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Cancer biology as revealed by the research autopsy

Abstract

A research autopsy is a post-mortem medical procedure performed on a deceased individual with the primary goal of collecting tissue to support basic and translational research. This approach has increasingly been used to investigate the pathophysiological mechanisms of cancer evolution, metastasis and treatment resistance. In this Review, we discuss the rationale for the use of research autopsies in cancer research and provide an evidence-based discussion of the quality of post-mortem tissues compared with other types of biospecimens. We also discuss the advantages of using post-mortem tissues over other types of biospecimens, including the large amounts of tissue that can be obtained and the extent of multiregion sampling that is achievable, which is not otherwise possible in living patients. We highlight how the research autopsy has supported the identification of the clonal origins and modes of spread among metastases, the extent that selective pressures imposed by treatments cause bottlenecks leading to parallel and convergent tumour evolution, and the creation of rare tissue banks and patient-derived model systems. Finally, we comment on the future of the research autopsy as an integral component of precision medicine strategies.

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Fig. 1: Methods and rationale for multiregion sampling.
Fig. 2: Multiregion sampling to understand the evolutionary biology of cancer.
Fig. 3: Interpretation of evolutionary dynamics relative to the sampling method.
Fig. 4: Incorporation of research autopsies into biomarker-driven adaptive clinical trials.
Fig. 5: Integration of multimodal data to maximize understanding of lethal cancer.

References

  1. 1.

    Buja, L. M., Barth, R. F., Krueger, G. R., Brodsky, S. V. & Hunter, R. L. The importance of the autopsy in medicine: perspectives of pathology colleagues. Acad. Pathol. 6, 2374289519834041 (2019).

    PubMed  PubMed Central  Google Scholar 

  2. 2.

    Blokker, B. M. et al. Conventional autopsy versus minimally invasive autopsy with postmortem MRI, CT, and CT-guided biopsy: comparison of diagnostic performance. Radiology 289, 658–667 (2018).

    PubMed  Google Scholar 

  3. 3.

    Kretzschmar, H. Brain banking: opportunities, challenges and meaning for the future. Nat. Rev. Neurosci. 10, 70–78 (2009).

    CAS  PubMed  Google Scholar 

  4. 4.

    Hajdu, S. I. A note from history: the first printed case reports of cancer. Cancer 116, 2493–2498 (2010).

    PubMed  Google Scholar 

  5. 5.

    Mariette, C. et al. Consensus on the pathological definition and classification of poorly cohesive gastric carcinoma. Gastric Cancer 22, 1–9 (2019).

    CAS  PubMed  Google Scholar 

  6. 6.

    Ghosh, S. K. Giovanni Battista Morgagni (1682–1771): father of pathologic anatomy and pioneer of modern medicine. Anat. Sci. Int. 92, 305–312 (2017).

    PubMed  Google Scholar 

  7. 7.

    Paget, S. The distribution of secondary growths in cancer of the breast. Lancet 133, 571–573 (1889). A seminal large post-mortem study of patients with breast cancer that led to the seed and soil hypothesis of metastasis.

    Google Scholar 

  8. 8.

    Fidler, I. J. Metastasis: quantitative analysis of distribution and fate of tumor emboli labeled with 125I-5-iodo-2′-deoxyuridine. J. Natl. Cancer Inst. 45, 773–782 (1970). The first study to provide experimental support for the seed and soil hypothesis.

    CAS  PubMed  Google Scholar 

  9. 9.

    Fidler, I. J. & Kripke, M. L. Metastasis results from preexisting variant cells within a malignant tumor. Science 197, 893–895 (1977).

    CAS  PubMed  Google Scholar 

  10. 10.

    Minn, A. J. et al. Distinct organ-specific metastatic potential of individual breast cancer cells and primary tumors. J. Clin. Invest. 115, 44–55 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Minn, A. J. et al. Lung metastasis genes couple breast tumor size and metastatic spread. Proc. Natl Acad. Sci. USA 104, 6740–6745 (2007).

    CAS  PubMed  Google Scholar 

  12. 12.

    Minn, A. J. et al. Genes that mediate breast cancer metastasis to lung. Nature 436, 518–524 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Bos, P. D. et al. Genes that mediate breast cancer metastasis to the brain. Nature 459, 1005–1009 (2009). This study and Minn et al. (J. Clin. Invest., 2005), Minn et al. (Proc. Natl Acad. Sci. USA, 2007) and Minn et al. (Nature, 2005) are the first studies to illustrate the molecular mechanisms of organotropism of breast cancer metastasis.

