[go: up one dir, main page]
Skip to main content

Advertisement

SpringerLink
Log in

Predicting breast cancer molecular subtypes from H &E-stained histopathological images using a spatial-transcriptomics-based patch filter

  • 1235: Supporting Medicine and Healthcare with Multimedia Tools
  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The molecular subtype of breast cancer plays an important role in the prognosis of patients and guides physicians to develop scientific therapeutic regimes. In clinical practice, physicians classify molecular subtypes of breast cancer with immunohistochemistry(IHC) technology, which requires a long cycle for diagnosis, resulting in a delay in effective treatment of patients with breast cancer. To improve the diagnostic rate, we proposed a machine learning method that predicted molecular subtypes of breast cancer from H&E-stained histopathological images. Although some molecular subtype prediction methods have been suggested, they are noisy and lack clinical evidence. To address these issues, we introduced a patch filter-based molecular subtype prediction (PFMSP) method using spatial transcriptomics data, training a patch filter with spatial transcriptomics data first, and then the trained filter was used to select valuable patches for molecular subtype prediction in other H&E-stained histopathological images. These valuable patches contained one or more genes expressed of ESR1, ESR2, PGR, and ERBB2. We evaluated the performance of our method on the spatial transcriptomics(ST) dataset and the TCGA-BRCA dataset, and the patches filtered by the patch filter achieved accuracies of 80% and 73.91% in predicting molecular subtypes on the ST and TCGA-BRCA datasets, respectively. Experimental results showed that the use of the trained patch filter to filter patches was beneficial to improving precision in predicting molecular subtypes of breast cancer.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Availability of Data and Materials

The manuscript contains third party material and obtained permissions are available on request by the Publisher. Everyone can download free and unlimited from the link. Breast cancer spatial transcriptome data are from https://data.mendeley.com/datasets/29ntw7sh4r/2. TCGA dataset are from https://portal.gdc.cancer.gov/projects/TCGA-BRCA and https://xenabrowser.net/datapages/.

Code Availability

Not applicable.

References

  1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F (2021) Global cancer statistics 2020: globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71(3):209–249

  2. McGranahan N, Swanton C (2017) Clonal heterogeneity and tumor evolution: past, present, and the future. Cell 168(4):613–628

    Article  Google Scholar 

  3. Vergara HM, Pape C, Meechan KI, Zinchenko V, Genoud C, Wanner AA, Mutemi KN, Titze B, Templin RM, Bertucci PY et al (2021) Whole-body integration of gene expression and single-cell morphology. Cell 184(18):4819–4837

    Article  Google Scholar 

  4. Geirsdottir L, David E, Keren-Shaul H, Weiner A, Bohlen SC, Neuber J, Balic A, Giladi A, Sheban F, Dutertre C-A et al (2019) Cross-species single-cell analysis reveals divergence of the primate microglia program. Cell 179(7):1609–1622

    Article  Google Scholar 

  5. Buonomo OC, Grasso A, Pistolese CA, Anemona L, Portarena I, Meucci R, Morando L, Deiana C, Materazzo M, Vanni G (2020) Evaluation of concordance between histopathological, radiological and biomolecular variables in breast cancer neoadjuvant treatment. Anticancer Res 40(1):281–286

    Article  Google Scholar 

  6. Bakovic V, Höglund A, Martin Cerezo ML, Henriksen R, Wright D (2022) Genomic and gene expression associations to morphology of a sexual ornament in the chicken. G3 Genes| Genomes| Genetics

  7. Allugunti VR (2022) Breast cancer detection based on thermographic images using machine learning and deep learning algorithms. International Journal of Engineering in Computer Science. 4(1):49–56

    Article  Google Scholar 

  8. Alsaqqa AH, Alkahlout MA, Abu-Naser SS (2022) Using deep learning to classify different types of vitamin. International Journal of Academic Engineering Research (IJAER). 6(1):1–6

    Google Scholar 

  9. Gupta A, Gupta S, Katarya R et al (2021) Instacovnet-19: a deep learning classification model for the detection of covid-19 patients using chest x-ray. Appl Soft Comput 99:106859

  10. Chaunzwa TL, Hosny A, Xu Y, Shafer A, Diao N, Lanuti M, Christiani DC, Mak RH, Aerts HJ (2021) Deep learning classification of lung cancer histology using ct images. Sci Rep 11(1):1–12

    Article  Google Scholar 

  11. Goceri E (2021) Deep learning based classification of facial dermatological disorders. Comput Biol Med 128:104118

    Article  Google Scholar 

  12. Wang X, Chen Y, Gao Y, Zhang H, Guan Z, Dong Z, Zheng Y, Jiang J, Yang H, Wang L et al (2021) Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning. Nat Commun 12(1):1–13

