Unsupervised representation learning of chromatin images identifies changes in cell state and tissue organization in DCIS.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
20 Jul 2024
20 Jul 2024
Historique:
received:
04
04
2023
accepted:
05
07
2024
medline:
20
7
2024
pubmed:
20
7
2024
entrez:
19
7
2024
Statut:
epublish
Résumé
Ductal carcinoma in situ (DCIS) is a pre-invasive tumor that can progress to invasive breast cancer, a leading cause of cancer death. We generate a large-scale tissue microarray dataset of chromatin images, from 560 samples from 122 female patients in 3 disease stages and 11 phenotypic categories. Using representation learning on chromatin images alone, without multiplexed staining or high-throughput sequencing, we identify eight morphological cell states and tissue features marking DCIS. All cell states are observed in all disease stages with different proportions, indicating that cell states enriched in invasive cancer exist in small fractions in normal breast tissue. Tissue-level analysis reveals significant changes in the spatial organization of cell states across disease stages, which is predictive of disease stage and phenotypic category. Taken together, we show that chromatin imaging represents a powerful measure of cell state and disease stage of DCIS, providing a simple and effective tumor biomarker.
Identifiants
pubmed: 39030176
doi: 10.1038/s41467-024-50285-1
pii: 10.1038/s41467-024-50285-1
doi:
Substances chimiques
Chromatin
0
Biomarkers, Tumor
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
6112Subventions
Organisme : United States Department of Defense | United States Navy | Office of Naval Research (ONR)
ID : N00014-22-1-2116
Organisme : Simons Foundation
ID : Simons Investigator Award
Organisme : U.S. Department of Health & Human Services | NIH | National Center for Complementary and Integrative Health (NCCIH)
ID : 1DP2AT012345
Organisme : Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation)
ID : 310030 208046
Informations de copyright
© 2024. The Author(s).
Références
Sung, H. et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Ca. Cancer J. Clin. 71, 209–249 (2021).
pubmed: 33538338
doi: 10.3322/caac.21660
Ward, E. M. et al. Cancer statistics: Breast cancer in situ. Ca. Cancer J. Clin. 65, 481–495 (2015).
pubmed: 26431342
doi: 10.3322/caac.21321
In situ breast carcinoma incidence statistics. Cancer Research UK https://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/breast-cancer/incidence-in-situ (2015).
van Seijen, M. et al. Ductal carcinoma in situ: to treat or not to treat, that is the question. Br. J. Cancer 121, 285–292 (2019).
pubmed: 31285590
pmcid: 6697179
doi: 10.1038/s41416-019-0478-6
Kim, C. et al. Interventions are needed to support patient–provider decision-making for DCIS: a scoping review. Breast Cancer Res. Treat. 168, 579–592 (2018).
pubmed: 29273956
doi: 10.1007/s10549-017-4613-x
Narod, S. A., Iqbal, J., Giannakeas, V., Sopik, V. & Sun, P. Breast cancer mortality after a diagnosis of ductal carcinoma in situ. JAMA Oncol. 1, 888–896 (2015).
pubmed: 26291673
doi: 10.1001/jamaoncol.2015.2510
Ryser, M. D. et al. Cancer outcomes in DCIS patients without locoregional treatment. JNCI J. Natl. Cancer Inst. 111, 952–960 (2019).
pubmed: 30759222
doi: 10.1093/jnci/djy220
Bijker, N. et al. Risk factors for recurrence and metastasis after breast-conserving therapy for ductal carcinoma-in-situ: analysis of European Organization for Research and Treatment of Cancer Trial 10853. J. Clin. Oncol. J. Am. Soc. Clin. Oncol. 19, 2263–2271 (2001).
doi: 10.1200/JCO.2001.19.8.2263
Baghban, R. et al. Tumor microenvironment complexity and therapeutic implications at a glance. Cell Commun. Signal. 18, 59 (2020).
pubmed: 32264958
pmcid: 7140346
doi: 10.1186/s12964-020-0530-4
Nelson, A. C., Machado, H. L. & Schwertfeger, K. L. Breaking through to the other side: microenvironment contributions to DCIS initiation and progression. J. Mammary Gland Biol. Neoplasia 23, 207–221 (2018).
pubmed: 30168075
pmcid: 6237657
doi: 10.1007/s10911-018-9409-z
Soto, A. M. & Sonnenschein, C. The tissue organization field theory of cancer: A testable replacement for the somatic mutation theory. BioEssays N. Rev. Mol. Cell. Dev. Biol. 33, 332–340 (2011).