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Rubin, M. A. et al. Rapid (‘warm’) autopsy study for procurement of metastatic prostate cancer. Clin. Cancer Res. 6, 1038–1045 (2000). The first published report describing the creation of a PDX from post-mortem tissue obtained from a patient with prostate cancer.

    CAS  PubMed  Google Scholar 

  15. 15.

    Morrissey, C. et al. Effects of androgen deprivation therapy and bisphosphonate treatment on bone in patients with metastatic castration-resistant prostate cancer: results from the University of Washington rapid autopsy series. J. Bone Miner. Res. 28, 333–340 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Iacobuzio-Donahue, C. A. et al. DPC4 gene status of the primary carcinoma correlates with patterns of failure in patients with pancreatic cancer. J. Clin. Oncol. 27, 1806–1813 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Liu, W. et al. Copy number analysis indicates monoclonal origin of lethal metastatic prostate cancer. Nat. Med. 15, 559–565 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Abbosh, C. et al. Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution. Nature 545, 446–451 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Gundem, G. et al. The evolutionary history of lethal metastatic prostate cancer. Nature 520, 353–357 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Savas, P. et al. The subclonal architecture of metastatic breast cancer: results from a prospective community-based rapid autopsy program “CASCADE”. PLOS Med. 13, e1002204 (2016).

    PubMed  PubMed Central  Google Scholar 

  21. 21.

    Razavi, P. et al. The genomic landscape of endocrine-resistant advanced breast cancers. Cancer Cell 34, 427–438.e6 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Yegnasubramanian, S. et al. DNA hypomethylation arises later in prostate cancer progression than CpG island hypermethylation and contributes to metastatic tumor heterogeneity. Cancer Res. 68, 8954–8967 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Yegnasubramanian, S. et al. Hypermethylation of CpG islands in primary and metastatic human prostate cancer. Cancer Res. 64, 1975–1986 (2004).

    CAS  PubMed  Google Scholar 

  24. 24.

    Pisapia, D. J. et al. Next-generation rapid autopsies enable tumor evolution tracking and generation of preclinical models. JCO Precis. Oncol. 2017, 1–13 (2017).

  25. 25.

    Wu, J. M. et al. Heterogeneity of breast cancer metastases: comparison of therapeutic target expression and promoter methylation between primary tumors and their multifocal metastases. Clin. Cancer Res. 14, 1938–1946 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Turajlic, S. et al. Tracking cancer evolution reveals constrained routes to metastases: TRACERx Renal. Cell 173, 581–594 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Vaught, J. Biobanking comes of age: the transition to biospecimen science. Annu. Rev. Pharmacol. Toxicol. 56, 211–228 (2016).

    CAS  PubMed  Google Scholar 

  28. 28.

    Carithers, L. J. et al. A novel approach to high-quality postmortem tissue procurement: The GTEx Project. Biopreserv. Biobank. 13, 311–319 (2015). Description of the GTEx project, which is designed to support understanding of the relationship between genomic variation and gene expression based on a large series of post-mortem-collected tissues.

    PubMed  PubMed Central  Google Scholar 

  29. 29.

    Farrell, M. M. & Levin, D. L. Brain death in the pediatric patient: historical, sociological, medical, religious, cultural, legal, and ethical considerations. Crit. Care Med. 21, 1951–1965 (1993).

    CAS  PubMed  Google Scholar 

  30. 30.

    De Georgia, M. A. History of brain death as death: 1968 to the present. J. Crit. Care 29, 673–678 (2014).

    PubMed  Google Scholar 

  31. 31.

    Jakušovaitė, I. et al. Determination of death: metaphysical and biomedical discourse. Med. 52, 205–210 (2016).

    Google Scholar 

  32. 32.

    Pozhitkov, A. E. & Noble, P. A. Gene expression in the twilight of death. BioEssays 39, 1700066 (2017).

    Google Scholar 

  33. 33.

    Grizzle, W. E., Otali, D., Sexton, K. C. & Atherton, D. S. Effects of cold ischemia on gene expression: a review and commentary. Biopreserv. Biobank. 14, 548–558 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Green, D. R. & Llambi, F. Cell death signaling. Cold Spring Harb. Perspect. Biol. 7, a006080 (2015).

    PubMed  PubMed Central  Google Scholar 

  35. 35.

    Shemie, S. D. & Gardiner, D. Circulatory arrest, brain arrest and death determination. Front. Cardiovasc. Med. 13, 15 (2018).

    Google Scholar 

  36. 36.

