Benchmarking response to checkpoint blockade in CT-26, MC38 and 4 T1 syngeneic tumor models

In order to better understand how some of the most commonly utilized syngeneic models respond to checkpoint inhibition, we chose CT-26, MC38, and 4 T1 models for characterization after treatment with a clinically relevant [15] combination of α-mPD-L1 + α-mCTLA-4, which has been shown to induce anti-tumor immune responses in syngeneic models [16]. After tumor implantation, mice were dosed twice a week with a combination of α-PDL-1 + α-CTLA-4 or isotype controls for 2 weeks and tumor growth and survival were measured. In the context of these experiments, the CT-26 model showed the most robust response to checkpoint inhibition (Fig. 1a and b) with 10/12 animals showing reduced tumor growth or stasis leading to an enhanced survival (Additional file 9: Figure S1a). In our hands, the MC38 tumor model showed a more varied response to the same checkpoint inhibition therapy, with delayed tumor growth, but only 1/12 mice showing complete response to therapy (Fig. 1c and d). However, despite only a modest reduction in tumor growth, checkpoint inhibition enhanced survival in this model (Additional file 9: Figure S1b). In contrast to the efficacy observed in CT-26 and MC38 after checkpoint inhibition, the 4 T1 tumor model showed no difference in tumor growth (Fig. 1e and f) and no enhanced survival benefit (Additional file 9: Figure S1c) in response to checkpoint inhibition. All three models expressed PD-L1 in both the myeloid and tumor (CD45-) compartments (Additional file 10: Figure S2). Given this variation in response across these three models observed in our lab and others [17–19], we sought to further characterize the kinetics of immune cell infiltration into the tumor microenvironment over the time course of tumorigenesis in these models as a means to better understand the possible reasons underlying differences in response.

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Fig. 1

Impact of α-mPD-L1+ α -mCTLA-4 treatment on tumor growth in syngeneic models. Line graphs show tumor volumes from (a) Balb/c CT-26 tumor bearing mice treated with isotype control or (b) anti-mPD-L1 + anti-mCTLA-4 combination treatment; (c) C57Bl/6 MC38 tumor bearing mice treated with Isotype Control or (d) anti-mPD-L1 + anti-mCTLA-4 combination treatment; (e) Balb/c 4T1 tumor bearing mice treated with isotype control or (f) anti-mPD-L1 + anti-mCTLA-4 combination treatment. Vertical dotted lines indicate the period of dosing. n=12 per group. Number of responders (those surviving longer than the last vehicle treated) in each model is indicated on each plot

Longitudinal Immunophenotyping of CT-26 tumors

CT-26 cells are a colon carcinoma cell line developed by exposing BALB/c mice to N-nitroso-N-methylurethane (NMU), resulting in a grade IV carcinoma that is fast growing and easily implantable [20], making it a workhorse model to study pre-clinical immune mechanisms [21]. This model has previously been characterized as enriched for cytotoxic T-cell and NK cells, using samples taken late in tumor development [17, 19]. In order to explore immune remodeling of the TME during the entire course of tumorigenesis in the CT-26 model, we collected tumors at day 3 when tumors were ~ 25 mm³, day 7 when tumors were ~ 100 mm³ and day 14 when tumors were ~ 500 mm³ (Fig. 2a and b) and performed flow cytometry and gene expression analysis. Examination of total immune infiltrate, measured by infiltration of CD45+ cells, indicated that early, day 3 tumors showed relatively little immune infiltrate (20% CD45+ cells) compared to other tumor/stromal cells (80% CD45-). Interestingly, at day 7 the amount of immune cells (60% CD45+) exceeded the amount of tumor/stromal cells (40% CD45-), which was reversed back to baseline levels by day 14 as the tumors became larger (Fig. 2). Examination of individual immune populations as a percentage of CD45+ cells indicated that the heightened immune infiltrate observed on day 7 was associated with an increased proportion of NK and CD3+ T-cells and a decreased proportion of CD11b + myeloid cells, while B-cells remained low and at a constant level throughout the course of tumorigenesis (Fig. 2d left). Examination of individual immune populations as a percentage of live cells showed similar kinetics, with the exception of myeloid cells which made up a larger proportion of live cells at day 7 (Fig. 2d right). Specifically, we observed that NK cells, CD8+ T-cells, and Tregs reached peak levels at day 7 and had decreased by day 14 (Fig. 2e and Additional file 4: Table S4). In addition to the overall change in T-cell populations, the number of CD8+ T-cells expressing Granzyme B (GzmB+) and PD-1 increased over the course of tumor growth, while the number of Tregs, associated with immune suppression significantly decreased (Fig. 2 g). This is consistent with the strong cytolytic T-cell immune response observed in this tumor model previously [17] and indicates a mechanistic reason why therapies, like checkpoint inhibition, that boost CD8+ T-cell responses work so well in the CT-26 model and suggest that therapies that target Tregs would need to be administered early in this model.

