Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 653

The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.

Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis

Open access
Fu, Yu, Jung, Alexander W., Torne, Ramon Viñas, Gonzalez, Santiago, Vöhringer, Harald, Shmatko, Artem, Yates, Lucy R., Jimenez-Linan, Mercedes, Moore, Luiza, Gerstung, Moritz·2020·Nature Cancer
DOI 10.1038/s43018-020-0085-8

Published version of a preprint

Open this paper in CoChat — free account

Abstract

We use deep transfer learning to quantify histopathological patterns across 17,355 hematoxylin and eosin-stained histopathology slide images from 28 cancer types and correlate these with matched genomic, transcriptomic and survival data. This approach accurately classifies cancer types and provides spatially resolved tumor and normal tissue distinction. Automatically learned computational histopathological features correlate with a large range of recurrent genetic aberrations across cancer types. This includes whole-genome duplications, which display universal features across cancer types, individual chromosomal aneuploidies, focal amplifications and deletions, as well as driver gene mutations. There are widespread associations between bulk gene expression levels and histopathology, which reflect tumor composition and enable the localization of transcriptomically defined tumor-infiltrating lymphocytes. Computational histopathology augments prognosis based on histopathological subtyping and grading, and highlights prognostically relevant areas such as necrosis or lymphocytic aggregates. These findings show the remarkable potential of computer vision in characterizing the molecular basis of tumor histopathology. Two papers by Kather and colleagues and Gerstung and colleagues develop workflows to predict a wide range of molecular alterations from pan-cancer digital pathology slides.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s43018-020-0085-8

Cites

22 of 22 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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

