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Head-to-Head Comparison of CCN4, DNMT3A, PTPN11, and SPARC as Suppressors of Anti-tumor Immunity

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Abstract

Purpose

Emergent cancer cells likely secrete factors that inhibit anti-tumor immunity. To identify such factors, we applied a functional assay with proteomics to an immunotherapy resistant syngeneic mouse melanoma model. Four secreted factors were identified that potentially mediate immunosuppression and could become targets for novel immunotherapies. We tested for consistent clinical correlates in existing human data and verified in vivo whether knocking out tumor cell production of these factors improved immune-mediated control of tumor growth.

Methods

Existing human data was analyzed for clinical correlates. A CRISPR/Cas9 approach to generate knockout cell lines and a kinetic analysis leveraging a Markov Chain Monte Carlo (MCMC) approach quantified the various knockouts’ effect on cells’ intrinsic growth rate. Flow cytometry was used to characterize differences in immune infiltration.

Results

While all four gene products were produced by malignant melanocytes, only increased CCN4 expression was associated with reduced survival in primary melanoma patients. In immunocompetent C57BL/6 mice the CCN4 knockout increased survival while the other knockouts had no effect. This survival advantage was lost when the CCN4 knockout cells were injected into immunocompromised hosts, indicating that the effect of CCN4 may be immune mediated. Parameter estimation from the MCMC analysis shows that CCN4 was the only knockout tested that decreased the net tumor growth rate in immunocompetent mice. Flow cytometry showed an increase in NK cell infiltration in CCN4 knockout tumors.

Conclusions

The results suggest that CCN4 is a mediator of immunosuppression in the melanoma tumor microenvironment and a potential collateral immunotherapy target.

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Data Availability

Bulk tissue transcriptomics profiling data using Illumina RNA sequencing was accessed from the cutaneous melanoma (SKCM) arm of the Cancer Genome Atlas. Data were downloaded from TCGA data commons using the “TCGAbiolinks” (V2.14.1) package in R (V3.6.3). Gene expression data were expressed in counts. The single cell RNAseq datasets used in the analysis for this article are available in Gene Expression Omnibus repository with the following GEO accession numbers: GSE115978 and GSE72056

Code Availability

The code used in the Markov Chain Monte Carlo analysis can be obtained from the following GitHub repository: https://github.com/arcoolbaugh/B16-In-Vivo-Screen

Abbreviations

IL-12:

Interleukin 12

DNMT3A:

DNA methyltransferase 3

PTPN11:

Protein Tyrosine Phosphatase Non-Receptor Type 11

CCN4:

Cellular Communication Network Factor 4 or Wnt-inducible signaling pathway protein-1

SPARC:

Secreted Protein Acidic and Rich in Cysteine

ATCC:

American Tissue Culture Collection

NSG:

NOD-scid IL2R γnull immunodeficient mice

WVU:

West Virginia University

SKCM:

Skin cutaneous melanoma

TCGA:

The Cancer Genome Atlas

scRNAseq:

Single cell RNA sequencing

MCMC:

Markov Chain Monte Carlo

WT:

Wildtype

KO:

Knock out

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Acknowledgements

Figure 4 was created with Biorender.com, for which the subscription was provided by the Cellular and Molecular Biology and Biomedical Engineering NIH T32 Program (GM133369) at West Virginia University. We would also like to thank Audry Fernandez for her assistance with flow cytometry experiments. This work was supported by grants received by David. J. Klinke II from the National Science Foundation (NSF CBET-1644932) and National Cancer Institute (NCI 1R01CA193473). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NSF or NCI. We also used equipment from the WVU Flow Cytometry & Single Cell Core Facility (RRID: SCR_017738). The core was supported by National Institute of Health Grants GM121322, GM103434, OD016165, and GM104942.

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Authors and Affiliations

  1. Department of Microbiology, Immunology, & Cell Biology, P.O. Box 9177, Morgantown, WV, 26506, USA

    Wentao Deng, Atefeh Razazan & David J. Klinke II

  2. Department of Chemical and Biomedical Engineering, West Virginia University, P.O. Box 6102, Morgantown, WV, 26506-6102, USA

    Anika C. Pirkey, Danielle Norman & David J. Klinke II

  3. West Virginia University Cancer Institute, 1 Medical Center Drive, Morgantown, WV, 26506, USA

    Anika C. Pirkey, Wentao Deng, Danielle Norman, Atefeh Razazan & David J. Klinke II

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Correspondence to David J. Klinke II.

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Animal Studies

All procedures involving animals were approved by the West Virginia University (WVU) Institutional Animal Care and Use Committee and performed at the WVU Animal Facility ((IACUC Protocol #1604002138).

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Supplementary Information

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12195_2023_787_MOESM1_ESM.tif

Supplementary file1 (TIF 17743 kb) Supplemental Figure S1: Western blot images confirming the knockout of (A) DNMT3A, (B) PTPN11, (C) SPARC, and (D) CCN4 in B16F0 cells.

12195_2023_787_MOESM2_ESM.tif

Supplementary file2 (TIF 13841 kb) Supplemental Figure S2: Representative images of diagnostics for Markov Chain Monte Carlo estimates of the posterior distribution of initial tumor bolus size in a single mouse. (A) The posterior distribution of the parameter for each of the three independent chains overlap, indicating agreement between the chains. (B) New steps in the Markov Chain were proposed with increased or decreased risk to achieve a desired acceptance fraction of test point of 0.20. (C) The full-length traces of three independent chains (colored in black, blue, and red, respectively) with over disperse starting points. (D) The Gelman-Rubin potential scale reduction factor (PSRF) was used to assess convergence of the Markov Chains to the posterior distribution in each parameter where a value of less than 1.2 indicated that the chains have converged.

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Pirkey, A.C., Deng, W., Norman, D. et al. Head-to-Head Comparison of CCN4, DNMT3A, PTPN11, and SPARC as Suppressors of Anti-tumor Immunity. Cel. Mol. Bioeng. 16, 431–442 (2023). https://doi.org/10.1007/s12195-023-00787-7

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