Cancer systems immunology

Reticker-Flynn, Nathan E.; Engleman, Edgar G.

doi:10.7554/elife.53839

Cited by 22 publications

(16 citation statements)

References 681 publications

(794 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Going forward, we encourage further refinement of the proposals that articulate microbiota-cancer-host 'axes' [18,92,111], 'networks' [72,86], and 'systems' [88][89][90][91]. Clarifying the precise components of these networks and how they interact holds promise to help contextualize novel biomarkers, to specify other causal mechanisms, and to rationally guide therapeutic interventions that focus on the most effective targets.…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 94%

See 1 more Smart Citation

Redrawing therapeutic boundaries: microbiota and cancer

et al. 2022

View full text Add to dashboard Cite

The unexpected roles of the microbiota in cancer challenge explanations of carcinogenesis that focus on tumor-intrinsic properties. Most tumors contain bacteria and viruses, and the host's proximal and distal microbiota influence both cancer incidence and therapeutic responsiveness. Continuing the history of cancer-microbe research, these findings raise a key question: to what extent is the microbiota relevant for clinical oncology? We approach this by critically evaluating three issues: how the microbiota provides a predictive biomarker of cancer growth and therapeutic responsiveness, the microbiota's causal role(s) in cancer development, and how therapeutic manipulations of the microbiota improve patient outcomes in cancer. Clarifying the conceptual and empirical aspects of the cancer-associated microbiota can orient future research and guide its implementation in clinical oncology.

show abstract

Section: Conclusion and Future Perspectivesmentioning

confidence: 94%

“…One proposed way forward is to incorporate ideas from systems medicine or ecology [72,[86][87][88][89][90][91][92]. For instance, we can track how perturbations in one part of the microbe-host ecology will result in adjustments, compensations, or disruptions to other parts of this system [93].…”

Section: Reevaluating Causalitymentioning

confidence: 99%

Redrawing therapeutic boundaries: microbiota and cancer

et al. 2022

View full text Add to dashboard Cite

show abstract

“…ESTIMATE is one of the widely used algorithms for quantifying the stromal and immune components in the TME of malignant tumor tissues [22]. It has shown effectiveness in a variety of malignancies, including breast cancer, urothelial cancer, multiple myeloma and neck squamous cell carcinoma [23][24][25][26][27][28][29] In this study, we estimated the immune components of the TME and the EGFR-TKI resistance related genes and then identified reliable prognostic biomarkers for LUAD. The newly found genes might render tumor cells more sensitive to EGFR-TKIs and immunotherapy for LUAD patients.…”

Section: Ivyspring International Publishermentioning

confidence: 99%

Identification of tumor microenvironment‐based genes associated with acquired resistance to EGFR Tyrosine Kinase Inhibitor in Lung Adenocarcinoma

Chen¹,

Li²,

Liu³

et al. 2022

J. Cancer

View full text Add to dashboard Cite

Background:The tumor microenvironment evidently affects treatment response and clinical outcome. This study aims to construct a tumor microenvironment-based crosstalk between immunotherapy and epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) in lung adenocarcinoma. Methods: We used ESTIMATE algorithm to calculate stromal and immune scores. Differentially expressed genes (DEGs) were extracted based on the comprehensive analysis of immune score groups and EGFR-TKI resistance samples. The independent prognostic value of the five selected genes was assessed by univariate/multivariate Cox regression analysis, survival analysis and the receiver operating characteristic (ROC) curve. Correlation analysis was performed using Spearman's rho value through TIMER 2.0. Results: The Kaplan-Meier survival curve show that patients with higher immune scores have significantly better overall survival. We identified 1328 DEGs from immune score groups and 806 DEGs from the EGFR-TKI resistance cohort GSE123066. A total of 19 co-regulated genes were found, and the Cox regression model produced a significant statistical prognosis for five genes (CENPF, CYSLTR1, GLDN, PIGR and SCGB3A1). Multivariate Cox regression analysis showed that the selected five gene signatures could be used as independent prognostic indicators. Furthermore, GSEA and correlation analysis demonstrated that CENPF was positively correlated to the signalling pathway which related to EGFR-TKI resistance and the well-known bypass gene. Conclusion:Our findings indicate that CENPF, CYSLTR1, GLDN, PIGR and SCGB3A1 are independent prognostic biomarkers associated with acquired EGFR-TKI resistance and tumor immune cell infiltration in lung adenocarcinoma, and CENPF may be a potential target that can improve immunotherapy efficacy and overcome the acquired EGFR-TKI resistance.

show abstract

“…These two methods complement each other in terms of throughput and resolution, and they need to be selected based on the question of interest. Using those technologies, we can define the characteristics of immune cells in the tumor microenvironment and their interaction with tumor cells, leading to a better understanding of the tumor‐immune ecosystem 43,44 …”

Section: Single‐cell Genomics: a Powerhouse For Advancing Tumor Immunology And Immunotherapy Researchmentioning

confidence: 99%

Tumor immunology in the age of single-cell genomics

Zhao

Ren

Gao

et al. 2021

Journal of Leukocyte Biology

View full text Add to dashboard Cite

Immunotherapies that were developed based on our understandings of tumor immunology have revolutionized cancer treatment. However, the success of immunotherapy is eclipsed by several grand challenges, including low response rate, intrinsic/acquired resistance and adverse effects. While a deeper understanding of the interaction between tumor and our immune system, especially the tumor immune niche, is essential to overcome those challenges, we are limited by the fact that most of our knowledge about tumor immunology is based on studies analyzing bulk populations of cells, which are often unable to fully characterize the various cell types and states engaged in immune cell functions. The advent of cutting single‐cell genomic technologies empowers us to dissect the tumor immune niche in a genome‐wide and spatially resolved manner in single cells, trace their clonal histories, and unveil their regulatory circuits. Future studies on tumor immunology in the age of single‐cell genomics, therefore, hold the promise to develop more effective and precise immunotherapies for human cancers. In this perspective, we will discuss how advanced single‐cell genomics approaches will revolutionize tumor immunology research and immunotherapies by catering the demand in the field of tumor immunology.

show abstract

Cancer systems immunology

Cited by 22 publications

References 681 publications

Redrawing therapeutic boundaries: microbiota and cancer

Redrawing therapeutic boundaries: microbiota and cancer

Identification of tumor microenvironment‐based genes associated with acquired resistance to EGFR Tyrosine Kinase Inhibitor in Lung Adenocarcinoma

Tumor immunology in the age of single-cell genomics

Contact Info

Product

Resources

About