Research works printed but not linked: 48

  1. Lindeman, N. I. et al. Molecular testing guideline for selection of lung cancer patients for EGFR and ALK tyrosine kinase inhibitors: guideline from the College of American Pathologists, International Association for the Study of Lung Cancer, and Association for Molecular Pathology. J. Thorac. Oncol. 8, 823–859 (2013). · DOI · Paper page
  2. Woodman, S. E., Lazar, A. J., Aldape, K. D. & Davies, M. A. New strategies in melanoma: molecular testing in advanced disease. Clin. Cancer Res. 18, 1195–1200 (2012). · DOI
  3. Russnes, H. G., Lingjærde, O. C., Børresen-Dale, A.-L. & Caldas, C. Breast cancer molecular stratification: from intrinsic subtypes to integrative clusters. Am. J. Pathol. 187, 2152–2162 (2017). · DOI · Paper page
  4. Dienstmann, R. et al. Consensus molecular subtypes and the evolution of precision medicine in colorectal cancer. Nat. Rev. Cancer 17, 79–92 (2017). · DOI · Paper page
  5. Cancer Genome Atlas Research Network. The molecular taxonomy of primary prostate cancer. Cell 163, 1011–1025 (2015). · DOI · Paper page
  6. Campanella, G. et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat. Med. 25, 1301–1309 (2019). · DOI · Paper page
  7. Bera, K., Schalper, K. A., Rimm, D. L., Velcheti, V. & Madabhushi, A. Artificial intelligence in digital pathology—new tools for diagnosis and precision oncology. Nat. Rev. Clin. Oncol. 16, 703–715 (2019). · DOI · Paper page
  8. Hegde, N. et al. Similar image search for histopathology: SMILY. NPJ Digit. Med. 2, 56 (2019). · DOI
  9. Saltz, J. et al. Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images. Cell Rep. 23, 181–193.e7 (2018). · DOI · Paper page
  10. Shia, J. et al. Morphological characterization of colorectal cancers in The Cancer Genome Atlas reveals distinct morphology–molecular associations: clinical and biological implications. Mod. Pathol. 30, 599–609 (2017). · DOI · Paper page
  11. Schaumberg, A. J., Rubin, M. A. & Fuchs, T. J. H&E-stained whole slide image deep learning predicts SPOP mutation state in prostate cancer. Preprint at bioRxiv https://doi.org/10.1101/064279 (2018). · DOI
  12. Tsou, P. & Wu, C.-J. Mapping driver mutations to histopathological subtypes in papillary thyroid carcinoma: applying a deep convolutional neural network. J. Clin. Med. Res. 8, 1675 (2019).
  13. Chang, P. et al. Deep-learning convolutional neural networks accurately classify genetic mutations in gliomas. AJNR Am. J. Neuroradiol. 39, 1201–1207 (2018). · DOI · Paper page
  14. Kather, J. N., Heij, L. R., Grabsch, H. I. & Kooreman, L. F. S. Pan-cancer image-based detection of clinically actionable genetic alterations. Nat. Cancer https://doi.org/10.1038/s43018-020-0087-6 (2020). · DOI · Paper page
  15. Kather, J. N., Schulte, J., Grabsch, H. I., Loeffler, C. & Muti, H. S. Deep learning detects virus presence in cancer histology. Preprint at bioRxiv https://doi.org/10.1101/690206 (2019). · DOI
  16. Yu, K.-H. et al. Association of omics features with histopathology patterns in lung adenocarcinoma. Cell Syst. 5, 620–627.e3 (2017). · DOI
  17. Schmauch, B., Romagnoni, A., Pronier, E. & Saillard, C. Transcriptomic learning for digital pathology. Preprint at bioRxiv https://doi.org/10.1101/760173 (2019). · DOI
  18. Cheng, J. et al. Integrative analysis of histopathological images and genomic data predicts clear cell renal cell carcinoma prognosis. Cancer Res. 77, e91–e100 (2017). · DOI
  19. Cooper, L. A. et al. PanCancer insights from The Cancer Genome Atlas: the pathologist’s perspective. J. Pathol. 244, 512–524 (2018). · DOI
  20. Gerstung, M. et al. The evolutionary history of 2,658 cancers. Nature 578, 122–128 (2020). · DOI · Paper page
  21. Carpenter, A. E. et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100 (2006). · DOI · Paper page
  22. Cancer Genome Atlas Research Network. Comprehensive and integrated genomic characterization of adult soft tissue sarcomas. Cell 171, 950–965.e28 (2017). · DOI · Paper page
  23. Zack, T. I. et al. Pan-cancer patterns of somatic copy number alteration. Nat. Genet. 45, 1134–1140 (2013). · DOI · Paper page
  24. Burger, P. C. et al. Small cell architecture—a histological equivalent of EGFR amplification in glioblastoma multiforme? J. Neuropathol. Exp. Neurol. 60, 1099–1104 (2001). · DOI