    Google Scholar 

  13. Shao W, Banh L, Kunder CA, Fan RE, Soerensen SJ, Wang JB, Teslovich NC, Madhuripan N, Jawahar A, Ghanouni P et al (2021) Prosregnet: a deep learning framework for registration of mri and histopathology images of the prostate. Med Image Anal 68:101919

  14. Lal S, Das D, Alabhya K, Kanfade A, Kumar A, Kini J (2021) Nucleisegnet: robust deep learning architecture for the nuclei segmentation of liver cancer histopathology images. Comput Biol Med 128:104075

    Article  Google Scholar 

  15. Su A, Lee H, Tan X, Suarez CJ, Andor N, Nguyen Q, Ji HP (2022) A deep learning model for molecular label transfer that enables cancer cell identification from histopathology images. NPJ Precis Oncol 6(1):1–11

    Google Scholar 

  16. Mathew T, Ajith B, Kini JR, Rajan J (2022) Deep learning-based automated mitosis detection in histopathology images for breast cancer grading. International Journal of Imaging Systems and Technology

  17. Nguyen C, Asad Z, Deng R, Huo Y (2022) Evaluating transformer-based semantic segmentation networks for pathological image segmentation. In: Medical imaging 2022: image processing, vol 12032, pp 942–947. SPIE

  18. Tsai P-C, Lee T-H, Kuo K-C, Su F-Y, Lee T-LM, Marostica E, Ugai T, Zhao M, Lau MC, Väyrynen JP et al (2023) Histopathology images predict multi-omics aberrations and prognoses in colorectal cancer patients. Nat Commun 14(1):2102

    Article  Google Scholar 

  19. Ding K, Zhou M, Wang H, Gevaert O, Metaxas D, Zhang S (2023) A large-scale synthetic pathological dataset for deep learning-enabled segmentation of breast cancer. Scientific Data. 10(1):231

    Article  Google Scholar 

  20. Jia Y, Liu J, Chen L, Zhao T, Wang Y (2024) Thitogene: a deep learning method for predicting spatial transcriptomics from histological images. Brief Bioinform 25(1):464

    Article  Google Scholar 

  21. Sun K, Zheng Y, Yang X, Jia W (2024) A novel transformer-based aggregation model for predicting gene mutations in lung adenocarcinoma. Med Biol Eng Comput 62(5):1427–1440

    Article  Google Scholar 

  22. Dadhania V, Gonzalez D, Yousif M, Cheng J, Morgan TM, Spratt DE, Reichert ZR, Mannan R, Wang X, Chinnaiyan A et al (2022) Leveraging artificial intelligence to predict erg gene fusion status in prostate cancer. BMC Cancer 22(1):1–9

    Article  Google Scholar 

  23. Wang Y, Kartasalo K, Weitz P, Acs B, Valkonen M, Larsson C, Ruusuvuori P, Hartman J, Rantalainen M (2021) Predicting molecular phenotypes from histopathology images: a transcriptome-wide expression-morphology analysis in breast cancer. Can Res 81(19):5115–5126

  24. Hong R, Liu W, DeLair D, Razavian N, Fenyö D (2021) Predicting endometrial cancer subtypes and molecular features from histopathology images using multi-resolution deep learning models. Cell Reports Medicine. 2(9):100400

    Article  Google Scholar 

  25. Kather JN, Heij LR, Grabsch HI, Loeffler C, Echle A, Muti HS, Krause J, Niehues JM, Sommer KA, Bankhead P et al (2020) Pan-cancer image-based detection of clinically actionable genetic alterations. Nature Cancer. 1(8):789–799

    Article  Google Scholar 

  26. Wang X, Zou C, Zhang Y, Li X, Wang C, Ke F, Chen J, Wang W, Wang D, Xu X et al (2021) Prediction of brca gene mutation in breast cancer based on deep learning and histopathology images. Front Genet 12:1147

    Google Scholar 

  27. Qu H, Zhou M, Yan Z, Wang H, Rustgi VK, Zhang S, Gevaert O, Metaxas DN (2021) Genetic mutation and biological pathway prediction based on whole slide images in breast carcinoma using deep learning. NPJ Precis Oncol 5(1):1–11

    Google Scholar 

  28. Coudray N, Ocampo PS, Sakellaropoulos T, Narula N, Snuderl M, Fenyö D, Moreira AL, Razavian N, Tsirigos A (2018) Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat Med 24(10):1559–1567