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Fig. 2

Changes in immune infiltrate over the course of CT-26 tumor development. (a) Schematic of sample collection. (b) Tumor volumes on indicated day post implant. (c) Proportion of CD45- to CD45+ cells measured at each timepoint by flow cytometry (d) Proportion of CD3+, CD11b, NK, and B-cells as a percent of CD45+ cells (left) or as a percent of live cells (right) measured by flow cytometry. (e) Sunburst blots showing T-cell and NK cell populations as a proportion of CD45+ cells. (f) Sunburst plots showing the proportion of myeloid cell populations as a proportion of CD45+ cells. (g) Flow cytometry data for individual T-cell populations. (h) Flow cytometry data for individual macrophage cell populations. (i) Gene expression data generated from a panel of 96 genes was used to calculate a GSVA score [4, 5] indicating enrichment for specific immune cell types at each timepoint. Flow cytometry data is 1 sample from 7 pooled tumors for day 3, 4 tumors from individual animals and 1 sample from 2 pooled tumors on day 7 and 6 individual tumors from day 14. Sunburst plots show data from a pool of n=6 samples. For GSVA scores day 3 n=4 tumors, day 7 n= 6 tumors, and Day 14 n=5 tumors. Statistical significance is indicated as NS=not significant, *p<0.05, **p<0.01,***p<0.001, ****p<0.0001. Data for sunburst plots available in Additional file 4: Table S4

Detailed analysis of the CD11b + myeloid populations revealed that pro-inflammatory M1-like and MHCII-CD206- cells, associated with anti-tumor immune activity remained relatively constant as a proportion of F480+ cells during the course of tumorigenesis. However, there was an increase in total myeloid cells by day 14, which was coupled with a trend towards a higher proportion of M2-like and MHCII+CD206+ macrophages, which are associated with tumor immune suppression and known to promote tumor growth and metastasis (Fig. 2f, h and Additional file 4: Table S4). This is consistent with early anti-tumor immune responses being suppressed as tumors progress and suggests intervention with myeloid-targeting agents might be optimal at or before day 7 in this model.

Using targeted gene expression data, we calculated enrichment scores for immune cell/phenotype signatures (Fig. 2i) [7]. Consistent with the flow cytometry data, this showed enhanced Tregs at day 7 and enhanced macrophages at day 14, with B-cells and neutrophils decreasing through the duration of tumorigenesis (Fig. 2i). In contrast to the flow cytometry data, we observed the peak score for CD8+ T-cells and NK cells at day 14 rather than day 7, which could be due to difference in samples analyzed or differences between gene and protein expression levels. In general, the gene enrichment signatures correlated well with cell population data generated by flow cytometry. Additionally, we observed an increase in gene signatures related to cytolytic activity consistent with the increase in activated T-cells as well as co-inhibition APC and T-cell signatures which is consistent with increasing CD274 (PD-L1) expression and a shift towards immunosuppression that occurs as tumors become larger and more aggressive. Taken together, this data shows that dynamic changes in immune infiltrate occur over the course of CT-26 tumor development.

