Additional file 1 of Comparing syngeneic and autochthonous models of breast cancer to identify tumor immune components that correlate with response to immunotherapy in breast cancer
Creators: name: Lal, Jessica Castrillon
name: Townsend, Madeline G.
name: Mehta, Anita K.
name: Oliwa, Madisson
name: Miller, Eric
name: Sotayo, Alaba
name: Cheney, Emily
name: Mittendorf, Elizabeth A.
name: Letai, Anthony
name: Guerriero, Jennifer L.
Publisher: name: figshare
Year: 2021
Subjects: subject: Immunology
subject: FOS: Clinical medicine

description: Additional file 1: Supplemental Figure 1. Immunophenotyping of cells used for generation of syngeneic murine models. Tumors from the MMTV-PyMT autochthonous model were used to generate inoculum to inject into wild type mice to generate 1E6, 1E5 and 1E4 syngeneic tumor models. Flow cytometry was performed on three separate batches of inoculum, which were used for each unique experiment and are shown as red (inoculum1), blue (inoculum2), and green (inoculum3). a. Gating strategy for flow cytometry b. Immune cell composition as a frequency of immune cell populations out of CD45+ cells. c. Shown are graphs corresponding to Fig. 1e (CD45), Fig. 1g (CD11b) and Fig. 1h (CD3) color coded by inoculum (experimental run). Graphs show mean ±SEM. *P&lt;0.05, **P&lt;0.01, *** P&lt;0.001, **** P&lt;0.001. Supplemental Figure 2. Example of FACs gating strategy. Gating strategy for flow cytometry. Supplemental Figure 3. Ratio of anti-tumor to pro-tumor macrophages. Tumors from the autochthonous MMTV-PyMT model were harvested and single-cell suspensions were generated. Cells (1E6, 1E5 or 1E4) were injected into mammary fat pad number 4 of FVB/NJ wild type mice. When the tumors reached 100 mm3, tumors were obtained and processed into single-cell suspension for immunophenotyping by flow cytometry to identify the ratio of classically activated macrophages CD40+ (a), CD80+ (b), and CD86+ (c) of F4/80+ to alternatively activated macrophages indicated by the mannose receptor, CD206 + of F4/80 + cells. Each data point represents an individual mouse. Graphs show mean ±SEM. *P&lt;0.05, **P&lt;0.01, *** P&lt;0.001, **** P&lt;0.001. Supplemental Figure 4. EMT6 1E6 and 1E4 models reproduce observations from the MMTV-PyMT syngeneic model. Balb/c mice were injected with 1E6 or 1E4 tumor cells in the mammary fat pad. When tumor reached 100-150 mm3, mice were sacrificed, and tumors were obtained for immunophenotyping analysis by flow cytometry. (a) The 1E6 tumors reached 100-150 mm3 faster than the 1E4 tumors. (b) Batch controls were used to control for variability between running flow experiments on different days, when tumors reached 100-150 mm3. (c-e) Flow cytometry was performed. Supplemental Figure 5. QC of mRNA expression analysis. Tumors from the autochthonous MMTV-PyMT model were harvested and single-cell suspensions were generated. Cells (1E6, 1E5 or 1E4) were injected into mammary fat pad number 4 of FVB/NJ wild type mice. When the tumors reached 100 mm3, RNA was extracted from tumors and NanoString analysis was performed. Data was analyzed using the Advanced Analysis 2.0 Module of the nSolverTM software (NanoString Technologies). The geNorm algorithm was used for selecting housekeeping genes and samples were normalized against positive controls. a. Normalization results shows consistency among selected housekeeping genes according to geNorm. b. Mean gene expression plotted versus variance. Housekeeping genes used in normalization are highlighted. c. Principle component analysis scored across the four models. The first two principal components explain 36% and 14% of variance respectively. d. Unsupervised hierarchical clustering was performed on all normalized transcripts above background. Orange cells indicate higher than average expression; blue cells indicate lower than average expression. e. A summary of cell types scores are shown across the four groups. e Quantification of cell types scores for neutrophils, dendritic cells, natural killer cells, and natural killer CD56dim are shown. Each data