Cooper, G. M. The Development and Causes of Cancer. Cell Mol. Approach 2nd Ed. (2000).
Gorringe, K. L. & Fox, S. B. Ductal carcinoma in situ biology, biomarkers, and diagnosis. Front. Oncol. 7, 248 (2017).
pubmed: 29109942
pmcid: 5660056
doi: 10.3389/fonc.2017.00248
Chapman, J.-A. W. et al. Ductal carcinoma in situ of the breast (DCIS) with heterogeneity of nuclear grade: prognostic effects of quantitative nuclear assessment. BMC Cancer 7, 174 (2007).
pubmed: 17845726
pmcid: 2001197
doi: 10.1186/1471-2407-7-174
Zink, D., Fischer, A. H. & Nickerson, J. A. Nuclear structure in cancer cells. Nat. Rev. Cancer 4, 677–687 (2004).
pubmed: 15343274
doi: 10.1038/nrc1430
Miller, N. A. et al. In situ duct carcinoma of the breast: clinical and histopathologic factors and association with recurrent carcinoma. Breast J. 7, 292–302 (2001).
pubmed: 11906438
doi: 10.1046/j.1524-4741.2001.99124.x
Tozbikian, G. et al. Atypical ductal hyperplasia bordering on ductal carcinoma in situ: interobserver variability and outcomes in 105 cases. Int. J. Surg. Pathol. 25, 100–107 (2017).
pubmed: 27481892
doi: 10.1177/1066896916662154
Pinder, S. E. & Ellis, I. O. The diagnosis and management of pre-invasive breast disease: Ductal carcinoma in situ (DCIS) and atypical ductal hyperplasia (ADH) – current definitions and classification. Breast Cancer Res. 5, 254 (2003).
pubmed: 12927035
pmcid: 314427
doi: 10.1186/bcr623
Rebbeck, C. A. et al. Gene expression signatures of individual ductal carcinoma in situ lesions identify processes and biomarkers associated with progression towards invasive ductal carcinoma. Nat. Commun. 13, 3399 (2022).
pubmed: 35697697
pmcid: 9192778
doi: 10.1038/s41467-022-30573-4
Bhat-Nakshatri, P. et al. A single-cell atlas of the healthy breast tissues reveals clinically relevant clusters of breast epithelial cells. Cell Rep. Med. 2, 100219 (2021).
pubmed: 33763657
pmcid: 7974552
doi: 10.1016/j.xcrm.2021.100219
Afghahi, A. et al. Chromosomal copy number alterations for associations of ductal carcinoma in situ with invasive breast cancer. Breast Cancer Res. 17, 108 (2015).
pubmed: 26265211
pmcid: 4534146
doi: 10.1186/s13058-015-0623-y
Casasent, A. K. et al. Multiclonal invasion in breast tumors identified by topographic single cell sequencing. Cell 172, 205–217.e12 (2018).
pubmed: 29307488
pmcid: 5766405
doi: 10.1016/j.cell.2017.12.007
Kim, S. Y. et al. Genomic differences between pure ductal carcinoma in situ and synchronous ductal carcinoma in situ with invasive breast cancer. Oncotarget 6, 7597–7607 (2015).
pubmed: 25831047
pmcid: 4480702
doi: 10.18632/oncotarget.3162
Newburger, D. E. et al. Genome evolution during progression to breast cancer. Genome Res. 23, 1097–1108 (2013).
pubmed: 23568837
pmcid: 3698503
doi: 10.1101/gr.151670.112
Xia, C., Fan, J., Emanuel, G., Hao, J. & Zhuang, X. Spatial transcriptome profiling by MERFISH reveals subcellular RNA compartmentalization and cell cycle-dependent gene expression. Proc. Natl. Acad. Sci. USA 116, 19490–19499 (2019).
pubmed: 31501331
pmcid: 6765259
doi: 10.1073/pnas.1912459116
Eng, C.-H. L. et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+. Nature 568, 235–239 (2019).
pubmed: 30911168
pmcid: 6544023
doi: 10.1038/s41586-019-1049-y
Wang, X. et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361, eaat5691 (2018).
pubmed: 29930089
pmcid: 6339868
doi: 10.1126/science.aat5691
Rodriques, S. G. et al. Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution. Science https://doi.org/10.1126/science.aaw1219 (2019).
