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免疫疗法在治疗血液系统癌症合并感染中的潜力——一种数学建模方法

Potential of Immunotherapies in Treating Hematological Cancer-Infection Comorbidities-A Mathematical Modelling Approach.

作者信息

Ottesen Johnny T, Andersen Morten

机构信息

Center for Mathematical Modeling-Human Health and Disease (COMMAND), Roskilde University, 4000 Roskilde, Denmark.

IMFUFA, Department of Science and Environment, Roskilde University, 4000 Roskilde, Denmark.

出版信息

Cancers (Basel). 2021 Jul 27;13(15):3789. doi: 10.3390/cancers13153789.

DOI:10.3390/cancers13153789
PMID:34359690
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8345105/
Abstract

BACKGROUND

The immune system attacks threats like an emerging cancer or infections like COVID-19 but it also plays a role in dealing with autoimmune disease, e.g., inflammatory bowel diseases, and aging. Malignant cells may tend to be eradicated, to appraoch a dormant state or escape the immune system resulting in uncontrolled growth leading to cancer progression. If the immune system is busy fighting a cancer, a severe infection on top of it may compromise the immunoediting and the comorbidity may be too taxing for the immune system to control.

METHOD

A novel mechanism based computational model coupling a cancer-infection development to the adaptive immune system is presented and analyzed. The model maps the outcome to the underlying physiological mechanisms and agree with numerous evidence based medical observations.

RESULTS AND CONCLUSIONS

Progression of a cancer and the effect of treatments depend on the cancer size, the level of infection, and on the efficiency of the adaptive immune system. The model exhibits bi-stability, i.e., virtual patient trajectories gravitate towards one of two stable steady states: a dormant state or a full-blown cancer-infection disease state. An infectious threshold curve exists and if infection exceed this separatrix for sufficiently long time the cancer escapes. Thus, early treatment is vital for remission and severe infections may instigate cancer progression. CAR T-cell Immunotherapy may sufficiently control cancer progression back into a dormant state but the therapy significantly gains efficiency in combination with antibiotics or immunomodulation.

摘要

背景

免疫系统会攻击诸如新发癌症或新冠病毒感染等威胁,但它在应对自身免疫性疾病(如炎症性肠病)和衰老方面也发挥着作用。恶性细胞可能倾向于被根除、进入休眠状态或逃避免疫系统,从而导致不受控制的生长,进而引发癌症进展。如果免疫系统忙于对抗癌症,在此基础上发生的严重感染可能会损害免疫编辑,并且这种合并症对免疫系统的负担可能过重,以至于免疫系统无法控制。

方法

提出并分析了一种基于新型机制的计算模型,该模型将癌症 - 感染发展与适应性免疫系统相耦合。该模型将结果映射到潜在的生理机制,并与大量基于证据的医学观察结果相符。

结果与结论

癌症的进展和治疗效果取决于癌症大小、感染程度以及适应性免疫系统的效率。该模型呈现出双稳态,即虚拟患者轨迹趋向于两个稳定稳态之一:休眠状态或全面爆发的癌症 - 感染疾病状态。存在一条感染阈值曲线,如果感染超过这条分界线足够长的时间,癌症就会逃脱。因此,早期治疗对于缓解至关重要,严重感染可能会促使癌症进展。嵌合抗原受体(CAR)T细胞免疫疗法可能足以将癌症进展控制回休眠状态,但该疗法与抗生素或免疫调节联合使用时效率会显著提高。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b62/8345105/b2764053a5a1/cancers-13-03789-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b62/8345105/562b553bb7bf/cancers-13-03789-g0A1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b62/8345105/c286726d5f15/cancers-13-03789-g0A2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b62/8345105/4e09b7958532/cancers-13-03789-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b62/8345105/74fd7026e05d/cancers-13-03789-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b62/8345105/16be76b76fc8/cancers-13-03789-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b62/8345105/fb948ec1a86d/cancers-13-03789-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b62/8345105/6bf46fc5514a/cancers-13-03789-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b62/8345105/b2764053a5a1/cancers-13-03789-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b62/8345105/562b553bb7bf/cancers-13-03789-g0A1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b62/8345105/c286726d5f15/cancers-13-03789-g0A2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b62/8345105/4e09b7958532/cancers-13-03789-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b62/8345105/74fd7026e05d/cancers-13-03789-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b62/8345105/16be76b76fc8/cancers-13-03789-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b62/8345105/fb948ec1a86d/cancers-13-03789-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b62/8345105/6bf46fc5514a/cancers-13-03789-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b62/8345105/b2764053a5a1/cancers-13-03789-g006.jpg

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