point represents an individual mouse. Graphs show mean ±SEM. Unpaired two tailed t-test. *P&lt;0.05, **P&lt;0.01, *** P&lt;0.001, **** P&lt;0.001. Supplemental Figure 6. Quantification of pathway analysis by NanoString. Signature scores from the NanoString pathways analysis are shown. Graphs show mean ±SEM with 4-5 mice per group. Unpaired two tailed t-test. *P&lt;0.05, **P&lt;0.01, *** P&lt;0.001, **** P&lt;0.001. Supplemental Figure 7. Pathway analysis by NanoString. a. The heatmap summarizes the mRNA transcripts categorized by their respective pathway. Shown are the signature scoring differences of the syngeneic models compared to the transgenic model. Orange represents higher than average scores; blue represents lower than average scores. b-d. Volcano plots representing differential expression analysis of genes expressed in the 1E6 (top), 1E5 (middle), 1E4 model (bottom) compared to the transgenic model. Volcano plots show fold change (log2) versus log10 p-values. False discovery rate thresholds are shown (FDR&lt;0.01, FDR&lt;0.05, FDR&lt;0.1, and FDR&lt;0.5). e. Normalized log2 counts of transcripts related to the Antigen presentation pathway from the Nanostring pathways analysis are shown. f. Normalized log2 counts of transcripts related to the immune signaling pathway are shown. Graphs show mean ±SEM with 4-5 mice per group. Unpaired two tailed t-test. *P&lt;0.05, **P&lt;0.01, *** P&lt;0.001, **** P&lt;0.001. Supplemental Figure 8. Syngeneic tumor treated with CTLA-4 and PD-L1 show significant differences in tumor responses. Mice of similar tumor burden from each syngeneic model and the autochthonous model were randomized into four treatment groups: IgG2B, PD-L1 inhibitor, CTLA-4 inhibitor, and the combination of PD-L1 and CTLA-4 inhibitors. Tumors were measured every 2-3 days. Tumor volumes were measured and plotted as mean total tumor burden ± SEM and tumor burden fold change were calculated at day 14 for the 1E6 (a), 1E5 (b), 1E4 (c) and autochthonous (d) models. MMTV-PyMT autochthonous mice were enrolled into as study at about 80 days old and when each tumor reached 80-100 mm3. Mice have 10 mammary fat pads and tumor arise in all 10 fat pads. Tumors from mammary fat pad numbers 5 and 10 were excluded from our analysis. The sum of the volumes for tumors from the MMTV-PyMT autochthonous fat pads (1-4 and 5-9) were used and indicated as “total tumor burden”. The syngeneic mice that had tumors that measured 80-100 mm3 were enrolled into an experiment. Shown are a summary of two individual experiments n=3-7/treatment groups. Two-way ANOVA multiple comparisons was performed *P&lt;0.05, **P&lt;0.01, *** P&lt;0.001, **** P&lt;0.001. Supplemental Figure 9. Comparison of predicted responders vs. non-responders by NanoString analysis. The NanoString data was reanalyzed grouping mice into predicted responders (1E6 and 1E5) and predicted non-responders (1E4 and autochthonous). a. Principle component analysis. b. Heatmap of immune cell scores. c. Cell types scores are shown across the two groups. Each data point represents an individual mouse. d. Heatmap of pathway analysis showing predicted responders (gray) and non-responders (orange). e. Differential expression of responders vs non-responders with the Inflammation Score Probe Set shown in red. Graphs show mean ±SEM. Unpaired two tailed t-test. *P&lt;0.05, **P&lt;0.01, *** P&lt;0.001, **** P&lt;0.001. Supplemental Figure 10. Summary slide of cellular immune components that correlate with response to immunotherapy. a. Key characteristics of immune cell subsets are summarized in the table and grouped by T-cells, myeloid cells, where “+” indicates significant changes, and blue and red represent significant increase and decrease expression, respectively, compared to the MMTV-PyMT model. Response to immune checkpoint blockade is also shown where blue and red represent significant increase and decrease response, respectively, compared to the vehicle control. b. Schematic representing the 4 tumor models and their immune composition. Figure created using biorender.com.