    Bate-Smith, E. C. & Bendall, J. R. Factors determining the time course of rigor mortis. J. Physiol. 110, 47–65 (1949).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Goldberg, D. et al. Changing metrics of organ procurement organization performance in order to increase organ donation rates in the United States. Am. J. Transplant. 17, 3183–3192 (2017).

    CAS  PubMed  Google Scholar 

  38. 38.

    Jimeno, A. et al. A direct pancreatic cancer xenograft model as a platform for cancer stem cell therapeutic development. Mol. Cancer Ther. 8, 310–314 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Tiriac, H. et al. Organoid profiling identifies common responders to chemotherapy in pancreatic cancer. Cancer Discov. 8, 1112–1129 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Pauli, C. et al. Personalized in vitro and in vivo cancer models to guide precision medicine. Cancer Discov. 7, 462–477 (2017).

    PubMed  PubMed Central  Google Scholar 

  41. 41.

    Aguirre, A. J. et al. Real-time genomic characterization of advanced pancreatic cancer to enable precision medicine. Cancer Discov. 8, 1096–1111 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Li, J. Z. et al. Sample matching by inferred agonal stress in gene expression analyses of the brain. BMC Genomics 8, 336 (2007).

    PubMed  PubMed Central  Google Scholar 

  43. 43.

    Lee, D. C. et al. A lactate-induced response to hypoxia. Cell 161, 595–609 (2015).

    CAS  PubMed  Google Scholar 

  44. 44.

    Xu, Y. et al. Glycolysis determines dichotomous regulation of T cell subsets in hypoxia. J. Clin. Invest. 126, 2678–2688 (2016).

    PubMed  PubMed Central  Google Scholar 

  45. 45.

    Lawless, S. J. et al. Glucose represses dendritic cell-induced T cell responses. Nat. Commun. 8, 15620 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Bär, W., Kratzer, A., Mächler, M. & Schmid, W. Postmortem stability of DNA. Forensic Sci. Int. 39, 59–70 (1988).

    PubMed  Google Scholar 

  47. 47.

    Sijen, T. Molecular approaches for forensic cell type identification: on mRNA, miRNA, DNA methylation and microbial markers. Forensic Sci. Int. Genet. 18 September, 21–32 (2015).

    CAS  PubMed  Google Scholar 

  48. 48.

    Vass, A. A. The elusive universal post-mortem interval formula. Forensic Sci. Int. 204, 34–40 (2011).

    PubMed  Google Scholar 

  49. 49.

    Dumache, R., Ciocan, V., Muresan, C., Rogobete, A. F. & Enache, A. Circulating microRNAs as promising biomarkers in forensic body fluids identification. Clin. Lab. 61, 1129–1135 (2015).

    CAS  PubMed  Google Scholar 

  50. 50.

    Freire-Aradas, A., Phillips, C. & Lareu, M. V. Forensic individual age estimation with DNA: From initial approaches to methylation tests. Forensic Sci. Rev. 29, 121–144 (2017).

    CAS  PubMed  Google Scholar 

  51. 51.

    Woerner, A. E. et al. Forensic human identification with targeted microbiome markers using nearest neighbor classification. Forensic Sci. Int. Genet. 38 Jan, 130–139 (2019).

    CAS  PubMed  Google Scholar 

  52. 52.

    Van den Berge, M., Wiskerke, D., Gerretsen, R. R. R., Tabak, J. & Sijen, T. DNA and RNA profiling of excavated human remains with varying postmortem intervals. Int. J. Leg. Med. 130, 1471–1480 (2016).

    Google Scholar 

  53. 53.

    Bauer, M. RNA in forensic science. Forensic Sci. Int. Genet. 1, 69–74 (2007).

    CAS  PubMed  Google Scholar 

  54. 54.

    Budowle, B., Schmedes, S. E. & Wendt, F. R. Increasing the reach of forensic genetics with massively parallel sequencing. Forensic Sci. Med. Pathol. 13, 342–349 (2017).

    CAS  PubMed  Google Scholar 

  55. 55.

    Hunt, R. W., D’Onise, K., Nguyen, A. M. T. & Venugopal, K. Where patients with cancer die: a population-based study, 1990 to 2012. J. Palliat. Care Nov 28, 825859718814813 (2018).

    Google Scholar 

  56. 56.

    Gao, W. et al. A population-based conceptual framework for evaluating the role of healthcare services in place of death. Healthcare 6, 107 (2018).

    PubMed Central  Google Scholar 

  57. 57.

    Bryant, V. A. et al. Childhood neoplasms presenting at autopsy: a 20-year experience. Pediatr. Blood Cancer 64, e26474 (2017).

