Clinical relevance of host immunity in breast cancer: from TILs to the clinic
Abstract
The clinical relevance of the host immune system in breast cancer has long been unexplored. Studies developed over the past decade have highlighted the biological heterogeneity of breast cancer, prompting researchers to investigate whether the role of the immune system in this malignancy is similar across different molecular subtypes of the disease. The presence of high levels of lymphocytic infiltration has been consistently associated with a more-favourable prognosis in patients with early stage triple-negative and HER2-positive breast cancer. These infiltrates seem to reflect favourable host antitumour immune responses, suggesting that immune activation is important for improving survival outcomes. In this Review, we discuss the composition of the immune infiltrates observed in breast cancers, as well as data supporting the clinical relevance of host antitumour immunity, as represented by lymphocytic infiltration, and how this biomarker could be used in the clinical setting. We also discuss the rationale for enhancing immunity in breast cancer, including early data on the efficacy of T-cell checkpoint inhibition in this setting.
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
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
High levels of lymphocyte infiltration have been related to longer disease-free survival or improved overall survival in multiple cancer types including early stage triple-negative and HER2-positive breast cancer.
“High levels of lymphocyte infiltration have been related to longer disease-free survival or improved overall survival (OS) in multiple cancer types [178] including early stage triple-negative and HER2-positive breast cancer [179].”
From AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020) · cites this paper as [179]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Combined Nivolumab and Ipilimumab or Monotherapy in Untreated Melanoma
Larkin, J., Chiarion-Sileni, V., Gonzalez, R., Grob, J. J., Cowey, C. L., Lao, C. D., Schadendorf, D., Dummer, R., Smylie, M., Rutkowski, P., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1504030 · Cited by 8,157 · see citing works
Mutational landscape determines sensitivity to PD-1 blockade in non–small cell lung cancer
Rizvi, N. A., Hellmann, M. D., Snyder, A., Kvistborg, P., Makarov, V., Havel, J. J., Lee, W., Yuan, J., Wong, P., Ho, T. S., et al. (2015) · Science · doi:10.1126/science.aaa1348 · Cited by 7,802 · see citing works
PD-1 blockade induces responses by inhibiting adaptive immune resistance
Tumeh, P. C., Harview, C. L., Yearley, J. H., Shintaku, I. P., Taylor, E. J. M., Robert, L., Chmielowski, B., Spasic, M., Henry, G., Ciobanu, V., et al. (2014) · Nature · doi:10.1038/nature13954 · Cited by 6,609 · see citing works
Tumor-associated B7-H1 promotes T-cell apoptosis: A potential mechanism of immune evasion
Dong, H., Strome, S. E., Salomao, D. R., Tamura, H., Hirano, F., Flies, D. B., Roche, P. C., Lu, J., Zhu, G., Tamada, K., et al. (2002) · Nature Medicine · doi:10.1038/nm730 · Cited by 5,025 · see citing works
Nivolumab plus Ipilimumab in Advanced Melanoma
Wolchok, J. D., Kluger, H., Callahan, M. K., Postow, M. A., Rizvi, N. A., Lesokhin, A. M., Segal, N. H., Ariyan, C. E., Gordon, R.-A., Reed, K., et al. (2013) · New England Journal of Medicine · doi:10.1056/nejmoa1302369 · Cited by 4,230 · see citing works
Molecular and genetic properties of tumors associated with local immune cytolytic activity
Rooney, M. S., Shukla, S. A., Wu, C. J., Getz, G., & Hacohen, N. (2015) · Cell · doi:10.1016/j.cell.2014.12.033 · Cited by 3,825 · see citing works
Apetoh, L., Ghiringhelli, F., Tesniere, A., Obeid, M., Ortiz, C., Criollo, A., Mignot, G., Maiuri, M. C., Ullrich, E., Saulnier, P., et al. (2007) · Nature Medicine · doi:10.1038/nm1622 · Cited by 3,056 · see citing works
Genomic correlates of response to CTLA-4 blockade in metastatic melanoma
Van Allen, E. M., Miao, D., Schilling, B., Shukla, S. A., Blank, C., Zimmer, L., Sucker, A., Hillen, U., Geukes Foppen, M. H., Goldinger, S. M., et al. (2015) · Science · doi:10.1126/science.aad0095 · Cited by 2,839 · see citing works
CD28 and CTLA-4 have opposing effects on the response of T cells to stimulation.
