Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Biopsies: next-generation biospecimens for tailoring therapy

Nature Reviews Clinical Oncology volume 10, pages 437–450 (2013)Cite this article

Subjects

A Correction to this article was published on 24 September 2013

This article has been updated

Abstract

The majority of samples in existing tumour biobanks are surgical specimens of primary tumours. Insights into tumour biology, such as intratumoural heterogeneity, tumour–host crosstalk, and the evolution of the disease during therapy, require biospecimens from the primary tumour and those that reflect the patient's disease in specific contexts. Next-generation 'omics' technologies facilitate deep interrogation of tumours, but the characteristics of the samples can determine the ultimate accuracy of the results. The challenge is to biopsy tumours, in some cases serially over time, ensuring that the samples are representative, viable, and adequate both in quantity and quality for subsequent molecular applications. The collection of next-generation biospecimens, tumours, and blood samples at defined time points during the disease trajectory—either for discovery research or to guide clinical decisions—presents additional challenges and opportunities. From an organizational perspective, it also requires new additions to the multidisciplinary therapeutic team, notably interventional radiologists, molecular pathologists, and bioinformaticians. In this Review, we describe the existing procedures for sample procurement and processing of next-generation biospecimens, and highlight the issues involved in this endeavour, including the ethical, logistical, scientific, informational, and financial challenges accompanying next-generation biobanking.

Key Points

  • Next-generation biospecimens are biopsy-type clinical specimens collected from patients at distinct time points and in a prespecified clinical context of treatment, made available for multidimensional high-throughput technologies

  • Biopsies of recurrent primary or metastatic tumours are highly sought after next-generation biospecimens for both research purposes and the clinical management of patients

  • Controlling preanalytical variables is critical to ensure that the results of multidimensional high-throughput profiling are accurate and reproducible

  • Standard operating procedures for biospecimen collection and processing, with quality assurance of every specimen, must be developed and adhered to, with particular emphasis placed on the training of personnel

  • Collection of next-generation biospecimens requires increased resources and a multidisciplinary team consisting of interventional radiologists and molecular pathologists

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Get just this article for as long as you need it

$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Workflow of next-generation biobanking, demonstrating the multidisciplinarity of the endeavour.
Figure 2: Interconnectivity between biopsies for discovery and biopsies to guide therapy.

Change history

  • 24 September 2013

    In the original published version of this article, the link in reference 106 was incorrect and should have referred to the Biospecimen Research Database of the NCI (page 444 of the article). This reference has now been corrected for the HTML and PDF versions of the article.

References

  1. Lovly, C. M. et al. Routine multiplex mutational profiling of melanomas enables enrollment in genotype-driven therapeutic trials. PLoS ONE 7, e35309 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Von Hoff, D. D. et al. Pilot study using molecular profiling of patients' tumors to find potential targets and select treatments for their refractory cancers. J. Clin. Oncol. 28, 4877–4883 (2010).

    Article  CAS  PubMed  Google Scholar 

  3. Roychowdhury, S. et al. Personalized oncology through integrative high-throughput sequencing: a pilot study. Sci. Transl. Med. 3, 111ra121 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. Desai, A. N. & Jere, A. Next-generation sequencing: ready for the clinics? Clin. Genet. 81, 503–510 (2012).

    Article  CAS  PubMed  Google Scholar 

  5. Blow, N. Biobanking: freezer burn. Nat. Methods 6, 173–178 (2009).

    Article  CAS  Google Scholar 

  6. International Cancer Genome Consortium et al. International network of cancer genome projects. Nature 464, 993–998 (2010).

  7. Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455, 1061–1068 (2008).

  8. Cancer Genome Atlas Research Network. Integrated genomic analyses of ovarian carcinoma. Nature 474, 609–615 (2011).

  9. Cancer Genome Atlas Research Network. Comprehensive molecular portraits of human breast tumours. Nature 490, 61–70 (2012).

  10. Cancer Genome Atlas Research Network Comprehensive molecular characterization of human colon and rectal cancer. Nature 487, 330–337 (2012).

  11. Cancer Genome Atlas Research Network. Comprehensive genomic characterization of squamous cell lung cancers. Nature 489, 519–525 (2012).