    Google Scholar 

  58. 58.

    Suzuki, H., Tanifuji, T., Abe, N. & Fukunaga, T. Causes of death in forensic autopsy cases of malnourished persons. Leg. Med. 15, 7–11 (2013).

    Google Scholar 

  59. 59.

    Carithers, L. J. & Moore, H. M. The genotype-tissue expression (GTEx) project. Biopreserv. Biobank. 13, 307–308 (2015).

    PubMed  PubMed Central  Google Scholar 

  60. 60.

    Wainberg, M. et al. Opportunities and challenges for transcriptome-wide association studies. Nat. Genet. 51, 592–599 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61.

    Rivas, M. A. et al. Effect of predicted protein-truncating genetic variants on the human transcriptome. Science 348, 666–669 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Mohammadi, P., Castel, S. E., Brown, A. A. & Lappalainen, T. Quantifying the regulatory effect size of cis-acting genetic variation using allelic fold change. Genome Res. 27, 1859–1871 (2017).

    Google Scholar 

  63. 63.

    Melé, M. et al. The human transcriptome across tissues and individuals. Science 348, 660–665 (2015).

    PubMed  PubMed Central  Google Scholar 

  64. 64.

    Tukiainen, T. et al. Landscape of X chromosome inactivation across human tissues. Nat. 550, 244–248 (2017).

    Google Scholar 

  65. 65.

    Saha, A. et al. Co-expression networks reveal the tissue-specific regulation of transcription and splicing. Genome Res. 27, 1843–1858 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. 66.

    Zhu, Y., Wang, L., Yin, Y. & Yang, E. Systematic analysis of gene expression patterns associated with postmortem interval in human tissues. Sci. Rep. 7, 5435 (2017).

    PubMed  PubMed Central  Google Scholar 

  67. 67.

    Fan, J. & Iacobuzio-Donahue, C. A. The science of rapid research autopsy. in Autopsy in the 21st Century: Best Practices and Future Diretions (eds. Hooper, J. E. & Williamson, A.) 151–166 (Springer International Publishing, 2018).

  68. 68.

    Embuscado, E. E. E. et al. Immortalizing the complexity of cancer metastasis: genetic features of lethal metastatic pancreatic cancer obtained from rapid autopsy. Cancer Biol. Ther. 4, 548–554 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. 69.

    Hooper, J. E. et al. A patient-derived xenograft model of parameningeal embryonal rhabdomyosarcoma for preclinical studies. Sarcoma 1, 826124 (2015).

    Google Scholar 

  70. 70.

    Misuraca, K. L., Cordero, F. J. & Becher, O. J. Pre-clinical models of diffuse intrinsic pontine glioma. Front. Oncol. 5, 172 (2015).

    PubMed  PubMed Central  Google Scholar 

  71. 71.

    Nguyen, H. M. et al. LuCaP prostate cancer patient-derived xenografts reflect the molecular heterogeneity of advanced disease and serve as models for evaluating cancer therapeutics. Prostate 77, 654–671 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. 72.

    Cocariu, E. A. et al. Correlations between the autolytic changes and postmortem interval in refrigerated cadavers. Rom. J. Intern. Med. 54, 105–112 (2016).

    PubMed  Google Scholar 

  73. 73.

    Fan, J. et al. Quantification of nucleic acid quality in postmortem tissues from a cancer research autopsy program. Oncotarget 7, 66906–66921 (2016).

    PubMed  PubMed Central  Google Scholar 

  74. 74.

    Tomita, H. et al. Effect of agonal and postmortem factors on gene expression profile: Quality control in microarray analyses of postmortem human brain. Biol. Psychiatry 55, 346–352 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. 75.

    Harrison, P. J. et al. The relative importance of premortem acidosis and postmortem interval for human brain gene expression studies: selective mRNA vulnerability and comparison with their encoded proteins. Neurosci. Lett. 200, 151–154 (1995).

    CAS  PubMed  Google Scholar 

  76. 76.

    Gilkes, D. M., Semenza, G. L. & Wirtz, D. Hypoxia and the extracellular matrix: drivers of tumour metastasis. Nat. Rev. Cancer 14, 430–439 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. 77.

    Nobre, A. R., Entenberg, D., Wang, Y., Condeelis, J. & Aguirre-Ghiso, J. A. The different routes to metastasis via hypoxia-regulated programs. Trends Cell Biol. 28, 941–956 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. 78.

