Tumor-associated macrophages (TAMs) have been shown to both aid and hinder tumor growth, with patient outcomes potentially hinging on the proportion of M1, pro-inflammatory/growth-inhibiting, to M2, growth-supporting, phenotypes. Strategies to stimulate tumor regression by promoting polarization to M1 are a novel approach that harnesses the immune system to enhance therapeutic outcomes, including chemotherapy. We recently found that nanotherapy with mesoporous particles loaded with albumin-bound paclitaxel (MSV-nab-PTX) promotes macrophage polarization towards M1 in breast cancer liver metastases (BCLM). However, it remains unclear to what extent tumor regression can be maximized based on modulation of the macrophage phenotype, especially for poorly perfused tumors such as BCLM. Here, for the first time, a CRISPR system is employed to permanently modulate macrophage polarization in a controlled in vitro setting. This enables the design of 3D co-culture experiments mimicking the BCLM hypovascularized environment with various ratios of polarized macrophages. We implement a mathematical framework to evaluate nanoparticle-mediated chemotherapy in conjunction with TAM polarization. The response is predicted to be not linearly dependent on the M1:M2 ratio. To investigate this phenomenon, the response is simulated via the model for a variety of M1:M2 ratios. The modeling indicates that polarization to an all-M1 population may be less effective than a combination of both M1 and M2. Experimental results with the CRISPR system confirm this model-driven hypothesis. Altogether, this study indicates that response to nanoparticle-mediated chemotherapy targeting poorly perfused tumors may benefit from a fine-tuned M1:M2 ratio that maintains both phenotypes in the tumor microenvironment during treatment.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
Agent affecting macrophage polarization
Breast cancer liver metastasis
Bovine serum albumin
Clustered regularly interspaced short palindromic repeats
Fetal bovine serum
High molecular weight
Minimum essential medium
Magnetic resonance imaging
Mesoporous particles loaded with nab-PTX
Mammalian target of rapamycin
Non-essential amino acids
Optimal cutting temperature
Polymerase chain reaction
Rapamycin-insensitive companion of mTOR
Small interfering RNA
Tris-buffered saline with Tween-20
Wyld L et al (2003) Prognostic factors for patients with hepatic metastases from breast cancer. Br J Cancer 89(2):284–290
van den Eynden GG et al (2013) The multifaceted role of the microenvironment in liver metastasis: biology and clinical implications. Can Res 73(7):2031–2043
Stessels F et al (2004) Breast adenocarcinoma liver metastases, in contrast to colorectal cancer liver metastases, display a non-angiogenic growth pattern that preserves the stroma and lacks hypoxia. Br J Cancer 90(7):1429–1436
Ma R et al (2015) Mechanisms involved in breast cancer liver metastasis. J Transl Med 13:64
Braga L et al (2004) Does hypervascularity of liver metastases as detected on MRI predict disease progression in breast cancer patients? AJR Am J Roentgenol 182(5):1207–1213
Liu LX, Zhang WH, Jiang HC (2003) Current treatment for liver metastases from colorectal cancer. World J Gastroenterol 9(2):193–200
Pezzella F, Gatter KC (2016) Evidence showing that tumors can grow without angiogenesis and can switch between angiogenic and nonangiogenicphenotypes. J Natl Cancer Inst 108(8):djw032
Leonard F et al (2016) Enhanced performance of macrophage-encapsulated nanoparticle albumin-bound-paclitaxel in hypo-perfused cancer lesions. Nanoscale 8(25):12544–12552
Daly JM et al (1985) Predicting tumor response in patients with colorectal hepatic metastases. Ann Surg 202(3):384–393