  12. The Cancer Genome Atlas. TCGA Tissue Sample Requirements: High Quality Requirements Yield High Quality Data [online], (2013).

  13. Koh, S. S. et al. Differential gene expression profiling of primary cutaneous melanoma and sentinel lymph node metastases. Mod. Pathol. 25, 828–837 (2012).

    Article  CAS  PubMed  Google Scholar 

  14. Shah, S. P. et al. Mutational evolution in a lobular breast tumour profiled at single nucleotide resolution. Nature 461, 809–813 (2009).

    Article  CAS  PubMed  Google Scholar 

  15. Ding, L. et al. Genome remodelling in a basal-like breast cancer metastasis and xenograft. Nature 464, 999–1005 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Turajlic, S. et al. Whole genome sequencing of matched primary and metastatic acral melanomas. Genome Res. 22, 196–207 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Aurilio, G. et al. Discordant hormone receptor and human epidermal growth factor receptor 2 status in bone metastases compared to primary breast cancer. Acta Oncol. http://dx.doi.org/10.3109/0284186X.2012.754990.

  19. Botteri, E. et al. Biopsy of liver metastasis for women with breast cancer: impact on survival. Breast 21, 284–288 (2012).

    Article  PubMed  Google Scholar 

  20. Gray, J. Cancer: Genomics of metastasis. Nature 464, 989–990 (2010).

    Article  CAS  PubMed  Google Scholar 

  21. Shi, H. et al. Melanoma whole-exome sequencing identifies (V600E)B-RAF amplification-mediated acquired B-RAF inhibitor resistance. Nat. Commun. 3, 724 (2012).

    Article  PubMed  CAS  Google Scholar 

  22. Doebele, R. C. et al. Mechanisms of resistance to crizotinib in patients with ALK gene rearranged non-small cell lung cancer. Clin. Cancer Res. 18, 1472–1482 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Flaherty, K. T. et al. Combined BRAF and MEK inhibition in melanoma with BRAF V600 mutations. N. Engl. J. Med. 367, 1694–1703 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Gorges, T. M. & Pantel, K. Circulating tumor cells as therapy-related biomarkers in cancer patients. Cancer Immunol. Immunother. 62, 931–939 (2013).

    Article  CAS  PubMed  Google Scholar 

  25. US National Library of Medicine. Clinicaltrials.gov [online], (2013).

  26. Dawson, S. J. et al. Analysis of circulating tumor DNA to monitor metastatic breast cancer. N. Engl. J. Med. 368, 1199–1209 (2013).

    Article  CAS  PubMed  Google Scholar 

  27. Misale, S. et al. Emergence of KRAS mutations and acquired resistance to anti-EGFR therapy in colorectal cancer. Nature 486, 532–536 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Murtaza, M. et al. Non-invasive analysis of acquired resistance to cancer therapy by sequencing of plasma DNA. Nature 497, 108–112 (2013).

    Article  CAS  PubMed  Google Scholar 

  29. Overman, M. J. et al. Use of research biopsies in clinical trials: are risks and benefits adequately discussed? J. Clin. Oncol. 31, 17–22 (2013).

    Article  PubMed  Google Scholar 

  30. Goulart, B. H. et al. Trends in the use and role of biomarkers in phase I oncology trials. Clin. Cancer Res. 13, 6719–6726 (2007).

    Article  CAS  PubMed  Google Scholar 

  31. Subramanian, J. & Simon, R. Gene expression-based prognostic signatures in lung cancer: ready for clinical use? J. Natl Cancer Inst. 102, 464–474 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Liu, G. et al. Pharmacogenetic analysis of BR.21, a placebo-controlled randomized phase III clinical trial of erlotinib in advanced non-small cell lung cancer. J. Thorac. Oncol. 7, 316–322 (2012).

    Article  CAS  PubMed  Google Scholar 

  33. Choi, Y. L. et al. EML4-ALK mutations in lung cancer that confer resistance to ALK inhibitors. N. Engl. J. Med. 363, 1734–1739 (2010).

