Spatial predictors of immunotherapy response in triple-negative breast cancer.
Humans
B-Lymphocytes
/ immunology
Biopsy
CD8-Positive T-Lymphocytes
/ immunology
Granzymes
/ metabolism
Histocompatibility Antigens Class II
/ immunology
Immunotherapy
Lewis X Antigen
/ metabolism
Neoadjuvant Therapy
Precision Medicine
Prognosis
Randomized Controlled Trials as Topic
T-Lymphocytes
/ immunology
Triple Negative Breast Neoplasms
/ immunology
Journal
Nature
ISSN: 1476-4687
Titre abrégé: Nature
Pays: England
ID NLM: 0410462
Informations de publication
Date de publication:
Sep 2023
Sep 2023
Historique:
received:
28
06
2022
accepted:
28
07
2023
medline:
4
10
2023
pubmed:
7
9
2023
entrez:
6
9
2023
Statut:
ppublish
Résumé
Immune checkpoint blockade (ICB) benefits some patients with triple-negative breast cancer, but what distinguishes responders from non-responders is unclear
Identifiants
pubmed: 37674077
doi: 10.1038/s41586-023-06498-3
pii: 10.1038/s41586-023-06498-3
pmc: PMC10533410
doi:
Substances chimiques
Granzymes
EC 3.4.21.-
Histocompatibility Antigens Class II
0
HNF1A protein, human
0
Lewis X Antigen
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
868-876Informations de copyright
© 2023. The Author(s).
Références
Schmid, P. et al. Event-free survival with pembrolizumab in early triple-negative breast cancer. N. Engl. J. Med. 386, 556–567 (2022).
pubmed: 35139274
Pardoll, D. M. The blockade of immune checkpoints in cancer immunotherapy. Nat. Rev. Cancer 12, 252–264 (2012).
pubmed: 22437870
pmcid: 4856023
Giesen, C. et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat. Methods 11, 417–422 (2014).
pubmed: 24584193
Adams, S. et al. Current landscape of immunotherapy in breast cancer: a review. JAMA Oncol. 5, 1205–1214 (2019).
pubmed: 30973611
pmcid: 8452050
Bianchini, G., Balko, J. M., Mayer, I. A., Sanders, M. E. & Gianni, L. Triple-negative breast cancer: challenges and opportunities of a heterogeneous disease. Nat. Rev. Clin. Oncol. 13, 674–690 (2016).
pubmed: 27184417
pmcid: 5461122
Bianchini, G., De Angelis, C., Licata, L. & Gianni, L. Treatment landscape of triple-negative breast cancer – expanded options, evolving needs. Nat. Rev. Clin. Oncol. 19, 91–113 (2022).
pubmed: 34754128
Schmid, P. et al. Pembrolizumab for early triple-negative breast cancer. N. Engl. J. Med. 382, 810–821 (2020).
pubmed: 32101663
Golstein, P. & Griffiths, G. M. An early history of T cell-mediated cytotoxicity. Nat. Rev. Immunol. 18, 527–535 (2018).
pubmed: 29662120
Azizi, E. et al. Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell 174, 1293–1308.e1236 (2018).
pubmed: 29961579
pmcid: 6348010
Wagner, J. et al. A single-cell atlas of the tumor and immune ecosystem of human breast cancer. Cell 177, 1330–1345.e1318 (2019).
pubmed: 30982598
pmcid: 6526772
Jackson, H. W. et al. The single-cell pathology landscape of breast cancer. Nature 578, 615–620 (2020).
pubmed: 31959985
Ali, H. R. et al. Imaging mass cytometry and multiplatform genomics define the phenogenomic landscape of breast cancer. Nat. Cancer 1, 163–175 (2020).
pubmed: 35122013
Danenberg, E. et al. Breast tumor microenvironment structures are associated with genomic features and clinical outcome. Nat. Genet. https://doi.org/10.1038/s41588-022-01041-y (2022).
doi: 10.1038/s41588-022-01041-y
pubmed: 35437329
pmcid: 7612730
Gianni, L. et al. Pathologic complete response (pCR) to neoadjuvant treatment with or without atezolizumab in triple-negative, early high-risk and locally advanced breast cancer: NeoTRIP Michelangelo randomized study. Ann. Oncol. https://doi.org/10.1016/j.annonc.2022.02.004 (2022).
doi: 10.1016/j.annonc.2022.02.004
pubmed: 36228963
Greenwald, N. F. et al. Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning. Nat. Biotechnol. https://doi.org/10.1038/s41587-021-01094-0 (2021).
doi: 10.1038/s41587-021-01094-0
pubmed: 34795433
pmcid: 9010346
Im, S. J. et al. Defining CD8
pubmed: 27501248
pmcid: 5297183
Lehmann, B. D. et al. Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. J. Clin. Invest. 121, 2750–2767 (2011).
pubmed: 21633166
pmcid: 3127435
Cortazar, P. et al. Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis. Lancet 384, 164–172 (2014).
pubmed: 24529560
Jansen, C. S. et al. An intra-tumoral niche maintains and differentiates stem-like CD8 T cells. Nature 576, 465–470 (2019).
pubmed: 31827286
pmcid: 7108171
Kursa, M. B. & Rudnicki, W. R. Feature selection with the Boruta package. J. Stat. Softw. 36, 1–13 (2010).
