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.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
de la Fuente IM (2015) Elements of the cellular metabolic structure. Front Mol Biosci 2:16. https://doi.org/10.3389/fmolb.2015.00016
Merlo LMF, Pepper JW, Reid BJ, Maley CC (2006) Cancer as an evolutionary and ecological process. Nat Rev Cancer 6:924–935
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. https://doi.org/10.1186/s13059-014-0453-8
Davis A, Gao R, Navin N (2017) Tumor evolution: linear, branching, neutral or punctuated? Biochim Biophys Acta. https://doi.org/10.1016/j.bbcan.2017.01.003
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
Rosai J (2011) Rosai and Ackerman’s surgical pathology, 10th edn. Mosby-Elsevier, Edinburgh
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
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. https://doi.org/10.1038/srep29434
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
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
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
Bosman FT (2017) Tumor heterogeneity: will it change what pathologists do? Pathobiology. https://doi.org/10.1159/000469664
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
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
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
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
Cortés JM, de Petris G, López JI (2017) Detection of intratumor heterogeneity in modern pathology: a multisite tumor sampling perspective. Front Med 4:25. https://doi.org/10.3389/fmed.2017.00025
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
Fearon ER, Vogelstein B (1990) A genetic model for colorectal tumorigenesis. Cell 61:759–767
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
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
Williams MJ, Werner B, Barnes CP, Graham TA, Sottoriva A (2016) Identification of neutral evolution across cancer types. Nat Genet 48:238–244
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
Kon R (2015) Dynamics of competitive systems with a single common limitating factor. Math Biosci Eng 12:71–81
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
Nagy JD (2004) Competition and natural selection in a mathematical model of cancer. Bull Math Biol 66:663–687
Marusyk A, Tabassum D, Altrock P (2014) Non-cell-autonomous driving of tumour growth suppots sub-clonal heterogeneity. Nature 514:54–58
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
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
López JI (2013) Renal tumors with clear cells. A review. Pathol Res Pract 209:137–146
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. https://doi.org/10.1016/j.ebiom.2017.04.009
Stanta G, Jahn SW, Bonin S, Hoefler G (2016) Tumor heterogeneity: principles and practical consequences. Virchows Arch 469:371–384
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
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
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
Pan Y, Ji JS, Jin JG, Kuo WP, Kang H (2017) Cancer liquid biopsy: is it ready for clinic? IEEE Pulse 8:23–27
Nadal C, Winder T, Gerger A, Tougeron D (2017) Future perspectives of circulating tumor DNA in colorectal cancer. Tumour Biol. https://doi.org/10.1177/1010428317705749
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. https://doi.org/10.1016/j.semcancer.2017.04.007
There is no funding support in this work.
This is an in silico work not involving human participants neither animals.
Conflict of interest
The authors declare no conflict of interest
About this article
Cite this article
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). https://doi.org/10.1007/s00428-017-2223-y
- Tumor sampling
- Intratumor heterogeneity
- Tumor evolution
- In silico analysis
- Divide and conquer algorithm
- Personalized therapy