Krummel, M. F., & Allison, J. P. (1995) · The Journal of experimental medicine · doi:10.1084/jem.182.2.459 · Cited by 2,245 · see citing works
Immunological aspects of cancer chemotherapy
Zitvogel, L., Apetoh, L., Ghiringhelli, F., & Kroemer, G. (2008) · Nature Reviews Immunology · doi:10.1038/nri2216 · Cited by 1,712 · see citing works
Spranger, S., Spaapen, R. M., Zha, Y., Williams, J., Meng, Y., Ha, T. T., & Gajewski, T. F. (2013) · Science Translational Medicine · doi:10.1126/scitranslmed.3006504 · Cited by 1,677 · see citing works
Fuertes, M. B., Kacha, A. K., Kline, J., Woo, S.-R., Kranz, D. M., Murphy, K. M., & Gajewski, T. F. (2011) · Journal of Experimental Medicine · doi:10.1084/jem.20101159 · Cited by 1,192 · see citing works
CEACAM1 regulates TIM-3-mediated tolerance and exhaustion
Huang, Y.-H., Zhu, C., Kondo, Y., Anderson, A. C., Gandhi, A., Russell, A., Dougan, S. K., Petersen, B.-S., Melum, E., Pertel, T., et al. (2015) · Nature · doi:10.1038/nature13848 · Cited by 717 · see citing works
Stagg, J., Loi, S., Divisekera, U., Ngiow, S. F., Duret, H., Yagita, H., Teng, M. W., & Smyth, M. J. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1016569108 · Cited by 496 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Harbeck, N., Penault-Llorca, F., Cortes, J., Gnant, M., Houssami, N., Poortmans, P., Ruddy, K., Tsang, J., & Cardoso, F. (2019) · Nature Reviews Disease Primers · doi:10.1038/s41572-019-0111-2 · Cited by 3,024 · see citing works
Triple-negative breast cancer: challenges and opportunities of a heterogeneous disease
Bianchini, G., Balko, J. M., Mayer, I. A., Sanders, M. E., & Gianni, L. (2016) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2016.66 · Cited by 3,079 · see citing works
Denkert, C., von Minckwitz, G., Darb-Esfahani, S., Lederer, B., Heppner, B. I., Weber, K. E., Budczies, J., Huober, J., Klauschen, F., Furlanetto, J., et al. (2018) · The Lancet Oncology · doi:10.1016/s1470-2045(17)30904-x · Cited by 2,157 · see citing works
Self-citationTertiary lymphoid structures in the era of cancer immunotherapy
Sautès-Fridman, C., Petitprez, F., Calderaro, J., & Fridman, W. H. (2019) · Nature reviews. Cancer · doi:10.1038/s41568-019-0144-6 · Cited by 1,884 · see citing works
Kathleen Cuningham Foundation Consortium for Research into Familial Breast Cancer (kConFab), Savas, P., Virassamy, B., Ye, C., Salim, A., Mintoff, C. P., Caramia, F., Salgado, R., Byrne, D. J., Teo, Z. L., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0078-7 · Cited by 992 · see citing works
Self-citationLoi, S., Drubay, D., Adams, S., Pruneri, G., Francis, P. A., Lacroix-Triki, M., Joensuu, H., Dieci, M. V., Badve, S., Demaria, S., et al. (2019) · Journal of Clinical Oncology · doi:10.1200/jco.18.01010 · Cited by 893 · see citing works
Self-citationPathogenesis of Triple-Negative Breast Cancer
Derakhshan, F., & Reis-Filho, J. S. (2022) · Annual Review of Pathology: Mechanisms of Disease · doi:10.1146/annurev-pathol-042420-093238 · Cited by 714 · see citing works
AI in Medical Imaging Informatics: Current Challenges and Future Directions
Panayides, A. S., Amini, A., Filipovic, N. D., Sharma, A., Tsaftaris, S. A., Young, A., Foran, D., Do, N., Golemati, S., Kurc, T., et al. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2991043 · Cited by 713 · see citing works
Schmid, P., Salgado, R., Park, Y. H., Muñoz-Couselo, E., Kim, S. B., Sohn, J., Im, S.-A., Foukakis, T., Kuemmel, S., Dent, R., et al. (2020) · Annals of Oncology · doi:10.1016/j.annonc.2020.01.072 · Cited by 413 · see citing works
Self-citationSingh, M., Al-Eryani, G., Carswell, S., Ferguson, J. M., Blackburn, J., Barton, K., Roden, D., Luciani, F., Giang Phan, T., Junankar, S., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11049-4 · Cited by 308 · see citing works
Cytotoxic T-cells mediate exercise-induced reductions in tumor growth