Longitudinal Immunophenotyping of MC38 tumors

MC38 cells are a colon adenocarcinoma cell line derived from C57Bl/6 mice [22]. Similar to our characterization of the CT-26 model, we examined changes in immune infiltrate in the TME throughout the course of tumor development by collecting tumors at day 3 when the tumors were ~ 100 mm³, day 7 when the tumors were ~ 400 mm³ and day 10 when the tumors were ~ 850 mm³ (Fig. 3a and b) and performed detailed flow cytometry and gene expression analysis. Unlike the CT-26 model, where we saw dynamic changes in immune infiltrate throughout the course of tumor development, CD45+ cells remained relatively constant between day 3 and day 7 until shifting to roughly equal proportions of CD45- to CD45+ cells at day 10 when tumors were largest (Fig. 3c). Similarly, the levels of CD11b + myeloid cells and CD3+ T-cells remained fairly constant over the course of tumor growth, with only a slight decrease in myeloid cells and a slight increase in T-cells as a proportion of CD45+ cells at day 10. The number of NK and B-cells as a proportion of CD45+ cells remained low over the entire course of the experiment (Fig. 3d left). Examination of individual populations as a percentage of live cells showed similar trends, with CD11b + cells decreasing and T-cells increasing at Day 10 (Fig. 3d right). A more detailed examination of T-cell populations revealed that unlike CT-26 where we observed expansion of Tregs, NK cells, and CD8+ T-cells at day 7, T-cell and NK populations in MC38 remained stable over the course of tumor development (Fig. 3e, Additional file 5: Table S5). Although there was an overall expansion of myeloid cell types over the course of tumor development, similar to observations in CT-26, this was not associated with a shift from M1-like to M2-like macrophage enrichment (Fig. 3f, Additional file 5: Table S5). Similar to the CT-26 model, MC38 tumors showed a significant increase in GzmB+ and PD-1+ CD8+ T-cells indicative of a cytolytic response to the tumor. However, unlike CT-26, immunosuppressive Tregs did not decrease (Fig. 3 g). The overall increase in F480+ macrophages that occurred during tumor growth was associated with a decrease in the M1-like pro-inflammatory macrophages associated with anti-tumor activity in this model (Fig. 3h). In line with the flow cytometry data, analysis of gene expression signatures indicated an increase in CD8+ T-cells, NK cells and cytolytic activity as well as a modest increase in macrophages over the time course of tumor development (Fig. 3i). Consequently, this data indicates that the MC38 model is characterized by an expansion of T-cell populations and macrophage populations during tumor development. However, the overall changes in immune infiltrate are not as dynamic as those observed in CT-26 (Fig. 2). This suggests that combining checkpoint inhibition with myeloid or Treg targeting agents might enhance efficacy in this model and that perhaps treatment timepoints would be less critical in this model due to the lack of dynamic changes over time.

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Fig. 3

Changes in immune infiltrate over the course of MC38 tumor development. (a) Schematic of sample collection. (b) Tumor volumes on indicated day post implant. (c) Proportion of CD45- to CD45+ cells measured at each timepoint by flow cytometry (d) Proportion of CD3+, CD11b, NK, and B-cells as a percent of CD45+ cells (left) or as a percent of live cells (right) measured by flow cytometry. (e) Sunburst blots showing T-cell and NK cell populations as a proportion of CD45+ cells. (f) Sunburst plots showing the proportion of myeloid cell populations as a proportion of CD45+ cells. (g) Flow cytometry data for individual T-cell populations. (h) Flow cytometry data for individual macrophage populations (i) Gene expression data generated from a panel of 96 genes was used to calculate a GSVA score [4, 5] indicating enrichment for specific immune cell types at each timepoint. Flow cytometry data is 1 sample from 10 pooled tumors for day 3, 10 individual tumors for day 7 and 10 individual tumors for day 14. Sunburst plots show data from a pool of all samples or a representative sample. For GSVA scores n=10 for all groups. Statistical significance is indicated as *p<0.05, **p<0.01,***p<0.001, ****p<0.0001. Data for sunburst plots available in Additional file 5: Table S5