Coussens LM, Werb Z (2002) Inflammation and cancer. Nature 420(6917):860–867
Balkwill F, Charles KA, Mantovani A (2005) Smoldering and polarized inflammation in the initiation and promotion of malignant disease. Cancer Cell 7(3):211–217
Martinez FO (2011) Regulators of macrophage activation. Eur J Immunol 41(6):1531–1534
Sica A, Mantovani A (2012) Macrophage plasticity and polarization: in vivo veritas. J Clin Investig 122(3):787–795
Jakubzick CV, Randolph GJ, Henson PM (2017) Monocyte differentiation and antigen-presenting functions. Nat Rev Immunol 17:349–362
Galdiero MR et al (2013) Tumor associated macrophages and neutrophils in cancer. Immunobiology 218(11):1402–1410
Mills CD (2015) Anatomy of a discovery: m1 and m2 macrophages. Front Immunol 6:212
Sica A et al (2006) Tumour-associated macrophages are a distinct M2 polarised population promoting tumour progression: potential targets of anti-cancer therapy. Eur J Cancer 42(6):717–727
Cao W et al (2015) Macrophage subtype predicts lymph node metastasis in oesophageal adenocarcinoma and promotes cancer cell invasion in vitro. Br J Cancer 113(5):738–746
Pantano F et al (2013) The role of macrophages polarization in predicting prognosis of radically resected gastric cancer patients. J Cell Mol Med 17(11):1415–1421
Georgoudaki A-M et al (2016) Reprogramming tumor-associated macrophages by antibody targeting inhibits cancer progression and metastasis. Cell Rep 15(9):2000–2011
Fuchs AK et al (2016) Carboxyl- and amino-functionalized polystyrene nanoparticles differentially affect the polarization profile of M1 and M2 macrophage subsets. Biomaterials 85:78–87
Oronsky B et al (2017) RRx-001: a systemically non-toxic M2-to-M1 macrophage stimulating and prosensitizing agent in Phase II clinical trials. Expert Opin Investig Drugs 26(1):109–119
Nathan MR, Schmid P (2017) The emerging world of breast cancer immunotherapy. Breast 37:200–206
Lewis C, Murdoch C (2005) Macrophage responses to hypoxia: implications for tumor progression and anti-cancer therapies. Am J Pathol 167(3):627–635
Leonard F et al (2017) Macrophage polarization contributes to the anti-tumoral efficacy of mesoporous nanovectors loaded with albumin-bound paclitaxel. Front Immunol 8:693
Leonard F, Godin B (2018) Agents for macrophage polarization. Houston Methodist, Houston
Babaev VR et al (2018) Loss of rictor in monocyte/macrophages suppresses their proliferation and viability reducing atherosclerosis in LDLR null mice. Front Immunol 9:215
Festuccia WT et al (2014) Myeloid-specific Rictor deletion induces M1 macrophage polarization and potentiates in vivo pro-inflammatory response to lipopolysaccharide. PLoS ONE 9(4):e95432
Refuerzo JS et al (2015) Liposomes: a nanoscale drug carrying system to prevent indomethacin passage to the fetus in a pregnant mouse model. Am J Obstet Gynecol 212(4):508 e1–7
Macklin P et al (2009) Multiscale modelling and nonlinear simulation of vascular tumour growth. J Math Biol 58(4–5):765–798
Wu M et al (2013) The effect of interstitial pressure on tumor growth: coupling with the blood and lymphatic vascular systems. J Theor Biol 320:131–151
McDougall SR, Anderson ARA, Chaplain MAJ (2006) Mathematical modelling of dynamic adaptive tumour-induced angiogenesis: clinical implications and therapeutic targeting strategies. J Theor Biol 241(3):564–589
Mahlbacher G et al (2018) Mathematical modeling of tumor-associated macrophage interactions with the cancer microenvironment. J Immunother Cancer 6(1):10
van de Ven AL et al (2012) Integrated intravital microscopy and mathematical modeling to optimize nanotherapeutics delivery to tumors. AIP Adv 2(1):11208
Curtis LT, Frieboes HB (HB) Modeling of combination chemotherapy and immunotherapy for lung cancer. In: 41st Annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, Berlin, Germany, pp 273–276