    Maley, C. C. et al. Classifying the evolutionary and ecological features of neoplasms. Nat. Rev. Cancer 17, 605–619 (2017). A commentary article proposing an objective set of metrics to quantify cell-intrinsic and cell-extrinsic factors that influence the clonal evolution of cancer.

    CAS  PubMed  PubMed Central  Google Scholar 

  79. 79.

    Turajlic, S., Sottoriva, A., Graham, T. & Swanton, C. Resolving genetic heterogeneity in cancer. Nat. Rev. Genet. 20, 404–416 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. 80.

    Greaves, M. Darwinian medicine: a case for cancer. Nat. Rev. Cancer 7, 213–221 (2007).

    CAS  PubMed  Google Scholar 

  81. 81.

    Wilson, B. A., Garud, N. R., Feder, A. F., Assaf, Z. J. & Pennings, P. S. The population genetics of drug resistance evolution in natural populations of viral, bacterial and eukaryotic pathogens. Mol. Ecol. 25, 42–66 (2016).

    CAS  PubMed  Google Scholar 

  82. 82.

    Kreiner, J. M., Stinchcombe, J. R. & Wright, S. I. Population genomics of herbicide resistance: adaptation via evolutionary rescue. Annu. Rev. Plant Biol. Apr 29, 611–635 (2017).

    Google Scholar 

  83. 83.

    Gillies, R. J., Verduzco, D. & Gatenby, R. A. Evolutionary dynamics of carcinogenesis and why targeted therapy does not work. Nat. Rev. Cancer 12, 487–493 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. 84.

    Ibrahim-Hashim, A. et al. Defining cancer subpopulations by adaptive strategies rather than molecular properties provides novel insights into intratumoral evolution. Cancer Res. 77, 2242–2254 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. 85.

    Spunt, S. L. et al. The clinical, research, and social value of autopsy after any cancer death. Cancer 118, 3002–3009 (2012).

    PubMed  Google Scholar 

  86. 86.

    Van Der Linden., A. et al. Post-mortem tissue biopsies obtained at minimally invasive autopsy: an RNA-quality analysis. PLOS ONE 9, e115675 (2014).

    PubMed  PubMed Central  Google Scholar 

  87. 87.

    Gerlinger, M. et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366, 883–892 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. 88.

    de Bruin, E. C., McGranahan, N. & Swanton, C. Analysis of intratumor heterogeneity unravels lung cancer evolution. Mol. Cell. Oncol. 2, e985549 (2015).

    PubMed  PubMed Central  Google Scholar 

  89. 89.

    Kim, S. K. et al. Comprehensive analysis of genetic aberrations linked to tumorigenesis in regenerative nodules of liver cirrhosis. J. Gastroenterol. 54, 628–640 (2019).

    CAS  PubMed  Google Scholar 

  90. 90.

    Zhang, A. W. et al. Interfaces of malignant and immunologic clonal dynamics in ovarian. Cancer. Cell 173, 1755–1769 (2018).

    CAS  Google Scholar 

  91. 91.

    Naxerova, K. et al. Origins of lymphatic and distant metastases in human colorectal cancer. Science 357, 55–60 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. 92.

    McPherson, A. et al. Divergent modes of clonal spread and intraperitoneal mixing in high-grade serous ovarian cancer. Nat. Genet. 48, 758–767 (2016).

    CAS  PubMed  Google Scholar 

  93. 93.

    Faltas, B. M. et al. Clonal evolution of chemotherapy-resistant urothelial carcinoma. Nat. Genet. 48, 1490–1499 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. 94.

    Brown, D. et al. Phylogenetic analysis of metastatic progression in breast cancer using somatic mutations and copy number aberrations. Nat. Commun. 8, 14944 (2017).

    PubMed  PubMed Central  Google Scholar 

  95. 95.

    Yachida, S. et al. Distant metastasis occurs late during the genetic evolution of pancreatic cancer. Nat. 467, 1114–1117 (2010).

    CAS  Google Scholar 

  96. 96.

    Ascierto, M. L. M. L. et al. Transcriptional mechanisms of resistance to anti-PD-1 therapy. Clin. Cancer Res. 23, 3168–3180 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  97. 97.

    Sottoriva, A. et al. A Big Bang model of human colorectal tumor growth. Nat. Genet. 47, 209–216 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. 98.

    Sun, R. et al. Between-region genetic divergence reflects the mode and tempo of tumor evolution. Nat. Genet. 49, 1015–1024 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  99. 99.

    Aryee, M. J. et al. DNA methylation alterations exhibit intraindividual stability and interindividual heterogeneity in prostate cancer metastases. Sci. Transl. Med. 5, 169ra10 (2013).