Longitudinal Immunophenotyping of 4 T1 tumors

4 T1 cells are a highly-metastatic triple negative breast cancer cell line derived from a BALB/c spontaneous mammary carcinoma [23]. Previous studies have shown this model to be highly myeloid enriched and refractory to immune-checkpoint blockade [17, 24], however a detailed examination of populations over time has not been examined. In order to characterize immune populations over the course of tumor development in this model, we collected tumors implanted orthotopically in the mammary fat pad at three timepoints, day 7 when tumors were ~ 170 mm³, day 14 when tumors were ~ 550 mm³, and day 18 when tumors were ~ 1000 mm³ (Fig. 4a and b). In comparison to CT-26 and MC38 models, 4 T1 tumors showed relatively little immune infiltrate, with CD45- cells remaining higher than CD45+ cells throughout the course of tumor growth. Similar to MC38, there were no dynamic changes in the amount of CD45+ cells and they remained relatively constant throughout the course of tumor growth (Fig. 4c). Consistent with 4 T1 tumors being a myeloid enriched model, CD11b + cells comprised the largest proportion of CD45+ immune cells and increased over the course of tumor development, while CD3+ cells decreased, and NK and B-cells remained low over the duration (Fig. 4d left). Examination of immune populations as a proportion of live cells showed a similar pattern to analysis as a proportion of CD45+ cells in this model (Fig. 4d right). A more detailed investigation of individual immune populations indicates that similar to MC38 and unlike CT-26, T-cell populations remain relatively constant over the time course of 4 T1 tumor development, with Tregs slightly decreasing and CD8+ T-cells slightly increasing as tumors progress (Fig. 4e and Additional file 6: Table S6). Detailed investigation of myeloid cell populations indicated that macrophage populations increase over tumor development, consistent with 4 T1 tumors being myeloid enriched (Fig. 4f and Additional file 6: Table S6). Additionally, although there was a small increase in Gzmb+ CD8+ T-cells, there was no increase in PD-1+ CD8+ T-cells and no decrease in Treg cells, indicating much less of a T-cell-mediated immune response to the tumor than that observed in the other two models (Fig. 4 g). Given the high level of F480+ cells in this model it is not surprising that there was no additional increase as tumors progressed (Fig. 4 h). However, interestingly, as tumors progressed there was a significant decrease in the M1-like macrophage population (Fig. 4h), shifting the balance towards the M2-like macrophage population, suggesting that this model may be primed to respond to myeloid targeted therapies. Consistent with the flow cytometry data, gene expression analysis indicated that macrophage populations expanded as 4 T1 tumors progressed. Similar to CT-26 and MC38 CD8+ T-cells, NK cells and cytolytic activity also increased (Fig. 4i). Taken together this data supports evidence that 4 T1 tumors represent a myeloid enriched tumor model which may explain why, despite the expansion in CD8+ T-cells, this model does not respond to checkpoint inhibition.

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Fig. 4

Changes in immune infiltrate over the course of 4T1 tumor development. (a) Schematic of sample collection. (b) Tumor volumes on indicated day post implant. (c) Proportion of CD45- to CD45+ cells measured at each timepoint by flow cytometry (d) Proportion of CD3+, CD11b, NK, and B-cells as a percent of CD45+ cells (left) or as a percent of live cells (right) measured by flow cytometry. (e) Sunburst plots showing T-cell and NK cell populations as a proportion of CD45+ cells. (f) Sunburst plots showing the proportion of myeloid cell populations as a proportion of CD45+ cells. (g) Flow cytometry data for individual CD8+ T-cell populations (h) Flow cytometry data for individual macrophage populations. (i) Gene expression data generated from a panel of 96 genes was used to calculate a GSVA score [4, 5] indicating enrichment for specific immune cell types at each timepoint. Flow cytometry data is n=10 for all timepoints. Sunburst plots show data from a pool of samples or a representative sample. For GSVA scores n=10 for day 7 and day 14 and n=9 for day 18. Statistical significance is indicated as *p<0.05, **p<0.01,***p<0.001, ****p<0.0001. Data for sunburst plots available in Additional file 6: Table S6

Checkpoint inhibition results in changes in immune infiltrate in CT-26

As CT-26 and MC38 models showed response to checkpoint inhibition, while 4 T1 did not (Fig. 1), we chose to perform a more detailed immune phenotype characterization after checkpoint therapy in the two responsive models. CT-26 tumors were implanted in Balb/c mice and dosed twice-weekly with α-mPD-L1+ α-mCTLA-4 before collecting tumors on day 14 after treatment (Fig. 5a). Flow cytometry analysis indicated that both CD3+ T-cell and NK cell populations expanded in response to therapy (Fig. 5b), with the total CD3+ T-cell population doubling (Additional file 7: Table S7). In particular, CD8+ T-cells responsible for driving an anti-tumor immune response were significantly increased from 5.37 to 9.64% (Fig. 5b and Additional file 7: Table S7). Coupled with this expansion of cytotoxic T-cells there was a dramatic reduction in all F480+ macrophage populations (Fig. 5c and Additional file 7: Table S7). Consistent with the expansion of CD8+ T-cells and response to therapy, we observed an increase in CD8 + GzmB expression levels as well as a compensatory upregulation of Tregs (Fig. 5d). Additionally, the decrease in F480+ macrophages was associated with a decrease in M2-like macrophages, tipping the balance towards an M1-like pro-inflammatory anti-tumor macrophage response (Fig. 5e). Gene expression analysis phenocopied flow cytometry analysis and indicated that T-cell and NK cell populations expanded after treatment with α-mPD-L1 + α-mCTLA-4, while total macrophage populations were reduced. Furthermore, co-inhibition and cytolytic activity signatures were enriched after treatment (Fig. 5f) indicating an activated T-cell response [25].

