Multisite tumor sampling enhances the detection of intratumor heterogeneity at all different temporal stages of tumor evolution


Intratumor heterogeneity (ITH) is an inherent process of tumor development that has received much attention in previous years, as it has become a major obstacle for the success of targeted therapies. ITH is also temporally unpredictable across tumor evolution, which makes its precise characterization even more problematic since detection success depends on the precise temporal snapshot at which ITH is analyzed. New and more efficient strategies for tumor sampling are needed to overcome these difficulties which currently rely entirely on the pathologist’s interpretation. Recently, we showed that a new strategy, the multisite tumor sampling, works better than the routine sampling protocol for the ITH detection when the tumor time evolution was not taken into consideration. Here, we extend this work and compare the ITH detections of multisite tumor sampling and routine sampling protocols across tumor time evolution, and in particular, we provide in silico analyses of both strategies at early and late temporal stages for four different models of tumor evolution (linear, branched, neutral, and punctuated). Our results indicate that multisite tumor sampling outperforms routine protocols in detecting ITH at all different temporal stages of tumor evolution. We conclude that multisite tumor sampling is more advantageous than routine protocols in detecting intratumor heterogeneity.

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  1. 1.

    de la Fuente IM (2015) Elements of the cellular metabolic structure. Front Mol Biosci 2:16.

    PubMed  PubMed Central  Google Scholar 

  2. 2.

    Merlo LMF, Pepper JW, Reid BJ, Maley CC (2006) Cancer as an evolutionary and ecological process. Nat Rev Cancer 6:924–935

    CAS  Article  PubMed  Google Scholar 

  3. 3.

    Hiley C, de Bruin EC, McGranahan N, Swanton C (2014) Deciphering intratumor heterogeneity and temporal acquisition of driver events to refine precision medicine. Genome Biol 15:453.

    Article  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Davis A, Gao R, Navin N (2017) Tumor evolution: linear, branching, neutral or punctuated? Biochim Biophys Acta.

  5. 5.

    Morris LGT, Riaz N, Desrichard A, Senbabaoglu Y, Ari Hakimi A, Makarov V, Reis-Filho JS, Chan TA (2016) Pan-cancer analysis of intratumor heterogeneity as a prognostic determinant of survival. Oncotarget 7:10051–10063

    PubMed  PubMed Central  Google Scholar 

  6. 6.

    Rosai J (2011) Rosai and Ackerman’s surgical pathology, 10th edn. Mosby-Elsevier, Edinburgh

    Google Scholar 

  7. 7.

    Kim TM, Jung SH, Baek IP, Lee SH, Choi YJ, Lee JY, Chung YJ, Lee SH (2014) Regional biases in mutation screening due to intratumoural heterogeneity of prostate cancer. J Pathol 233:425–435

    CAS  Article  PubMed  Google Scholar 

  8. 8.

    Pearce DA, Arthur LM, Turnbull AK, Renshaw L, Sabine VS, Thomas JS, Bartlett JM, Dixon JM, Sims AH (2016) Tumour sampling method can significantly influence gene expression profiles derived from neoadjuvant window studies. Sci Rep 6:29434.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Paracchini L, Mannarino L, Craparotta I, Romualdi C, Fruscio R, Grassi T, Fotia V, Caratti G, Perego P, Calura E, Clivio L, D’Incalci M, Beltrame L, Marchini S (2016) Regional and temporal heterogeneity of epithelial ovarian cancer tumor biopsies: implications for therapeutic strategies. Oncotarget. 10.18632/oncotarget.10505

  10. 10.

    Ledgerwood LG, Kumar D, Eterovic AK, Wick J, Chen K, Zhao H, Tazi L, Manna P, Kerley S, Joshi R, Wang L, Chiosea SI, Garnett JD, Tsue TT, Chien J, Mills GB, Grandis JR, Thomas SM (2016) The degree of intratumor mutational heterogeneity varies by primary tumor sub-site. Oncotarget 7:27185–27198. 10.18632/oncotarget.8448

    Article  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Bettoni F, Masotti C, Habr-Gama A, Correa BR, Gama-Rodrigues J, Vianna MR, Vailati BB, São Julião GP, Fernandez LM, Galante PA, Camargo AA, Perez RO (2017) Intratumoral genetic heterogeneity in rectal cancer. Are single biopsies representative of the entirety of the tumor? Ann Surg 265:e4–e6

    Article  PubMed  Google Scholar 

  12. 12.