Gonzalez-Ericsson, P. I. et al. Tumor-specific major histocompatibility-II expression predicts benefit to anti-PD-1/L1 therapy in patients with HER2-negative primary breast cancer. Clin. Cancer Res. 27, 5299–5306 (2021).
pubmed: 34315723
pmcid: 8792110
Axelrod, M. L., Cook, R. S., Johnson, D. B. & Balko, J. M. Biological consequences of MHC-II expression by tumor cells in cancer. Clin. Cancer Res. 25, 2392–2402 (2019).
pubmed: 30463850
Baldominos, P. et al. Quiescent cancer cells resist T cell attack by forming an immunosuppressive niche. Cell 185, 1694–1708.e1619 (2022).
pubmed: 35447074
Sade-Feldman, M. et al. Defining T cell states associated with response to checkpoint immunotherapy in melanoma. Cell 175, 998–1013.e1020 (2018).
pubmed: 30388456
pmcid: 6641984
Li, H. et al. Dysfunctional CD8 T cells form a proliferative, dynamically regulated compartment within human melanoma. Cell https://doi.org/10.1016/j.cell.2018.11.043 (2018).
doi: 10.1016/j.cell.2018.11.043
pubmed: 30595452
pmcid: 7253294
Gruosso, T. et al. Spatially distinct tumor immune microenvironments stratify triple-negative breast cancers. J. Clin. Invest. 129, 1785–1800 (2019).
pubmed: 30753167
pmcid: 6436884
Keren, L. et al. A structured tumor-immune microenvironment in triple negative breast cancer revealed by multiplexed ion beam imaging. Cell 174, 1373–1387.e1319 (2018).
pubmed: 30193111
pmcid: 6132072
Oliveira, G. et al. Phenotype, specificity and avidity of antitumour CD8
pubmed: 34290406
pmcid: 9187974
Caushi, J. X. et al. Transcriptional programs of neoantigen-specific TIL in anti-PD-1-treated lung cancers. Nature 596, 126–132 (2021).
pubmed: 34290408
pmcid: 8338555
Bassez, A. et al. A single-cell map of intratumoral changes during anti-PD1 treatment of patients with breast cancer. Nat. Med. 27, 820–832 (2021).
pubmed: 33958794
Brooks, S. A. & Leathem, A. J. Expression of the CD15 antigen (Lewis x) in breast cancer. Histochem. J. 27, 689–693 (1995).
pubmed: 8557532
Marron, T. U. et al. Neoadjuvant clinical trials provide a window of opportunity for cancer drug discovery. Nat. Med. 28, 626–629 (2022).
pubmed: 35347282
pmcid: 9901535
Salgado, R. et al. The evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer: recommendations by an International TILs Working Group 2014. Ann. Oncol. 26, 259–271 (2015).
pubmed: 25214542
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
pubmed: 23104886
Trapnell, C. et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 7, 562–578 (2012).
pubmed: 22383036
pmcid: 3334321
Ring, B. Z. et al. Generation of an algorithm based on minimal gene sets to clinically subtype triple negative breast cancer patients. BMC Cancer 16, 143 (2016).
pubmed: 26908167
pmcid: 4763445
Mei, H. E., Leipold, M. D. & Maecker, H. T. Platinum-conjugated antibodies for application in mass cytometry. Cytometry A 89, 292–300 (2016).
pubmed: 26355391
Han, G., Spitzer, M. H., Bendall, S. C., Fantl, W. J. & Nolan, G. P. Metal-isotope-tagged monoclonal antibodies for high-dimensional mass cytometry. Nat. Protoc. 13, 2121–2148 (2018).
pubmed: 30258176
pmcid: 7075473
Han, G. et al. Atomic mass tag of bismuth-209 for increasing the immunoassay multiplexing capacity of mass cytometry. Cytometry A 91, 1150–1163 (2017).
pubmed: 29205767
pmcid: 5802970
Schapiro, D. et al. histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data. Nat. Methods 14, 873–876 (2017).
pubmed: 28783155
pmcid: 5617107
Chevrier, S. et al. Compensation of signal spillover in suspension and imaging mass cytometry. Cell Systems 6, 612–620.e615 (2018).
pubmed: 29605184
pmcid: 5981006
Zanotelli, V. R. & Bodenmiller, B. ImcSegmentationPipeline: a pixel classification based multiplexed image segmentation pipeline. GitHub https://github.com/BodenmillerGroup/ImcSegmentationPipeline (2017).
Berg, S. et al. ilastik: interactive machine learning for (bio)image analysis. Nat. Methods 16, 1226–1232 (2019).
pubmed: 31570887
Carpenter, A. E. et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100 (2006).
pubmed: 17076895
pmcid: 1794559
Levine, J. H. et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015).
pubmed: 26095251
pmcid: 4508757
Kratochvíl, M. et al. GigaSOM.jl: high-performance clustering and visualization of huge cytometry datasets. GigaScience 9, giaa127 (2020).
pubmed: 33205814
pmcid: 7672468
Nowicka, M. et al. CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets. F1000Res. https://doi.org/10.12688/f1000research.11622.1 (2017).