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Fig. 5

Checkpoint inhibition results in dynamic changes in immune infiltrate in CT-26. (a) Schematic of treatment and sample collection in CT-26 model. (b) Representative sunburst plot showing T-cell population changes after α-mPD-L1+ α-mCTLA-4 treatment in CT-26 tumors. (c) Representative sunburst plot showing changes in myeloid populations after α-mPD-L1+ α-CTLA-4 treatment in CT-26 tumors. (d) Flow cytometry data for individual T-cell populations from isotype control treated (n=20) or α-mPD-L1+ α-CTLA-4 treated (n=17) tumors. (e) Flow cytometry data for individual macrophage populations from isotype control treated (n=20) or α-mPD-L1+ α-CTLA-4 treated (n=17) tumors. (f) Gene expression data generated from a panel of 96 genes was used to calculate a GSVA score [4, 5] indicating enrichment for specific immune cell types after treatment with isotype control (n=10) or α-mPD-L1+ α-CTLA-4 (n=9) in CT-26 tumors. (g) Schematic of Treatment and sample collection in MC38 model. (h) Representative sunburst plots showing T-cell population changes after α-mPD-L1+ α-mCTLA-4 treatment in MC38 tumors. (i) Representative sunburst plots showing changes in myeloid populations after α-mPD-L1+ α-mCTLA-4 treatment in MC38 tumors. (j) Flow cytometry data for individual T-cell populations from isotype control treated (n=20) or α-mPD-L1+ α-CTLA-4 treated (n=18) tumors. (k) Flow cytometry data for individual macrophage populations from isotype control treated (n=20) or α-mPD-L1+ α-CTLA-4 treated (n=18) tumors. (l) Gene expression data generated from a panel of 96 genes was used to calculate a GSVA score [4, 5] indicating enrichment for specific immune cell types after isotype control (n=6) or α-mPD-L1+ α-CTLA-4 (n=9) treatment in MC38 tumors. Data for sunburst plots available in Additional files 7, 8: Tables S7 and S8

Similar to CT-26, MC38 tumors were implanted in C57Bl/6 mice and dosed twice weekly with α-mPD-L1 + α-mCTLA-4 before collecting tumors on day 10 (Fig. 5 g). In comparison to CT-26, MC38 tumors showed a much more modest increase in CD3+ T-cells (Fig. 5 h Additional file 8: Table S8), but still showed a significant increase in CD8+ T-cells which expanded from 1.31 to 3.28% (Additional file 8: Table S8) in response to treatment. Unlike the CT-26 model, MC38 tumors did not show a dramatic reduction in F480+ macrophages (Fig. 5i and Additional file 8: Table S8). Similar to CT-26, checkpoint inhibition in MC38 led to increased GzmB expression in the expanded CD8+ T-cell population (Fig. 5j). However, unlike CT-26, this model did not exhibit a compensatory upregulation in Tregs (Fig. 5j) and did not show any change in overall macrophage populations or shifts in M1-like versus M2-like macrophage levels (Fig. 5k), suggesting a less robust T-cell response and a more suppressive myeloid microenvironment which may explain the less pronounced response to checkpoint inhibition in this model. Mirroring the flow cytometry data, gene expression analysis indicated that T-cell populations increased, and myeloid populations remained stable. Increases in co-inhibition and cytolytic activity signatures were much less pronounced than those observed in CT-26 (Fig. 5 l). Taken together, this data suggests that expansion of tumor-resident T-cells, in particular CD8+ T-cells, coupled with a decrease in myeloid cells is required for response to checkpoint therapy.