Risom, T. et al. Transition to invasive breast cancer is associated with progressive changes in the structure and composition of tumor stroma. Cell 185, 299–310.e18 (2022).
pubmed: 35063072
pmcid: 8792442
doi: 10.1016/j.cell.2021.12.023
Uhler, C. & Shivashankar, G. V. Nuclear mechanopathology and cancer diagnosis. Trends Cancer 4, 320–331 (2018).
pubmed: 29606315
doi: 10.1016/j.trecan.2018.02.009
Talwar, S., Kumar, A., Rao, M., Menon, G. I. & Shivashankar, G. V. Correlated spatio-temporal fluctuations in chromatin compaction states characterize stem cells. Biophys. J. 104, 553–564 (2013).
pubmed: 23442906
pmcid: 3566460
doi: 10.1016/j.bpj.2012.12.033
Galati, A., Micheli, E. & Cacchione, S. Chromatin structure in telomere dynamics. Front. Oncol. 3, 46 (2013).
pubmed: 23471416
pmcid: 3590461
doi: 10.3389/fonc.2013.00046
Murga, M. et al. Global chromatin compaction limits the strength of the DNA damage response. J. Cell Biol. 178, 1101–1108 (2007).
pubmed: 17893239
pmcid: 2064646
doi: 10.1083/jcb.200704140
Lanctôt, C., Cheutin, T., Cremer, M., Cavalli, G. & Cremer, T. Dynamic genome architecture in the nuclear space: regulation of gene expression in three dimensions. Nat. Rev. Genet. 8, 104–115 (2007).
pubmed: 17230197
doi: 10.1038/nrg2041
Dekker, J. & Mirny, L. The 3D genome as moderator of chromosomal communication. Cell 164, 1110–1121 (2016).
pubmed: 26967279
pmcid: 4788811
doi: 10.1016/j.cell.2016.02.007
Venkatachalapathy, S., Jokhun, D. S. & Shivashankar, G. V. Multivariate analysis reveals activation-primed fibroblast geometric states in engineered 3D tumor microenvironments. Mol. Biol. Cell 31, 803–812 (2020).
pubmed: 32023167
pmcid: 7185960
doi: 10.1091/mbc.E19-08-0420
Venkatachalapathy, S., Jokhun, D. S., Andhari, M. & Shivashankar, G. V. Single cell imaging-based chromatin biomarkers for tumor progression. Sci. Rep. 11, 23041 (2021).
pubmed: 34845273
pmcid: 8630115
doi: 10.1038/s41598-021-02441-6
Kobayashi, H., Cheveralls, K. C., Leonetti, M. D. & Royer, L. A. Self-supervised deep learning encodes high-resolution features of protein subcellular localization. Nat. Methods 19, 995–1003 (2022).
pubmed: 35879608
pmcid: 9349041
doi: 10.1038/s41592-022-01541-z
Weigert, M., Schmidt, U., Haase, R., Sugawara, K. & Myers, G. Star-convex polyhedra for 3D object detection and segmentation in microscopy. In 2020 IEEE Winter Conference on Applications of Computer Vision (WACV) 3655–3662 (IEEE, 2020).
Goodfellow, I., Bengio, Y. & Courville, A. Deep Learning (MIT Press, 2016).
