The DOI matches its Crossref record; 82 of the 83 references it cites were found in a registry or an index.
Genetically Defined Syngeneic Mouse Models of Ovarian Cancer as Tools for the Discovery of Combination Immunotherapy
Open accessPublished version of a preprint
Abstract
Abstract Despite advances in immuno-oncology, the relationship between tumor genotypes and response to immunotherapy remains poorly understood, particularly in high-grade serous tubo-ovarian carcinomas (HGSC). We developed a series of mouse models that carry genotypes of human HGSCs and grow in syngeneic immunocompetent hosts to address this gap. We transformed murine-fallopian tube epithelial cells to phenocopy homologous recombination–deficient tumors through a combined loss of Trp53, Brca1, Pten, and Nf1 and overexpression of Myc and Trp53R172H, which was contrasted with an identical model carrying wild-type Brca1. For homologous recombination–proficient tumors, we constructed genotypes combining loss of Trp53 and overexpression of Ccne1, Akt2, and Trp53R172H, and driven by KRASG12V or Brd4 or Smarca4 overexpression. These lines form tumors recapitulating human disease, including genotype-driven responses to treatment, and enabled us to identify follistatin as a driver of resistance to checkpoint inhibitors. These data provide proof of concept that our models can identify new immunotherapy targets in HGSC. Significance: We engineered a panel of murine fallopian tube epithelial cells bearing mutations typical of HGSC and capable of forming tumors in syngeneic immunocompetent hosts. These models recapitulate tumor microenvironments and drug responses characteristic of human disease. In a Ccne1-overexpressing model, immune-checkpoint resistance was driven by follistatin. This article is highlighted in the In This Issue feature, p. 211
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“Generation of multiple fallopian tube epithelial cell lines with combinations of common mutations in HGSOC (Tp53, Brca1, Brca2, Ccne1, Akt2, Brd4, Smarca4, Kras, Myc, Nf1, and Pten) using CRISPR/Cas9 recapitulated histopathological and clinical features observed in HGSOC patients, such as ascites and peritoneal metastases (Iyer et al., 2021).”
From Three-dimensional modelling of ovarian cancer: from cell lines to organoids for discovery and personalized medicine (2022) · cites this paper as Iyer et al., 2021
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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
featureCounts: an efficient general purpose program for assigning sequence reads to genomic features
Liao, Y., Smyth, G. K., & Shi, W. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt656 · Cited by 27,219
Integrated genomic analyses of ovarian carcinoma
Perou, C. M. (2019) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/hvp3-wg08 · Cited by 7,927
Cancer immunotherapy using checkpoint blockade
Ribas, A., & Wolchok, J. D. (2018) · Science · doi:10.1126/science.aar4060 · Cited by 6,791
Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y., & Greenleaf, W. J. (2013) · Nature Methods · doi:10.1038/nmeth.2688 · Cited by 6,422
Molecular and cellular insights into T cell exhaustion
Wherry, E. J., & Kurachi, M. (2015) · Nature Reviews Immunology · doi:10.1038/nri3862 · Cited by 4,652
Wherry, E. J. (2011) · Nature Immunology · doi:10.1038/ni.2035 · Cited by 4,062 · cited 2×
“S3I), indicative once again of impaired effector T-cell functions (36, 37).”
BackgroundVignali, D. A. A., Collison, L. W., & Workman, C. J. (2008) · Nature Reviews Immunology · doi:10.1038/nri2343 · Cited by 3,463
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Vázquez-Garćıa, I., Uhlitz, F., Ceglia, N., Lim, J. L., Wu, M. K. Y., Mohibullah, N., Niyazov, J., Ruiz, A. E. B., Boehm, K. M., Bojilova, V., et al. (2022) · Nature · doi:10.1038/s41586-022-05496-1 · Cited by 329
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Schoutrop, E., Moyano‐Galceran, L., Lheureux, S., Mattsson, J., Lehti, K., Dahlstrand, H. M., & Magalhaes, I. (2022) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2022.03.027 · Cited by 181
Metadata
- Authors
- Iyer, S., Zhang, S., Yucel, S., Horn, H., Smith, S. G., Reinhardt, F., Hoefsmit, E., Assatova, B., Casado, J., Meinsohn, M.-C., Barrasa, M. I., Bell, G. W., Pérez-Villatoro, F., Huhtinen, K., Hynninen, J., Oikkonen, J., Galhenage, P. M., Pathania, S., Hammond, P. T., Neel, B. G., Farkkila, A., Pépin, D., & Weinberg, R. A.
- Affiliations
- Whitehead Institute for Biomedical Research (US)
- NYU Langone Health (US)
- Broad Institute (US)
- Massachusetts Institute of Technology (US)
- 1Whitehead Institute for Biomedical Research, Cambridge, Massachusetts.
- University of Helsinki (FI)
- Harvard University (US)
- 6Research Program in Systems Oncology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
- University of Turku (FI)
- University of Massachusetts Boston (US)
- Corresponding author
- Pépin, David
- Weinberg, Robert A.
- ORCID
- Iyer, Sonia
- Zhang, Shuang
- Horn, Heiko
- Smith, Sean G.
- Assatova, Bimarzhan
- Casado, Julia
- Meinsohn, Marie-Charlotte
- Barrasa, M. Inmaculada
- Bell, George W.
- Pérez-Villatoro, Fernando
- Huhtinen, Kaisa
- Hynninen, Johanna
- Oikkonen, Jaana
- Galhenage, Pamoda M.
- Pathania, Shailja
- Hammond, Paula T.
- Neel, Benjamin G.
- Farkkila, Anniina
- Pépin, David
- Weinberg, Robert A.
- Venue
- Cancer Discovery
- Published
- 2021-02-01
- Volume
- 11
- Issue
- 2
- Pages
- 384-407
- ISSN
- 2159-8274 (ISSN-L)
- 2159-8290
- PMC
- PMC8344888
- OpenAlex
- W3097756289
- Cited by
- 136
- References
- 83
- Citations by year
- 2026: 20 · 2025: 33 · 2024: 24 · 2023: 21 · 2022: 22 · 2021: 13
- FWCI
- 12.99
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Cancer Discovery (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- UTUPub (University of Turku) (submittedVersion)
- Funders
- NIH (R01 CA0784561, P01 CA080111)
- Ovarian Cancer Research Program (W81XWH-17-1-0212)
- US Army Medical Research (21702-5014)
- Topics
- Health Sciences › Medicine › Oncology › Ovarian cancer diagnosis and treatment
- Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › BRCA gene mutations in cancer
- Keywords
- high-grade serous ovarian cancer, syngeneic mouse model, genetically engineered mouse models, combination immunotherapy, immunotherapy resistance, immune checkpoint inhibitors, follistatin, homologous recombination deficiency, BRCA1, PTEN, neurofibromatosis type 1, c-Myc, cyclin E1, KRAS G12V, BRD4, SMARCA4, tumor microenvironment, immunotherapy response prediction
- MeSH terms
- Immune Checkpoint Inhibitors, Animals, Disease Models, Animal, Drug Therapy, Combination, Fallopian Tube Neoplasms, Female, Mice, Transgenic, Ovarian Neoplasms, Cystadenocarcinoma, Serous
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