    Article  Google Scholar 

  29. Rawat RR, Ortega I, Roy P, Sha F, Shibata D, Ruderman D, Agus DB (2020) Deep learned tissue “fingerprints’’ classify breast cancers by er/pr/her2 status from h &e images. Sci Rep 10(1):1–13

    Article  Google Scholar 

  30. Diao JA, Wang JK, Chui WF, Mountain V, Gullapally SC, Srinivasan R, Mitchell RN, Glass B, Hoffman S, Rao SK et al (2021) Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes. Nat Commun 12(1):1–15

    Article  Google Scholar 

  31. Woerl A-C, Eckstein M, Geiger J, Wagner DC, Daher T, Stenzel P, Fernandez A, Hartmann A, Wand M, Roth W et al (2020) Deep learning predicts molecular subtype of muscle-invasive bladder cancer from conventional histopathological slides. Eur Urol 78(2):256–264

    Article  Google Scholar 

  32. Phan NN, Huang C-C, Chuang EY (2020) Predicting molecular subtypes of breast cancer using pathological images by deep convolutional neural network from public dataset. bioRxiv

  33. Jaber MI, Song B, Taylor C, Vaske CJ, Benz SC, Rabizadeh S, Soon-Shiong P, Szeto CW (2020) A deep learning image-based intrinsic molecular subtype classifier of breast tumors reveals tumor heterogeneity that may affect survival. Breast Cancer Res 22(1):1–10

    Article  Google Scholar 

  34. Sirinukunwattana K, Domingo E, Richman SD, Redmond KL, Blake A, Verrill C, Leedham SJ, Chatzipli A, Hardy C, Whalley CM et al (2021) Image-based consensus molecular subtype (imcms) classification of colorectal cancer using deep learning. Gut 70(3):544–554

    Article  Google Scholar 

  35. Liu T, Huang J, Liao T, Pu R, Liu S, Peng Y (2022) A hybrid deep learning model for predicting molecular subtypes of human breast cancer using multimodal data. Irbm. 43(1):62–74

    Article  Google Scholar 

  36. Shao Z, Bian H, Chen Y, Wang Y, Zhang J, Ji X et al (2021) Transmil: transformer based correlated multiple instance learning for whole slide image classification. Adv Neural Inf Process Syst 34:2136–2147

  37. Li B, Li Y, Eliceiri KW (2021) Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14318–14328

  38. Lu MY, Williamson DF, Chen TY, Chen RJ, Barbieri M, Mahmood F (2021) Data-efficient and weakly supervised computational pathology on whole-slide images. Nat Biomed Eng 5(6):555–570

    Article  Google Scholar 

  39. He B, Bergenstråhle L, Stenbeck L, Abid A, Andersson A, Borg Å, Maaskola J, Lundeberg J, Zou J (2020) Integrating spatial gene expression and breast tumour morphology via deep learning. Nat Biomed Eng 4(8):827–834

    Article  Google Scholar 

Download references

Funding

This work was funded by the Major Projects of Technological Innovation in Hubei Province (2019AEA170, 2019ACA161), the Translational Medicine and Interdisciplinary Research Joint Fund of Zhongnan Hospital of Wuhan University (ZNJC202226).

Author information

Authors and Affiliations

  1. Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Bayi Road, Wuhan, 430072, Hubei, China

    Yuqi Chen, Juan Liu, Lang Wang & Peng Jiang

  2. AI Center for Pathololgical Diagnosis, Landing Intelligence Med. Co., Ltd, Wuhan University, Wuhan, 430072, Hubei, China

    Baochuan Pang & Dehua Cao

Authors
  1. Yuqi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Juan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Peng Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Baochuan Pang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Dehua Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Yuqi Chen assumes primary responsibilities, encompassing the identification of issues, proposal of solutions, experimentation, and paper writing. Juan Liu oversees the paper’s quality and provides guidance. Concurrently, Lang Wang and Peng Jiang are responsible for enhancing the graphics, whereas Baochuan Pang and Dehua Cao have refined the language within the paper.

Corresponding author

Correspondence to Juan Liu.

Ethics declarations

Conflict of Interest/Competing Interests

The authors declare that they have no competing interests, or other interests that might be perceived to influence the results and/or discussion reported in this manuscript.

Ethics Approval

Not applicable.

Consent to Participate

Not applicable.

Consent for Publication

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, Y., Liu, J., Wang, L. et al. Predicting breast cancer molecular subtypes from H &E-stained histopathological images using a spatial-transcriptomics-based patch filter. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-20160-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11042-024-20160-8

Keywords

Search

Navigation