    Bosman FT (2017) Tumor heterogeneity: will it change what pathologists do? Pathobiology.

  13. 13.

    López JI, Cortés JM (2016) A divide-and-conquer strategy in tumor sampling enhances detection of intratumor heterogeneity in pathology routine: a modeling approach in clear cell renal cell carcinoma. F1000Res 5:385. 10.12688/f1000research.8196.2

    Article  PubMed  PubMed Central  Google Scholar 

  14. 14.

    López JI, Cortés JM (2016) A multi-site cutting device implements efficiently the divide-and-conquer strategy in tumor sampling. F1000Res 5:1587. 10.12688/f1000research.9091.2

    Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Guarch R, Cortés JM, Lawrie CH, López JI (2016) Multi-site tumor sampling (MSTS) significantly improves the performance of histological detection of intratumour heterogeneity in clear cell renal cell carcinoma (CCRCC). F1000Res 5:2020. 10.12688/f1000research.9419.2

    Article  PubMed  PubMed Central  Google Scholar 

  16. 16.

    López JI, Cortés JM (2017) Multi-site tumor sampling (MSTS): a new tumor selection method to enhance intratumor heterogeneity detection. Hum Pathol 64:1–6

    Article  PubMed  Google Scholar 

  17. 17.

    Cortés JM, de Petris G, López JI (2017) Detection of intratumor heterogeneity in modern pathology: a multisite tumor sampling perspective. Front Med 4:25.

    Article  Google Scholar 

  18. 18.

    Ling S, Hu Z, Yang Z, Yang F, Li Y, Lin P, Chen K, Dong L, Cao L, Tao Y, Hao L, Chen Q, Gong Q, Wu D, Li W, Zhao W, Tian X, Hao C, Hungate EA, Catenacci DV, Hudson RR, Li WH, Lu X, Wu CI (2015) Extremely high genetic diversity in a single tumor points to prevalence of non-Darwinian cell evolution. Proc Natl Acad Sci U S A 112:E6496–E6505

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Fearon ER, Vogelstein B (1990) A genetic model for colorectal tumorigenesis. Cell 61:759–767

    CAS  Article  PubMed  Google Scholar 

  20. 20.

    Kang H, Salomon MP, Sottoriva A, Zhao J, Toy M, Press MF, Curtis C, Marjoram P, Siegmund K, Shibata D (2015) Many private mutations originate from the first few divisions of a human colorectal adenoma. J Pathol 237:355–362

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Sottoriva A, Kang H, Ma Z, Graham TA, Salomon MP, Zhao J, Marjoram P, Siegmund K, Press MF, Shibata D, Curtis C (2015) A big bang model of human colorectal tumor growth. Nat Genet 47:209–216

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Williams MJ, Werner B, Barnes CP, Graham TA, Sottoriva A (2016) Identification of neutral evolution across cancer types. Nat Genet 48:238–244

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Gerlinger M, Rowan AJ, Horswell S, Larkin J, Endesfelder D, Gronroos E, Martinez P, Matthews N, Stewart A, Tarpey P, Varela I, Phillimore B, Begum S, McDonald NQ, Butler A, Jones D, Raine K, Latimer C, Santos CR, Nohadani M, Eklund AC, Spencer-Dene B, Clark G, Pickering L, Stamp G, Gore M, Szallasi Z, Downward J, Futreal PA, Swanton C (2012) Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 366:883–892

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Kon R (2015) Dynamics of competitive systems with a single common limitating factor. Math Biosci Eng 12:71–81

    Article  PubMed  Google Scholar 

  25. 25.

    Gatenby RA, Vincent TL (2003) Application of quantitative models from population biology and evolutionary game theory to tumor therapeutic strategies. Mol Cancer Ther 2:919–927

    CAS  PubMed  Google Scholar 

  26. 26.

    Nagy JD (2004) Competition and natural selection in a mathematical model of cancer. Bull Math Biol 66:663–687

    Article  PubMed  Google Scholar 

  27. 27.