    PubMed  PubMed Central  Google Scholar 

  100. 100.

    Turajlic, S. et al. Deterministic evolutionary trajectories influence primary tumor growth: TRACERx Renal. Cell 173, 595–610 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  101. 101.

    Reiter, J. G. J. G. et al. Minimal functional driver gene heterogeneity among untreated metastases. Science 361, 1033–1037 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  102. 102.

    Berquist, S. W. et al. Systemic therapy in the management of localized and locally advanced renal cell carcinoma: current state and future perspectives. Int. J. Urol. 26, 532–542 (2019).

    PubMed  Google Scholar 

  103. 103.

    Makohon-Moore, A. P. et al. Limited heterogeneity of known driver gene mutations among the metastases of individual patients with pancreatic cancer. Nat. Genet. 49, 358–366 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  104. 104.

    Borazanci, E. et al. Pancreatic cancer: ‘a riddle wrapped in a mystery inside an enigma’. Clin. Cancer Res. 23, 1629–1637 (2017).

    PubMed  Google Scholar 

  105. 105.

    Rosenthal, R. et al. Neoantigen-directed immune escape in lung cancer evolution. Nature 567, 479–485 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  106. 106.

    Mitchell, T. J. et al. Timing the landmark events in the evolution of clear cell renal cell cancer: TRACERx Renal. Cell 173, 611–623 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  107. 107.

    Jones, S. et al. Comparative lesion sequencing provides insights into tumor evolution. Proc. Natl Acad. Sci. USA 105, 4283–4288 (2008). The first study to use whole-exome sequencing data to derive estimates of the evolutionary life history of a neoplasm.

    CAS  PubMed  PubMed Central  Google Scholar 

  108. 108.

    Körber, V. et al. Evolutionary trajectories of IDHWT glioblastomas reveal a common path of early tumorigenesis instigated years ahead of initial diagnosis. Cancer Cell 35, 692–704.e12 (2019).

    PubMed  Google Scholar 

  109. 109.

    Wu, R. et al. Genomic landscape and evolutionary trajectories of ovarian cancer precursor lesions. J. Pathol. 248, 41–50 (2019).

    PubMed  PubMed Central  Google Scholar 

  110. 110.

    Matsuda, Y. et al. The prevalence and clinicopathological characteristics of high-grade pancreatic intraepithelial neoplasia autopsy study evaluating the entire pancreatic parenchyma. Pancreas 46, 658–664 (2017).

    PubMed  Google Scholar 

  111. 111.

    Groot, V. P. et al. Systematic review on the treatment of isolated local recurrence of pancreatic cancer after surgery; re-resection, chemoradiotherapy and SBRT. HPB 19, 83–92 (2017).

    PubMed  Google Scholar 

  112. 112.

    Makohon-Moore, A. P. et al. Precancerous neoplastic cells can move through the pancreatic ductal system. Nature 561, 201–205 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  113. 113.

    Martincorena, I. et al. Somatic mutant clones colonize the human esophagus with age. Science 362, 911–917 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  114. 114.

    Yizhak, K. et al. RNA sequence analysis reveals macroscopic somatic clonal expansion across normal tissues. Science 364, eaaw0726 (2019).

    CAS  PubMed  Google Scholar 

  115. 115.

    Biswas, R. et al. Genomic profiling of multiple sequentially acquired tumor metastatic sites from an “exceptional responder” lung adenocarcinoma patient reveals extensive genomic heterogeneity and novel somatic variants driving treatment response. Mol. Case Stud. 2, a001263 (2016).

    Google Scholar 

  116. 116.

    Sanchez-Vega, F. et al. EGFR and MET amplifications determine response to HER2 inhibition in ERBB2-amplified esophagogastric cancer. Cancer Discov. 9, 199–209 (2019).

    PubMed  Google Scholar 

  117. 117.

    Juric, D. et al. Convergent loss of PTEN leads to clinical resistance to a PI(3)Kα inhibitor. Nature 518, 240–244 (2015).

    CAS  PubMed  Google Scholar 

  118. 118.

    Topalian, S. L., Drake, C. G. & Pardoll, D. M. Immune checkpoint blockade: a common denominator approach to cancer therapy. Cancer Cell 27, 450–461 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  119. 119.

    Sohal, D. P. S. et al. Metastatic pancreatic cancer: American Society of Clinical Oncology clinical practice guideline. J. Clin. Oncol. 34, 2784–2796 (2016).

    PubMed  PubMed Central  Google Scholar 

  120. 120.

