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Resources User-Developed Protocols 2. PDF 67KB. PDF KB. Instrument User Manuals 2. PDF 1MB. Software User Guides 3. Not significant, according to multiple t test with Bonferroni correction. To investigate if the mechanisms of action of propranolol extended beyond anti-angiogenic effects, we analyzed if propranolol affected the infiltration of T cells.
The cellular composition of the lymphoid compartment of the MCA tumors was analyzed by multicolor flow cytometry.
We did not observe changes in the abundance of NK cells in the tumors Supplementary Fig. A — F Single cell suspensions were made from excised MCA tumors at the experimental endpoint and analyzed by flow cytometry. For tumor growth and Kaplan—Meier curves, statistical analyses were performed using TumGrowth software. For other comparisons, multiple t tests with Bonferroni correction for multiple comparison were used. Not significant.
To directly test if the therapeutic efficacy of propranolol is dependent on the presence of T cells, we inoculated T cell deficient nude mice with MCA cells and treated them with propranolol. In nude mice, the growth rate of MCA tumors and survival rate of mice were comparable between control and propranolol treated mice Fig.
An experiment with immune competent mice was conducted using the same cell line stock concurrently, which confirmed the anti-tumor effect of propranolol in immune competent mice data not shown. These results indicated that the anti-tumor effect of propranolol depends on the presence of T cells. Since immunosuppressive cells of the myeloid lineage can contribute to tumor progression in a variety of cancer types and influence the recruitment and function of T cells [ 30 ], we investigated if propranolol affected the cellular composition of the myeloid compartment in the TME of MCA tumors.
Flow cytometry-based analysis of the TME showed that propranolol treatment led to a striking reduction in the abundance of intratumoral myeloid-derived suppressor cells MDSCs Fig. No difference was observed in the numbers of tumor associated macrophages TAMs Fig. However, interestingly, we found an increase in PDL1 expression Fig.
There was no difference in the expression level of the M2 marker CD Fig. In summary, our data suggest that macrophages can be affected directly by the blockade of ADRB. Multiple t tests with Bonferroni correction for multiple comparison were used for statistical testing. The differentiated macrophages were treated with the pan-ADRB agonist isoprenaline or with propranolol. The expression of PDL1 was increased by propranolol treatment while isoprenaline had no effect Fig.
Data show two biological replicates. G Gene ontology analysis showing the biological processes most significantly enriched within genes that are differentially expressed between TAMs isolated from control mice or propranolol treated mice. We identified genes, which were regulated by more than 1.
The top ten most upregulated and down regulated genes are listed in Supplementary Table 3 , and the complete list of differentially regulated genes can be found in Supplementary Table 4. Gene ontology enrichment analysis revealed that the differentially regulated genes were involved in biological processes, including inflammatory response, myeloid leukocyte migration, and granulocyte migration Fig. First, propranolol was combined with anti-PDL1 treatment. MCA tumors were minimally responsive to anti-PDL1 treatment, and the tumor growth and survival rate of mice were comparable between the two ICI treated groups with or without propranolol Supplementary Fig.
S4A, B. Similar results were obtained when propranolol was combined with anti-PD1 treatment Supplementary Fig. Next, we tested the effect of anti-CTLA4 in combination with propranolol. Interestingly, the addition of propranolol further delayed tumor growth and improved the survival rate compared to anti-CTLA4 alone Fig. To investigate if the anti-tumor effects of propranolol and its ability to enhance the efficacy of anti-CTLA4 therapy also applied to other cancer types, we performed a similar experiment using the MC38 colon cancer model.
We observed a similar anti-tumor effect of propranolol as a single agent as well as increased therapeutic efficacy when combined with anti-CTLA4 therapy Fig. To gain insight into the mechanism of action of the observed synergistic effect, we performed immunohistochemical staining of CD8 and CD34 on paraffin embedded MCA tumor tissue sections from mice treated with anti-CTLA4 with or without propranolol.
The anti-angiogenic effect of propranolol as a single agent treatment was also observed when used in combination with anti-CTLA4 in both the MCA Fig. S5A, B. A Treatment regimen and experimental setup of immune checkpoint inhibitor ICI tumor studies.
Each line represents one animal. Pie charts indicate the response rate of treated mice. Therapeutic response is defined as having stable tumor volume for more than 7 days four measurements. Statistical analyses were performed using TumGrowth software. We then tested if propranolol could increase the response rate of an already very effective treatment regimen of combined anti-PD1 and anti-CTLA4 antibodies. S4E, F. Out of the six tumor free mice from both ICI treated groups, three mice relapsed after treatment discontinuation.
As described above, complete tumor clearance was observed in a limited number of mice after combination treatment of ICI and PRO. To investigate if these mice developed immune memory against the cancer cells, we re-inoculated the mice with MCA cancer cells three months after tumor clearance. CD44 was used to identify murine memory T cells. The response levels between all tumor free mice were similar.
The data supports that propranolol exerts anti-tumor effects that involve the formation of an anti-tumoral immune environment, and it suggests that combinations of propranolol and ICI could lead to durable therapeutic responses. Clinical trials of checkpoint inhibitors have achieved suboptimal results in STS patients [ 4 ]; therefore, there is a need to improve immunological responses towards the tumor with immune modulatory agents.