    Marusyk A, Tabassum D, Altrock P (2014) Non-cell-autonomous driving of tumour growth suppots sub-clonal heterogeneity. Nature 514:54–58

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Wang Y, Waters J, Leung ML, Unruh A, Roh W, Shi X, Chen K, Scheet P, Vattathil S, Lisng H, Multani A, Zhang H, Zhao R, Michor F, Meric-Bernstam F, Navin NE (2014) Clonal evolution in breast cancer revealed by single nucleus genome sequencing. Nature 512:155–160

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Gerlinger M, Horswell S, Larkin J, Rowan AJ, Salm MP, Varela I, Fisher R, McGranahan N, Matthews N, Santos CR, Martinez P, Phillimore B, Begum S, Rabinowitz A, Spencer-Dene B, Gulati S, Bates PA, Stamp G, Pickering L, Gore M, Nicol DL, Hazell S, Futreal PA, Stewart A, Swanton C (2014) Genomic architecture and evolution of clear cell renal cell carcinomas defined by multiregion sequencing. Nat Genet 46:225–233

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  30. 30.

    López JI (2013) Renal tumors with clear cells. A review. Pathol Res Pract 209:137–146

    Article  PubMed  Google Scholar 

  31. 31.

    Okegawa T, Morimoto M, Nishizawa S, Kitazawa S, Honda K, Araki H, Tamura T, Ando A, Satomi Y, Nutahara K, Hara T (2017) Intratumor heterogeneity in primary kidney cancer revealed by metabolic profiling of multiple spatially separated samples within tumors. EBioMedicine.

  32. 32.

    Stanta G, Jahn SW, Bonin S, Hoefler G (2016) Tumor heterogeneity: principles and practical consequences. Virchows Arch 469:371–384

    CAS  Article  PubMed  Google Scholar 

  33. 33.

    Saraga E, Bautista D, Dorta G, Chaubert P, Martin P, Sordat B, Protiva P, Blum A, Bosman F, Benhattar J (1997) Genetic heterogeneity in sporadic colorectal adenomas. J Pathol 181:281–286

    CAS  Article  PubMed  Google Scholar 

  34. 34.

    Baisse B, Bouzourene H, Saraga EP, Bosman FT, Benhattar J (2001) Intratumor genetic heterogeneity in advanced human colorectal adenocarcinoma. Int J Cancer 93:346–352

    CAS  Article  PubMed  Google Scholar 

  35. 35.

    Soultati A, Stares M, Swanton C, Larkin J, Turajlic S (2015) How should clinicians address intratumor heterogeneity in clear cell renal cell carcinoma? Curr Opin Urol 25:358–366

    Article  PubMed  Google Scholar 

  36. 36.

    Pan Y, Ji JS, Jin JG, Kuo WP, Kang H (2017) Cancer liquid biopsy: is it ready for clinic? IEEE Pulse 8:23–27

    Article  PubMed  Google Scholar 

  37. 37.

    Nadal C, Winder T, Gerger A, Tougeron D (2017) Future perspectives of circulating tumor DNA in colorectal cancer. Tumour Biol.

  38. 38.

    Appierto V, Di Cosimo S, Reduzzi C, Pala V, Cappelletti V, Daidone MG (2017) How to study and overcome tumor heterogeneity with circulating biomarkers: the breast cancer case. Semin Cancer Biol.

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All the authors acknowledge the following:

1. Substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work.

2. Drafting the work or revising it critically for important intellectual content.

3. Final approval of the version to be published.

4. Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Corresponding author

Correspondence to José I. López.

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This is an in silico work not involving human participants neither animals.

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The authors declare no conflict of interest

Electronic Supplementary Materials


Fig. S1 (supplemental figure): Animated gif showing the time evolution of Fig. 3. (GIF 7374 kb)

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Erramuzpe, A., Cortés, J.M. & López, J.I. Multisite tumor sampling enhances the detection of intratumor heterogeneity at all different temporal stages of tumor evolution. Virchows Arch 472, 187–194 (2018).

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  • Tumor sampling
  • Personalized
  • Intratumor heterogeneity
  • Tumor evolution
  • In silico analysis
  • Divide and conquer algorithm
  • Personalized therapy