The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Exploiting the neoantigen landscape for immunotherapy of pancreatic ductal adenocarcinoma
Open accessAbstract
Immunotherapy approaches for pancreatic ductal adenocarcinoma (PDAC) have met with limited success. It has been postulated that a low mutation load may lead to a paucity of T cells within the tumor microenvironment (TME). However, it is also possible that while neoantigens are present, an effective immune response cannot be generated due to an immune suppressive TME. To discern whether targetable neoantigens exist in PDAC, we performed a comprehensive study using genomic profiles of 221 PDAC cases extracted from public databases. Our findings reveal that: (a) nearly all PDAC samples harbor potentially targetable neoantigens; (b) T cells are present but generally show a reduced activation signature; and (c) markers of efficient antigen presentation are associated with a reduced signature of markers characterizing cytotoxic T cells. These findings suggest that despite the presence of tumor specific neoepitopes, T cell activation is actively suppressed in PDAC. Further, we identify iNOS as a potential mediator of immune suppression that might be actionable using pharmacological avenues.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome
Li, B., & Dewey, C. N. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-323 · Cited by 22,179
PD-1 Blockade in Tumors with Mismatch-Repair Deficiency
Le, D. T., Uram, J. N., Wang, H., Bartlett, B. R., Kemberling, H., Eyring, A. D., Skora, A. D., Luber, B. S., Azad, N. S., Laheru, D., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1500596 · Cited by 9,377
The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity
Barretina, J., Caponigro, G., Stransky, N., Venkatesan, K., Margolin, A. A., Kim, S., Wilson, C. J., Lehár, J., Kryukov, G. V., Sonkin, D., et al. (2012) · Nature · doi:10.1038/nature11003 · Cited by 7,950
Self-citationGenomic analyses identify molecular subtypes of pancreatic cancer
Bailey, P., Chang, D. K., Nones, K., Johns, A. L., Patch, A.-M., Gingras, M.-C., Miller, D. K., Christ, A. N., Bruxner, T. J. C., Quinn, M. C., et al. (2016) · Nature · doi:10.1038/nature16965 · Cited by 3,621
Self-citationGenomic classification of cutaneous melanoma
Akbani, R., Akdemir, K. C., Aksoy, B. A., Albert, M., Ally, A., Amin, S. B., Arachchi, H., Arora, A., Auman, J. T., Ayala, B., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.044 · Cited by 3,151
Self-citationWhole genomes redefine the mutational landscape of pancreatic cancer
Initiative, A. P. C. G., Waddell, N., Pajic, M., Patch, A.-M., Chang, D. K., Kassahn, K. S., Bailey, P., Johns, A. L., Miller, D., Nones, K., et al. (2015) · Nature · doi:10.1038/nature14169 · Cited by 2,642
Self-citationHidalgo, M. (2010) · New England Journal of Medicine · doi:10.1056/nejmra0901557 · Cited by 2,570
Feig, C., Jones, J. O., Kraman, M., Wells, R. J. B., Deonarine, A., Chan, D. S., Connell, C. M., Roberts, E. W., Zhao, Q., Caballero, O. L., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1320318110 · Cited by 2,100
Identification of T-cell Receptors Targeting KRAS-Mutated Human Tumors
Wang, Q. J., Yu, Z., Griffith, K., Hanada, K.-I., Restifo, N. P., & Yang, J. C. (2016) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-15-0188 · Cited by 240
Tumor microenvironment-based feed-forward regulation of NOS2 in breast cancer progression
Heinecke, J. L., Ridnour, L. A., Cheng, R. Y. S., Switzer, C. H., Lizardo, M. M., Khanna, C., Glynn, S. A., Hussain, S. P., Young, H. A., Ambs, S., et al. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1401799111 · Cited by 177
Cited by
Personalized RNA neoantigen vaccines stimulate T cells in pancreatic cancer
Rojas, L. A., Sethna, Z., Soares, K. C., Olcese, C., Pang, N., Patterson, E., Lihm, J., Ceglia, N., Guasp, P., Chu, A., et al. (2023) · Nature · doi:10.1038/s41586-023-06063-y · Cited by 1,432
Neoantigens: promising targets for cancer therapy
Xie, N., Shen, G., Gao, W., Huang, Z., Huang, C., & Fu, L. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-022-01270-x · Cited by 1,020
O’Reilly, E. M., Oh, D.-Y., Dhani, N., Renouf, D. J., Lee, M. A., Sun, W., Fisher, G., Hezel, A., Chang, S.-C., Vlahovic, G., et al. (2019) · JAMA Oncology · doi:10.1001/jamaoncol.2019.1588 · Cited by 767
Immunotherapy for pancreatic cancer: A 2020 update
Schizas, D., Charalampakis, N., Kole, C., Economopoulou, P., Koustas, E., Gkotsis, E., Ziogas, D., Psyrri, A., & Karamouzis, M. V. (2020) · Cancer Treatment Reviews · doi:10.1016/j.ctrv.2020.102016 · Cited by 458
Broadening the Impact of Immunotherapy to Pancreatic Cancer: Challenges and Opportunities
Balachandran, V. P., Beatty, G. L., & Dougan, S. K. (2019) · Gastroenterology · doi:10.1053/j.gastro.2018.12.038 · Cited by 444
Metadata
- Authors
- Bailey, P., Chang, D. K., Forget, M.-A., Lucas, F. A. S., Alvarez, H. A., Haymaker, C., Chattopadhyay, C., Kim, S.-H., Ekmekcioglu, S., Grimm, E. A., Biankin, A. V., Hwu, P., Maitra, A., & Roszik, J.
- Venue
- Scientific Reports
- Published
- 2016-10-20
- Volume
- 6
- Issue
- 1
- Pages
- 35848-35848
- ISSN
- 2045-2322 (ISSN-L)
- OpenAlex
- W2538063110
- Cited by
- 170
- References
- 10
- FWCI
- 7.57
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