  25. Kebebew, E. et al. The prevalence and prognostic value of BRAF mutation in thyroid cancer. Ann. Surg. 246, 466–470 (2007). · DOI · Paper page
  26. O’Hara, A. J. & Bell, D. W. The genomics and genetics of endometrial cancer. Adv. Genomics Genet. 2012, 33–47 (2012).
  27. Thorsson, V. et al. The immune landscape of cancer. Immunity 48, 812–830.e14 (2018). · DOI · Paper page
  28. Nawaz, S., Heindl, A., Koelble, K. & Yuan, Y. Beyond immune density: critical role of spatial heterogeneity in estrogen receptor-negative breast cancer. Mod. Pathol. 28, 766–777 (2015). · DOI
  29. Pollheimer, M. J. et al. Tumor necrosis is a new promising prognostic factor in colorectal cancer. Hum. Pathol. 41, 1749–1757 (2010). · DOI
  30. Jögi, A., Vaapil, M., Johansson, M. & Påhlman, S. Cancer cell differentiation heterogeneity and aggressive behavior in solid tumors. Ups. J. Med. Sci. 117, 217–224 (2012). · DOI
  31. Gooden, M. J. M., de Bock, G. H., Leffers, N., Daemen, T. & Nijman, H. W. The prognostic influence of tumour-infiltrating lymphocytes in cancer: a systematic review with meta-analysis. Br. J. Cancer 105, 93–103 (2011). · DOI · Paper page
  32. Bayraktar, O. A., Bartels, T., Polioudakis, D. & Holmqvist, S. Single-cell in situ transcriptomic map of astrocyte cortical layer diversity. Preprint at bioRxiv https://doi.org/10.1101/432104 (2018). · DOI
  33. Elston, R. C. On Fisher’s method of combining P-values. Biom. J. 33, 339–345 (1991). · DOI
  34. Mann, H. B. & Whitney, D. R. On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat. 18, 50–60 (1947). · DOI · Paper page
  35. Best, D. J. & Roberts, D. E. Algorithm AS 89: the upper tail probabilities of Spearman’s rho. J. R. Stat. Soc. C Appl. Stat. 24, 377–379 (1975).
  36. Harrell, F. E. Jr, Califf, R. M., Pryor, D. B., Lee, K. L. & Rosati, R. A. Evaluating the yield of medical tests. J. Am. Med. Assoc. 247, 2543–2546 (1982). · DOI · Paper page
  37. Grossman, R. L. et al. Toward a shared vision for cancer genomic data. N. Engl. J. Med. 375, 1109–1112 (2016). · DOI · Paper page
  38. Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010). · DOI · Paper page
  39. LeDell, E., Petersen, M. & van der Laan, M. Computationally efficient confidence intervals for cross-validated area under the ROC curve estimates. Electron. J. Stat. 9, 1583–1607 (2015). · DOI · Paper page
  40. Fieller, E. C., Hartley, H. O. & Pearson, E. S. Tests for rank correlation coefficients. I. Biometrika 44, 470–481 (1957). · DOI · Paper page
  41. Martincorena, I. et al. Universal patterns of selection in cancer and somatic tissues. Cell 171, 1029–1041.e21 (2017). · DOI · Paper page
  42. Van Loo, P. et al. Allele-specific copy number analysis of tumors. Proc. Natl Acad. Sci. USA 107, 16910–16915 (2010). · DOI · Paper page
  43. Taylor, A. M. et al. Genomic and functional approaches to understanding cancer aneuploidy. Cancer Cell 33, 676–689.e3 (2018). · DOI · Paper page
  44. Croft, D. et al. The Reactome Pathway Knowledgebase. Nucleic Acids Res. 42, D472–D477 (2014). · DOI · Paper page
  45. Cox, D. R. in Breakthroughs in Statistics (eds Kotz, S. & Johnson, N. L.) 527–541 (Springer, 1992). · DOI · Paper page
  46. Simon, N., Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for Cox’s proportional hazards model via coordinate descent. J. Stat. Softw. 39, 1–13 (2011). · DOI · Paper page
  47. Singer, Y. & Duchi, J. C. Efficient learning using forward-backward splitting. Adv. Neural Inf. Proc. Syst. 22, 495–503 (2009).
  48. Dentro, S. C. et al. Portraits of genetic intra-tumour heterogeneity and subclonal selection across cancer types. Preprint at bioRxiv https://doi.org/10.1101/312041 (2018). · DOI

Cited by

See all 653

Metadata

Authors
Fu, Y., Jung, A. W., Torne, R. V., Gonzalez, S., Vöhringer, H., Shmatko, A., Yates, L. R., Jimenez-Linan, M., Moore, L., & Gerstung, M.
Venue
Nature Cancer
Publisher
Springer Science and Business Media LLC
Published
2020-07-27
Volume
1
Issue
8
Pages
800-810
ISSN
2662-1347 (ISSN-L)
DOI
10.1038/s43018-020-0085-8
OpenAlex
W3043835773
Cited by
653
References
22
FWCI
34.76
Type
journal article
Language
en
Open access
green
Licence
Funders
  • Novo Nordisk Fonden (NNF17OC0027594)
  • Cancer Research UK (C20/A20917)
  • Wellcome Trust (214584/Z/18/Z)

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.