CD91 on dendritic cells governs immunosurveillance of nascent, emerging tumors

Abstract

The immune system detects aberrant, premalignant cells and eliminates them before the development of cancer. Immune cells, including T cells, have been shown to be critical components in eradicating these aberrant cells, and when absent in the host, incidence of cancer increases. Here, we show that CD91, a receptor expressed on antigen-presenting cells, is required for priming immune responses to nascent, emerging tumors. In the absence of CD91, effector immune responses are subdued, and tumor incidence and progression are amplified. We also show that, consequently, tumors that arise in the absence of CD91 express neo-epitopes with indices that are indicative of greater immunogenicity. Polymorphisms in human CD91 that are expected to affect ligand binding are shown to influence antitumor immune responses in cancer patients. This study presents a molecular mechanism for priming immune responses to nascent, emerging tumors that becomes a predictor of cancer susceptibility and progression.

Keywords: Adaptive immunity; Antigen presentation; Cancer; Immunology.

Figure 1. Lack of CD91 on DCs abrogates immunosurveillance of cancer.

(A) Schematic for generating mice with a deficiency of CD91 in DCs. (B) Mice deficient in CD91 expression in CD11c cells (CD91^fl/flCD11c^Cre, n = 13) were inoculated with a single dose of MCA s.c. WT littermates (CD91^fl/fl, n = 15) or Rag2^–/– (n = 15) mice were similarly inoculated. Mice were monitored 3 times a week for the appearance of palpable tumors. Tumor incidence is identified as tumors greater than 2 mm in diameter in any axis. P values were obtained by Gehan-Breslow-Wilcoxon text. (C) Tumors that grew in any mice (n = 11, 4, and 14 for CD91^fl/flCD11c^Cre, CD91^fl/fl, and Rag2^–/– mice, respectively) were measured. Growth curves for individual mice are shown in gray with the same group symbol. P values were obtained by 1-way ANOVA analysis of comparisons of area under the curve for each group. **P < 0.01, ***P < 0.001.

Figure 2. Lack of CD91 on DCs prevents priming of effector responses to emerging tumors.

Mice were injected with 200 μg MCA s.c. The injection site was harvested 2 weeks later (A–D and M–O) or the palpable tumor 9 and 18 weeks later (E–L and M–O) and analyzed by flow cytometry. Frequencies of CD3⁺ cells (A, E, and I), CD3^–NK1.1⁺ cells (B, F, and J), and CD11b⁺ and CD11c⁺ cells (C, G, and K) were measured. (D, H, and L) Remaining CD11b^–CD11c^– cells were further separated into populations that were either Ly6C⁺ or Gr1⁺. Each circle represents samples from a single mouse. The error bars depict standard deviation. The bar predicts the median. The length of the box represents the interquartile range. (M and N) The frequency of leukocytes (CD45⁺ cells) within the tumor was determined at each time point (M), as well as the frequency of lymphocytes (CD3⁺ and NK1.1⁺) among the CD45⁺ cells (N). (O) Flow cytometry (FCS) data were analyzed via unsupervised approaches using the MATLAB tool Cyt. The L1 statistical differences between the MFI distribution of the indicated markers on CD45⁺ cells were determined. The data are reported so that distributions derived from CD91^fl/flCD11c^Cre mice are the reference population, and the difference calculated is the change in the CD91^fl/fl mice distribution from the reference. Analyses were done on n = 6 mice/group, and statistical significance was determined by Student’s 2-tailed t test.

Figure 3. CD91-mediated immunity to nascent tumors is fixated on neo-epitopes with high differential aggretope index.

Tumors induced with MCA in CD91^fl/flCD11c^Cre and CD91^fl/fl mice were harvested and analyzed by whole-exome sequencing. (A) Total number of all SNVs derived from tumors in either group. (B) The total number of 8-, 9-, 10-, and 11-mer–mutated peptides spanning the mutation and predicted from each SNV. (C–E) The number of mutated peptides with the indicated half maximal inhibitory concentration (IC₅₀) threshold. (F) The average differential aggretope index (DAI), which measures the differential binding affinity between the WT and corresponding mutated peptides, derived from all mutated peptides for the H-2-K^b or H-2-D^b MHC allele. (G) The average DAI for only mutated peptides with high affinity for H-2-K^b or H-2-D^b MHC binding (IC₅₀ < 500). The box plots depict the minimum and maximum values (whiskers), the upper and lower quartiles, and the median. The length of the box represents the interquartile range. (H and I) The number of peptides with indicated maximum DAI for H-2^b (H) or H-2^d (I). Statistical significance was determined by Student’s 2-tailed t test. *P < 0.05.

Figure 4. Effect of predicted high-impact SNVs on receptor stability, HSP docking, and antitumor effector response.

(A) Distribution of 1233 candidate SNVs binned into 89 exons of the CD91/LRP1 gene. Individual ligand binding domains are indicated. Interdomain regions that play no binding or membrane anchoring role are colored uniformly black. The inset figure shows the number of mutations per base in each domain. Vertical height of each bar indicates the number of SNPs falling within that exon. (B) Visualization of high-impact SNV –log(P value) score of change in stability energy between the SNV-altered CD91 and WT. Higher scores represent increased magnitude of change in stability energy compared with all other candidate SNVs. Red line was chosen at a Z score of 1.96, and points above this threshold have a P < 0.05. (C) Scatter plot of high-impact SNV effect on hsp90-CD91 binding interactions. Negative and positive scores represent increased binding or decreased ligand binding, respectively, in hsp90-altered CD91 receptors compared with WT reference binding energies. The 4 ligand binding domains of CD91 are color coded as indicated. Black arrows represent SNVs that appear in 4 individual patients with lung squamous cell carcinoma, and red arrows represent SNVs that appear in 4 individual patients with skin cutaneous melanoma. The position of each SNV is indicated by a number next to each arrow. (D and E) Stacked bar plot showing predicted high-impact SNVs in human samples of lung squamous cell carcinomas (D) or skin cutaneous melanoma (E) and the associated presence of CD8⁺ immune cells from transcriptomic data. RNA-Seq expression levels (in FPKM) are used as a proxy for CD8⁺ T cell presence. Solid bars indicate SNVs with –ΔΔG docking impact, and hatched bars indicate SNVs with +ΔΔG docking impact.