Zhang, X., Wang, X., Shivashankar, G. V. & Uhler, C. Graph-based autoencoder integrates spatial transcriptomics with chromatin images and identifies joint biomarkers for Alzheimer’s disease. Nat. Commun. 13, 7480 (2022).
pubmed: 36463283
pmcid: 9719477
doi: 10.1038/s41467-022-35233-1
Mah, L.-J., El-Osta, A. & Karagiannis, T. C. H2AX: a sensitive molecular marker of DNA damage and repair. Leukemia 24, 679–686 (2010).
pubmed: 20130602
doi: 10.1038/leu.2010.6
Wolf, F. A. et al. PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Genome Biol. 20, 59 (2019).
pubmed: 30890159
pmcid: 6425583
doi: 10.1186/s13059-019-1663-x
Haghverdi, L., Büttner, M., Wolf, F. A., Buettner, F. & Theis, F. J. Diffusion pseudotime robustly reconstructs lineage branching. Nat. Methods 13, 845–848 (2016).
pubmed: 27571553
doi: 10.1038/nmeth.3971
Reis-Filho, J. S. & Lakhani, S. R. The diagnosis and management of pre-invasive breast disease Genetic alterations in pre-invasive lesions. Breast Cancer Res. 5, 313 (2003).
pubmed: 14580249
pmcid: 314410
doi: 10.1186/bcr650
Steinman, S., Wang, J., Bourne, P., Yang, Q. & Tang, P. Expression of cytokeratin markers, ER-alpha, PR, HER-2/neu, and EGFR in pure ductal carcinoma in situ (DCIS) and DCIS with co-existing invasive ductal carcinoma (IDC) of the breast. Ann. Clin. Lab. Sci. 37, 127–134 (2007).
pubmed: 17522367
Tan, P. H., Goh, B. B., Chiang, G. & Bay, B. H. Correlation of nuclear morphometry with pathologic parameters in ductal carcinoma in situ of the breast. Mod. Pathol. 14, 937–941 (2001).
pubmed: 11598161
doi: 10.1038/modpathol.3880415
D’Urso, M. & Kurniawan, N. A. Mechanical and physical regulation of fibroblast–myofibroblast transition: from cellular mechanoresponse to tissue pathology. Front. Bioeng. Biotechnol. 8, 609653 (2020).
pubmed: 33425874
pmcid: 7793682
doi: 10.3389/fbioe.2020.609653
Gupta, S., Marcel, N., Sarin, A. & Shivashankar, G. V. Role of actin dependent nuclear deformation in regulating early gene expression. PLOS ONE 7, e53031 (2012).
pubmed: 23285252
pmcid: 3532443
doi: 10.1371/journal.pone.0053031
Goodman, A. et al. 2018 Data Science Bowl. https://kaggle.com/competitions/data-science-bowl-2018 (2018).
Schmidt, U., Weigert, M., Broaddus, C. & Myers, G. Cell detection with star-convex polygons. In Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 (eds. Frangi, A. F., Schnabel, J. A., Davatzikos, C., Alberola-López, C. & Fichtinger, G.) 265–273 (Springer International Publishing, 2018).
Otsu, N. A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9, 62–66 (1979).
doi: 10.1109/TSMC.1979.4310076
Maas, A. L., Hannun, A. Y. & Ng, A. Y. Rectifier nonlinearities improve neural network acoustic models. In Proceedings of the 30th International Conference on Machine Learning, (Atlanta, Georgia, USA, 2013).
Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
McInnes, L., Healy, J., Saul, N. & Großberger, L. UMAP: Uniform manifold approximation and projection. J. Open Source Softw. 3, 861 (2018).
doi: 10.21105/joss.00861
Paszke, A. et al. PyTorch: an imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems vol. 32 (Curran Associates, Inc., 2019).
Alzubaidi, L. et al. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J. Big Data 8, 53 (2021).
pubmed: 33816053
pmcid: 8010506
doi: 10.1186/s40537-021-00444-8
Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).
pubmed: 32015543
pmcid: 7056644
doi: 10.1038/s41592-019-0686-2
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Methodol. 57, 289–300 (1995).
doi: 10.1111/j.2517-6161.1995.tb02031.x
The pandas development team. pandas-dev/pandas: Pandas. https://doi.org/10.5281/zenodo.7344967 (2022).
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
pubmed: 29409532
pmcid: 5802054
doi: 10.1186/s13059-017-1382-0
Zhang, X., et al Unsupervised representation learning of chromatin images identifies changes in cell state and tissue organization in DCIS. uhlerlab/DCISprogression. https://doi.org/10.5281/zenodo.11247538 (2024).