1 immunotherapy in cancer

Wang

Livingston

et al. 2020

Preprint

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24 Background: Despite remarkable success, only a subset of cancer patients have shown benefit from the anti-PD1 25 therapy. Therefore, there is a growing need to identify predictive biomarkers and therapeutic combinations for 26 improving the clinical efficacy. 27Results: Based upon the hypothesis that aberrations of any gene that are close to MHC class I genes in the gene 28 network are likely to deregulate MHC I pathway and affect tumor response to anti-PD1, we developed a network 29 approach to infer genes, pathway, and potential therapeutic target genes associated with response to PD-1/PD-L1 30 checkpoint immunotherapies in cancer. Our approach successfully identified genes (e.g. B2M and PTEN) and 31 pathways (e.g. JAK/STAT and WNT) known to be associated with anti-PD1 response. Our prediction was further 32 validated by 5 CRISPR gene sets associated with tumor resistance to cytotoxic T cells. Our results also showed that 33 many cancer genes that act as hubs in the gene network may drive immune evasion through indirectly deregulating 34 the MHC I pathway. The integration analysis of transcriptomic data of the 34 TCGA cancer types and our prediction 35 reveals that MHC I-immunoregulations may be tissue-specific. The signature-based score, the MHC I association 36 immunoscore (MIAS), calculated by integration of our prediction and TCGA melanoma transcriptomic data also 37 showed a good correlation with patient response to anti-PD1 for 354 melanoma samples complied from 5 cohorts. 38In addition, most targets of the 36 compounds that have been tested in clinical trials or used for combination 39 treatments with anti-PD1 are in the top list of our prediction (AUC=0.833). Integration of drug target data with our 40 top prediction further identified compounds that were recently shown to enhance tumor response to anti-PD1, such 41 as inhibitors of GSK3B, CDK, and PTK2. 42Conclusion: Our approach is effective to identify candidate genes and pathways associated with response to anti- 43PD-1 therapy, and can also be employed for in silico screening of potential compounds to enhances the efficacy of 44 anti-PD1 agents against cancer. 45 46 47 48 50 Breakthroughs in cancer immunotherapies have opened a new front in the war against cancer [1]. Instead of 51 directly targeting cancer cells using specific inhibitors, immunotherapies stimulate and modulate the host's 52 immune system to eliminate cancer cells. Recently, immune checkpoint blockade (ICB), which enhances T-cell 53 activity by inhibiting immunosuppressive checkpoint molecules such as cytotoxic T-lymphocyte-associated antigen 54 4 (CTLA-4), programmed cell death 1 (PD-1), and programmed cell death protein ligand 1 (PD-L1), has produced 55remarkably durable responses in some cancer patients. Despite these successes, only a subset of cancer patients 56 benefits from these therapies, and rates of response vary widely among cancer types. Therefore, there is a growing 57 need to understand the mechanisms underlying this de novo resistance, to select predictive biomar...

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KRAS-IRF2 Axis Drives Immune Suppression and Immune Therapy Resistance in Colorectal Cancer

Cited by 411 publications

References 64 publications

Gene expression and methylation profiles identified CXCL3 and CXCL8 as key genes for diagnosis and prognosis of colon adenocarcinoma

Gene expression and methylation profiles identified CXCL3 and CXCL8 as key genes for diagnosis and prognosis of colon adenocarcinoma

Oncogenic KRAS Drives Immune Suppression in Colorectal Cancer

A computational network approach to identify predictive biomarkers and therapeutic combinations for anti-PD-1 immunotherapy in cancer

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