    Jaber, Y., Reichard, C. A. & Chapin, B. F. Emerging role of cytoreductive prostatectomy in patients with metastatic disease. Transl. Androl. Urol. 7, S505–S513 (2018).

    PubMed  PubMed Central  Google Scholar 

  121. 121.

    Winter, J. M. et al. Survival after resection of pancreatic adenocarcinoma: results from a single institution over three decades. Ann. Surg. Oncol. 19, 169–175 (2012).

    PubMed  Google Scholar 

  122. 122.

    Yachida, S. et al. Establishment and characterization of a new cell line, A99, from a primary small cell carcinoma of the pancreas. Pancreas 40, 905–910 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  123. 123.

    Skapek, S. X. et al. Rhabdomyosarcoma. Nat. Rev. Dis. Prim. 5, 1 (2019).

    PubMed  Google Scholar 

  124. 124.

    Alabran, J. L. et al. Overcoming autopsy barriers in pediatric cancer research. Pediatr. Blood Cancer 60, 204–209 (2013).

    PubMed  Google Scholar 

  125. 125.

    Hawkins, D. S., Spunt, S. L. & Skapek, S. X. Children’s Oncology Group’s 2013 blueprint for research: soft tissue sarcomas. Pediatr. Blood Cancer 60, 1001–1008 (2013).

    PubMed  Google Scholar 

  126. 126.

    Monje, M. et al. Hedgehog-responsive candidate cell of origin for diffuse intrinsic pontine glioma. Proc. Natl Acad. Sci. USA 108, 4453–4458 (2011).

    CAS  PubMed  Google Scholar 

  127. 127.

    Caretti, V. et al. Human pontine glioma cells can induce murine tumors. Acta Neuropathol. 127, 897–909 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  128. 128.

    Sanchez, H. & Chamberlin, G. Utilizing the autopsy for quality improvement. in Autopsy in the 21st Century: Best Practices and Future Directions (eds. Hooper, J. E. & Williamson, A.) 19–38 (Springer International, 2018).

  129. 129.

    Cabot, R. C. Diagnostic pitfalls identified during a study of three thousand autopsies. J. Am. Med. Assoc. LIX, 2295–2298 (1912). A study illustrating the rates of incidental and unappreciated clinically relevant findings found at autopsy.

    Google Scholar 

  130. 130.

    Shojania, K. G., Burton, E. C., McDonald, K. M. & Goldman, L. Changes in rates of autopsy-detected diagnostic errors over time: a systematic review. JAMA 289, 2849–2856 (2003).

    PubMed  Google Scholar 

  131. 131.

    Chow, S.-C. Adaptive clinical trial design. Annu. Rev. Med. 65, 405–415 (2014).

    CAS  PubMed  Google Scholar 

  132. 132.

    Gallo, P. et al. Adaptive designs in clinical drug development—an executive summary of the PhRMA working group. J. Biopharm. Stat. 16, 275–283 (2006).

    PubMed  Google Scholar 

  133. 133.

    Freidlin, B. & Korn, E. L. Biomarker-adaptive clinical trial designs. Pharmacogenomics 11, 1679–1682 (2010).

    PubMed  Google Scholar 

  134. 134.

    Regev, A. et al. The human cell atlas. Elife 6, e27041 (2017).

  135. 135.

    Manjili, M. H. Tumor dormancy and relapse: from a natural byproduct of evolution to a disease state. Cancer Res. 77, 2564–2569 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  136. 136.

    Chukwueke, U. N. & Brastianos, P. K. Sequencing brain metastases and opportunities for targeted therapies. Pharmacogenomics 18, 585–594 (2017).

    CAS  PubMed  Google Scholar 

  137. 137.

    Palmieri, D., Chambers, A. F., Felding-Habermann, B., Huang, S. & Steeg, P. S. The biology of metastasis to a sanctuary site. Clin. Cancer Res. 13, 1656–1662 (2007).

    CAS  PubMed  Google Scholar 

  138. 138.

    Beerenwinkel, N., Greenman, C. D. & Lagergren, J. Computational cancer biology: an evolutionary perspective. PLOS Comput. Biol. 12, e1004717 (2016).

    PubMed  PubMed Central  Google Scholar 

  139. 139.

    Levitin, H. M., Yuan, J. & Sims, P. A. Single-cell transcriptomic analysis of tumor heterogeneity. Trends Cancer 4, 264–268 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  140. 140.

    Reiter, J. G. et al. Reconstructing metastatic seeding patterns of human cancers. Nat. Commun. 8, 14114 (2017). This article presents a phylogenetic algorithm developed specifically for the analysis of multiregion-sequenced tumour samples.