Retrospective analyses have shown a correlation between post-diagnostic use of nonselective beta blockers and increased relapse-free survival in a variety of cancer types [ 34 , 35 ]. In this study, we demonstrated the potential of propranolol as a potent immune modulatory agent that can enhance the therapeutic efficacy of anti-CTLA4 checkpoint inhibitor therapy in STS using a murine fibrosarcoma model.
We also show a similar effect of propranolol in a murine model of colon cancer. Preclinical studies on the therapeutic effect of propranolol have identified direct anti-proliferative effect on angiosarcoma cells using in vitro assays and immune deficient mice.
In our study, we did not observe a difference in Ki67 expression in tumor sections, indicating that a direct anti-proliferative effect of propranolol is not a major contributing factor to its anti-tumor effects in vivo. Activation of the ADRBs on immune cells has been shown to dampen the inflammatory response towards infectious disease [ 36 , 37 ]. The propranolol induced increase in T cell infiltration is essential for the improved tumor control, since T cell deficient nude mice did not benefit from propranolol in terms of tumor growth and survival rate.
The myeloid cell of the TME can inhibit T cell functions, shape the angiogenic network in cancer, and regulate the recruitment of lymphocytes to the tumor [ 30 , 38 ].
In this study, we observed a striking reduction of MDSCs in the tumor. Here, we investigated the direct impact of propranolol on macrophage function in vitro using BMDMs. Interestingly, we observed that propranolol alone was able to neutralize the M2 polarizing effect, which could be related to its ability to inhibit lipin-1, an enzyme suggested to be essential for IL4-mediated macrophage polarization in vitro. These findings suggest that the propranolol induced changes in the lymphoid compartment could be partly caused by an altered TAM-mediated recruitment of lymphocytes.
Propranolol treatment induces a shift of the TME towards a pro-inflammatory state, which is potentially synergistic with ICI therapies. A high number of tumor infiltrating T cells has been associated with increased overall survival and response to immunotherapy in a variety of cancer types, including colorectal cancers, ovarian cancers, and melanoma [ 42 , 43 ]. The combination resulted in a reduced tumor growth rate and increased overall survival.
In one case, the combination treatment resulted in complete MCA tumor clearance. Notably, the tumor free mouse is resistant to subsequent MCA tumor re-challenge three months after tumor clearance, highlighting that a durable anti-tumor response was obtained.
Analysis of the presence of tumor reactive splenocytes showed that the complete response was accompanied by acquired long lasting T cell memory.
A retrospective analysis of melanoma patients treated with immune therapies showed that the concurrent usage of propranolol correlates with prolonged overall survival [ 46 ].
Recently, a phase I clinical trial investigating the combination of propranolol and pembrolizumab in a limited number of patients indicated that the treatment combination is tolerable and could lead to increased response rates [ 47 ].
Additionally, a preclinical study has shown that ADRB blockade by propranolol or genetic ablation of Adrb2 improved anti-PD1 treatment outcome in a murine breast cancer model [ 48 ]. In our study, propranolol treatment did not improve the response of MCA model to anti-PD1 or anti-PDL1 treatment, which could be due to the difference in cancer types. Further effort needs to be directed to identify the cancer types that can benefit from the combination treatment, and elucidate which ADRB receptor is more critical to the anti-tumor effect of propranolol.
In another study, anti-CTLA4 enhanced the treatment outcome of a therapeutic cancer vaccine through the improvement of T cell priming in the lymphoid tissues [ 50 ]. Therefore, it is possible that propranolol and anti-CTLA4 synergistically stimulate the initial priming phase of an anti-cancer immune response, making this combination treatment particularly effective.
The data presented in this study provide new insights into the therapeutic effect of propranolol on STS and shows that propranolol modulates immune responses against STS by increasing T cell recruitment and by reducing the number of tumor infiltrating MDSCs. The study supports the idea that adrenergic signaling contributes to therapy resistance and strengthens the rationale of combining propranolol, a low cost, and relatively non-toxic adjuvant therapy, with ICI therapies for treating cancer patients.
Animal experiments were conducted at the animal facility of the Department of Oncology, Herlev Hospital, Denmark, under license issued by the Animal Experiments Inspectorate. Experimental mice were females between 8 and 16 weeks of age.
The right flank of mice was subcutaneously s. The mice were randomized into control or treatment groups after cancer cell inoculation. Tumor dimensions length and width were measured three times a week with a digital caliper by a blinded researcher.
Tumor volumes were calculated by the formula: 0. Mice were sacrificed by cervical dislocation and the tumors were collected for further analysis unless otherwise noted. Tumors with ulceration were excluded from the analysis. Cells lines were not tested for mycoplasm contaminations.
Water bottles for all mice were changed every 3—4 days. In checkpoint inhibitor treated groups, mice were treated intraperitoneally i. Control mice were treated with PBS following the same treatment scheme. The growth medium was refreshed on day 3. Excised tumors were minced with scissors and enzymatically digested in RPMI medium containing 2.
Dead cells were excluded using Zombie Aqua viability dye BioLegend. All antibodies used are listed in Supplementary Table 1.
Data were analyzed with Flow Jo software Tree Star. Primer sequences are listed in supplementary table 2. The library quality was assessed using a Fragment Analyzer followed by library quantification using the Illumina library quantification kit.
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