Time course of response to checkpoint inhibition in CT-26

Given the dynamic changes in immune infiltrate observed over the time course of CT-26 tumorigenesis and the strong response to checkpoint inhibition, we sought to more thoroughly characterize the time course of response to checkpoint inhibition in this model. To do so, we treated mice bearing CT-26 tumors twice a week with the combination of α-mPD-L1 + α-mCTLA-4 and collected tumors at day 7 or day 14 post-treatment (Fig. 6a) and performed RNAseq and proximity extension assay (PEA) proteomic analysis (Additional file 11: Figure S3) on the samples. RNAseq analysis indicated that 1672 genes were significantly (p adjusted p < 0.05) changed by α-mPD-L1 + α-mCTLA-4 treatment at day 7 and 1508 genes were changed by therapy at day 14 (Fig. 6b). Of these changes, expression of 242 genes were altered by checkpoint inhibition at both timepoints (Fig. 6b & c). Interestingly, samples clustered by day and treatment, not by tumor size (Additional file 12: Figure S4), suggesting that response may rely on the time dependent immune changes we have observed. At day 7, the transcript profile indicated an enrichment for migration of leukocytes in response to inflammation and communication between innate and adaptive immune cells, while at day 14 transcript profiles were enriched for T-helper cell signaling pathways (Fig. 6d). Consistent with this, proteomic analysis indicated that α-mPD-L1 + α-mCTLA-4 treatment resulted in upregulation of chemokines and cytokines associated with inflammation in recruitment of leukocytes at day 7 (IL-6, CXCL1, CCL3, CCL2, Il1β, and CSF2), which returned to control levels by day 14 (Fig. 6e). This supports the idea that α-mPD-L1 + α-mCTLA-4 treatment enhances an early (day 7) inflammatory response, that drives later (day 14) T-cell infiltration and anti-tumor immune response. Similarly, upstream pathway analysis of the transcriptome data indicated that lipopolysaccharide (LPS), IL-1B, TNF, IFNG, and NFKB1A pathways associated with inflammation were activated at day 7 (Fig. 6f). However, by day 14, although LPS and IFNG pathways remained activated, this was coupled with STAT1 and IL21 pathway enrichment indicative of innate and adaptive immune response to inflammation [26]. Examination of lymphocyte subtype fractions using transcript expression indicated that coupled with the enhanced inflammation induced by α-mPD-L1 + α-mCTLA-4 treatment, there was an increase in CD8+ T-cells, NK-cells, and M1-like (pro-inflammatory) macrophages (Fig. 6 g), suggesting that these cell types are responsible for driving the responses observed in the CT-26 model. In this particular study there were not enough animals to dissect differences in responder and non-responder mice, so we performed a larger CT-26 study and examined differences in immune cell content between responders and non-responders. Interestingly, there was no difference in T-cell content between responders and non-responders (Additional file 13: Figure S5a, S5b, S5c). However, frequency of CD11b + and F480+ myeloid cells was significantly higher in non-responders (Additional file 13: Figure S5d and S5e). This supports our observations, and others [27], that expansion of myeloid cells during tumor progression (Fig. 2f , h) may suppress response to checkpoint blockade and that eliminating the suppressive myeloid populations (e.g. macrophages) is important for eliciting anti-tumor response to checkpoint blockade. Taken together, this suggests that while α-mPD-L1 + α-mCTLA-4 treatment drives an expected T-cell activation, it is the myeloid content of the tumor that correlates and possibly determines anti-tumor response. Further studies would be needed to determine the mechanistic role of myeloid populations in response to immunotherapy.

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Fig. 6

Checkpoint inhibition results in enhanced inflammation and T-cell response in CT-26 syngeneic tumors. (a) Schematic of dosing and sample collection. (b) Venn diagram indicating the number of genes regulated by checkpoint inhibition at each timepoint. (c) Heat map analysis of 242 differentially expressed genes between control and α-mPD-L1 + α-mCTLA-4 treatment at both day 7 and day 14. (d) IPA pathway analysis of differentially expressed genes. Z-score indicates a pathway with genes exhibiting overall increase mRNA levels (orange bars) or decreased mRNA levels (blue bars). The ratio (orange line) indicates the ratio of genes from the dataset that map to the same pathway. (e) Normalized protein expression (NPX) levels of chemokines measured by O-link PEA assay. (f) Upstream regulator pathways from IPA pathway analysis. (g) Quantification of immune cellular subtypes based on RNAseq gene signatures between Isotype Control and α-mPD-L1+ α-mCTLA-4 treated samples taken at day 7 and day 14 time points. Statistical significance is indicated as *p<0.05, **p<0.01,***p<0.001, ****p<0.0001, n=5 for RNAseq, n= 3-5 for PEA