    CAS  PubMed  PubMed Central  Google Scholar 

  141. 141.

    Zaccaria, S., El-Kebir, M., Klau, G. W. & Raphael, B. J. Phylogenetic copy-number factorization of multiple tumor samples. J. Comput. Biol. 25, 689–708 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  142. 142.

    Niknafs, N., Beleva-Guthrie, V., Naiman, D. Q. & Karchin, R. Subclonal hierarchy inference from somatic mutations: automatic reconstruction of cancer evolutionary trees from multi-region next generation sequencing. PLOS Comput. Biol. 11, e1004416 (2015).

    PubMed  PubMed Central  Google Scholar 

  143. 143.

    Malikic, S., McPherson, A. W., Donmez, N. & Sahinalp, C. S. Clonality inference in multiple tumor samples using phylogeny. Bioinformatics 31, 1349–1356 (2015).

    CAS  PubMed  Google Scholar 

  144. 144.

    El-Kebir, M., Satas, G. & Raphael, B. J. Inferring parsimonious migration histories for metastatic cancers. Nat. Genet. 50, 718–726 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  145. 145.

    Tang, Z., Kang, B., Li, C., Chen, T. & Zhang, Z. GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res. 47, W556–W560 (2019).

    PubMed  PubMed Central  Google Scholar 

  146. 146.

    Ma, Y. & Wei, P. FunSPU: a versatile and adaptive multiple functional annotation-based association test of whole-genome sequencing data. PLOS Genet. 15, e1008081 (2019).

    PubMed  PubMed Central  Google Scholar 

  147. 147.

    Mounir, M. et al. New functionalities in the TCGAbiolinks package for the study and integration of cancer data from GDC and GTEx. PLOS Comput. Biol. 15, e1006701 (2019).

    PubMed  PubMed Central  Google Scholar 

  148. 148.

    Achkar, T., Wilson, J., Simon, J., Rosenzweig, M. & Puhalla, S. Metastatic breast cancer patients: attitudes toward tissue donation for rapid autopsy. Breast Cancer Res. Treat. 155, 159–164 (2016).

    PubMed  Google Scholar 

  149. 149.

    Tsitsikas, D. A., Brothwell, M., Chin Aleong, J.-A. & Lister, A. T. The attitudes of relatives to autopsy: a misconception. J. Clin. Pathol. 64, 412–414 (2011).

    PubMed  Google Scholar 

  150. 150.

    Alsop, K. et al. A community-based model of rapid autopsy in end-stage cancer patients. Nat. Biotechnol. 34, 1010–1014 (2016). A comprehensive description of the logistics of running a large-scale research autopsy programme for patients with cancer.

    CAS  PubMed  Google Scholar 

  151. 151.

    Siminoff, L. A. et al. Impact of cognitive load on family decision makers’ recall and understanding of donation requests for the genotype-tissue expression (GTEx) project. J. Clin. Ethics 29, 20–30 (2018).

    PubMed  Google Scholar 

  152. 152.

    Hoadley, K. A. et al. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell 158, 929–944 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors acknowledge grant support from the US National Institutes of Health (grants R01 CA179991 and R35 CA220508 to C.I.A,-D) and salary support from the Parker Institute for Cancer Immunotherapy to T.J.H.

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All authors researched data for the manuscript and made substantial contributions to the discussion of the content. C.A.I.-D., J.E.H. and T.J.H. wrote the manuscript. C.A.I.-D., P.B., R.K., J.E.H. and T.J.H. reviewed and/or edited the manuscript before submission.

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Correspondence to Christine A. Iacobuzio-Donahue.

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C.I.A.-D. and T.J.H. have received research support from Bristol-Myers Squibb. The other authors declare no competing interests.

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Nature Reviews Cancer thanks M. Rubin, S. Turajlic and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Glossary

Rapid autopsy

An autopsy that is performed within 2 h of cardiopulmonary arrest.

Warm autopsy

An autopsy that is performed so rapidly that the deceased person’s body has not yet cooled to room temperature.

Somatic mosaicism

The presence of two or more genetically distinct populations of cells within an individual.

Tumour dormancy

A state in which viable cancer cells remain quiescent for a prolonged period.

Sanctuary sites

Tissues within the body in which cancer cells are protected from pharmacological agents or other therapies.

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Iacobuzio-Donahue, C.A., Michael, C., Baez, P. et al. Cancer biology as revealed by the research autopsy. Nat Rev Cancer 19, 686–697 (2019). https://doi.org/10.1038/s41568-019-0199-4

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