Rundqvist, H., Veliça, P., Barbieri, L., Gameiro, P. A., Bargiela, D., Gojkovic, M., Mijwel, S., Reitzner, S. M., Wulliman, D., Ahlstedt, E., et al. (2020) · eLife · doi:10.7554/elife.59996 · Cited by 216 · see citing works
Hendry, S. A., Farnsworth, R. H., Solomon, B., Achen, M. G., Stacker, S. A., & Fox, S. B. (2016) · Frontiers in Immunology · doi:10.3389/fimmu.2016.00621 · Cited by 188 · see citing works
A population-level digital histologic biomarker for enhanced prognosis of invasive breast cancer
Amgad, M., Hodge, J. M., Elsebaie, M. A. T., Bodelon, C., Puvanesarajah, S., Gutman, D. A., Siziopikou, K. P., Goldstein, J. A., Gaudet, M. M., Teras, L. R., et al. (2024) · Nature Medicine · doi:10.1038/s41591-023-02643-7 · Cited by 137 · see citing works
Soundararajan, R., Fradette, J., Konen, J., Moulder, S., Zhang, X., Gibbons, D., Varadarajan, N., Wistuba, I., Tripathy, D., Bernatchez, C., et al. (2019) · Cancers · doi:10.3390/cancers11050714 · Cited by 103 · see citing works
Atezolizumab for the treatment of breast cancer
Reddy, S. M., Carroll, E., & Nanda, R. (2020) · Expert Review of Anticancer Therapy · doi:10.1080/14737140.2020.1732211 · Cited by 94 · see citing works
Wu, J., Li, X., Teng, X., Rubin, D. L., Napel, S., Daniel, B. L., & Li, R. (2018) · Breast Cancer Research · doi:10.1186/s13058-018-1039-2 · Cited by 53 · see citing works
Thagaard, J., Stovgaard, E. S., Vognsen, L. G., Hauberg, S., Dahl, A., Ebstrup, T., Doré, J., Vincentz, R. E., Jepsen, R. K., Roslind, A., et al. (2021) · Cancers · doi:10.3390/cancers13123050 · Cited by 50 · see citing works
Refining patient selection for breast cancer immunotherapy: beyond PD-L1
Kossai, M., Radosevic-Robin, N., & Penault-Llorca, F. (2021) · ESMO Open · doi:10.1016/j.esmoop.2021.100257 · Cited by 36 · see citing works
Deep learning in digital pathology for personalized treatment plans of cancer patients
Wen, Z., Wang, S., Yang, D. M., Xie, Y., Chen, M., Bishop, J., & Xiao, G. (2023) · Seminars in Diagnostic Pathology · doi:10.1053/j.semdp.2023.02.003 · Cited by 34 · see citing works
Lin, T., Qiu, Y., Peng, W., & Peng, L. (2020) · BioMed Research International · doi:10.1155/2020/2148253 · Cited by 31 · see citing works
Immunotherapeutic strategies in breast cancer: a clinical update
Zhang, J. Q., & Plitas, G. (2021) · Journal of Surgical Oncology · doi:10.1002/jso.26287 · Cited by 6 · see citing works
- Authors
- Savas, P., Salgado, R., Denkert, C., Sotiriou, C., Darcy, P. K., Smyth, M. J., & Loi, S.
- Affiliations
- Peter MacCallum Cancer Centre (AU)
- Institut Jules Bordet (BE)
- Deutsches Konsortium für Translationale Krebsforschung (DE)
- QIMR Berghofer Medical Research Institute (AU)
- Corresponding author
- Loi, Sherene
- ORCID
- Venue
- Nature Reviews Clinical Oncology
- Published
- 2015-12-15
- Volume
- 13
- Issue
- 4
- Pages
- 228-241
- ISSN
- 1759-4774 (ISSN-L)
- 1759-4782
- OpenAlex
- W2221588520
- Cited by
- 890
- References
- 14
- Citations by year
- 2026: 50 · 2025: 64 · 2024: 86 · 2023: 120 · 2022: 140 · 2021: 109 · 2020: 101 · 2019: 75 · 2018: 54 · 2017: 58 · 2016: 32
- FWCI
- 5.16
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Reviews Clinical Oncology (publishedVersion)
- PubMed (publishedVersion)
- Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles) (submittedVersion)
- Funders
- Bristol-Myers Squibb
- Pfizer
- Cancer Council Victoria
- Topics
- Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Life Sciences › Immunology and Microbiology › Immunology › Immune cells in cancer
- Keywords
- tumor-infiltrating lymphocytes, breast cancer, prognostic factors, predictive biomarkers, triple-negative breast cancer, HER2-positive breast cancer, molecular subtypes, immune checkpoint inhibitors, early breast cancer
- MeSH terms
- Animals, Breast Neoplasms, Female, Humans, Immunity, Prognosis, Biomarkers, Tumor, Lymphocytes, Tumor-Infiltrating, Patient Outcome Assessment
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