Hallowell RW et al (2017) mTORC2 signalling regulates M2 macrophage differentiation in response to helminth infection and adaptive thermogenesis. Nat Commun 8:14208
Ambarus CA et al (2012) Systematic validation of specific phenotypic markers for in vitro polarized human macrophages. J Immunol Methods 375(1–2):196–206
Porcheray F et al (2005) Macrophage activation switching: an asset for the resolution of inflammation. Clin Exp Immunol 142(3):481–489
Maeda A et al (2019) Poly(I:C) stimulation is superior than Imiquimod to induce the antitumoral functional profile of tumor-conditioned macrophages. Eur J Immunol 49(5):801–811
Jablonski KA et al (2015) Novel markers to delineate murine M1 and M2 macrophages. PLoS ONE 10(12):e0145342
Brown JM, Recht L, Strober S (2017) The promise of targeting macrophages in cancer therapy. Clin Cancer Res 23(13):3241–3250
Mills CD, Lenz LL, Harris RA (2016) A breakthrough: macrophage-directed cancer immunotherapy. Cancer Res 76(3):513–516
Mills CD et al (2000) M-1/M-2 macrophages and the Th1/Th2 paradigm. J Immunol 164(12):6166–6173
Pyonteck SM et al (2013) CSF-1R inhibition alters macrophage polarization and blocks glioma progression. Nat Med 19(10):1264–1272
Tariq M et al (2017) Macrophage polarization: anti-cancer strategies to target tumor-associated macrophage in breast cancer. J Cell Biochem 118(9):2484–2501
Poh AR, Ernst M (2018) Targeting macrophages in cancer: from bench to bedside. Front Oncol 8:49
Mahlbacher GE, Reihmer KC, Frieboes HB (2019) Mathematical modeling of tumor-immune cell interactions. J Theor Biol 469:47–60
Tanei T et al (2016) Redirecting transport of nanoparticle albumin-bound paclitaxel to macrophages enhances therapeutic efficacy against liver metastases. Can Res 76(2):429–439
Vogel SN, Carboni JM, Manthey CL (1994) Paclitaxel, a mimetic of bacterial lipopolysaccharide (LPS) in murine macrophages, in taxane anticancer agents. In: Georg GI, Chen TT, Ojima I (eds) American Chemical Society, Washington, DC, pp 162–172
Leonard acknowledges Houston Methodist Research Institute Department of Nanomedicine Innovative Grant Award and METAvivor Foundation Early Career Investigator Award. Leonard and Godin gratefully acknowledge funding from George and Angelina Kostas Research Center for Cardiovascular Nanomedicine Grant. Frieboes acknowledges partial support by the National Institutes of Health/National Cancer Institute Grant R15CA203605.
Conflict of interest
The authors have no conflicts to disclose.
Ethical approval and ethical standards
In vivo mouse studies were performed in accordance with the Houston Methodist Research Institute Institutional Animal Care and Use Committee (IACUC—approval number: AUP-0617–0020). The animal research was conducted in full compliance with federal, state, and local regulations and institutional policies.
Balb/c mice (6–8 weeks, females) were purchased from Jackson laboratory for all of the animal experiments in this study.
Cell line authentication
4T1 mouse breast cancer cells were purchased from the American Type Culture Collection (ATCC) (Manassas, VA, USA), which tests and authenticates the cells in its collection.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
About this article
Cite this article
Leonard, F., Curtis, L.T., Hamed, A.R. et al. Nonlinear response to cancer nanotherapy due to macrophage interactions revealed by mathematical modeling and evaluated in a murine model via CRISPR-modulated macrophage polarization. Cancer Immunol Immunother (2020). https://doi.org/10.1007/s00262-020-02504-z
- Cancer immunotherapy
- Macrophage polarization
- Breast cancer liver metastases
- Mathematical modeling
- computational simulation