    Article  CAS  PubMed  Google Scholar 

  34. Sharma, M. R. & Schilsky, R. L. Role of randomized phase III trials in an era of effective targeted therapies. Nat. Rev. Clin. Oncol. 9, 208–214 (2011).

    Article  PubMed  CAS  Google Scholar 

  35. Amir, E. et al. Tissue confirmation of disease recurrence in breast cancer patients: pooled analysis of multi-centre, multi-disciplinary prospective studies. Cancer Treat. Rev. 38, 708–714 (2011).

    Article  PubMed  Google Scholar 

  36. Amir, E. et al. Prospective study evaluating the impact of tissue confirmation of metastatic disease in patients with breast cancer. J. Clin. Oncol. 30, 587–592 (2012).

    Article  PubMed  Google Scholar 

  37. Chia, S. Testing for discordance at metastatic relapse: does it matter? J. Clin. Oncol. 30, 575–576 (2012).

    Article  PubMed  Google Scholar 

  38. Niikura, N. et al. Loss of human epidermal growth factor receptor 2 (HER2) expression in metastatic sites of HER2-overexpressing primary breast tumors. J. Clin. Oncol. 30, 593–599 (2012).

    Article  PubMed  Google Scholar 

  39. Watanabe, T. et al. Heterogeneity of KRAS status may explain the subset of discordant KRAS status between primary and metastatic colorectal cancer. Dis. Colon Rectum 54, 1170–1178 (2011).

    Article  PubMed  Google Scholar 

  40. Artale, S. et al. Mutations of KRAS and BRAF in primary and matched metastatic sites of colorectal cancer. J. Clin. Oncol. 26, 4217–4219 (2008).

    Article  PubMed  Google Scholar 

  41. Kim, E. et al. The BATTLE trial: personalizing therapy for lung cancer. Cancer Discov. 1, 44–53 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Tran, B. et al. Feasibility of real time next generation sequencing of cancer genes linked to drug response: results from a clinical trial. Int. J. Cancer 132, 1547–1555 (2013).

    Article  CAS  PubMed  Google Scholar 

  43. Dancey, J. E., Bedard, P. L., Onetto, N. & Hudson, T. J. The genetic basis for cancer treatment decisions. Cell 148, 409–420 (2012).

    Article  CAS  PubMed  Google Scholar 

  44. Institute of Cancerology Gustave Roussy. Official launch of the clinical, academic and international trial WINTHER : a bioinformatics scoring system that predicts the response to known treatments for each patient [online], (2012).

  45. Tsimberidou, A. M. et al. Personalized medicine in a phase I clinical trials program: the MD Anderson Cancer Center Initiative. Clin. Cancer Res. 18, 6373–6383 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Wright, J. R. et al. Why cancer patients enter randomized clinical trials: exploring the factors that influence their decision. J. Clin. Oncol. 22, 4312–4318 (2004).

    Article  PubMed  Google Scholar 

  47. Peppercorn, J. et al. Ethics of mandatory research biopsy for correlative end points within clinical trials in oncology. J. Clin. Oncol. 28, 2635–2640 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Lee, J. M. et al. Feasibility and safety of sequential research-related tumor core biopsies in clinical trials. Cancer 119, 1357–1364 (2013).

    Article  CAS  PubMed  Google Scholar 

  49. Olson, E. M., Lin, N. U., Krop, I. E. & Winer, E. P. The ethical use of mandatory research biopsies. Nat. Rev. Clin. Oncol. 8, 620–625 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Tam, A. L. et al. Feasibility of image-guided transthoracic core-needle biopsy in the BATTLE lung trial. J. Thorac. Oncol. 8, 436–442 (2013).

    Article  PubMed  Google Scholar 

  51. Dowlati, A. et al. Sequential tumor biopsies in early phase clinical trials of anticancer agents for pharmacodynamic evaluation. Clin. Cancer Res. 7, 2971–2976 (2001).

    CAS  PubMed  Google Scholar 

  52. Nazarian, L. N. et al. Safety and efficacy of sonographically guided random core biopsy for diffuse liver disease. J. Ultrasound Med. 19, 537–541 (2000).

    Article  CAS  PubMed  Google Scholar 

  53. Caliskan, K. C., Cakmakci, E., Celebi, I. & Basak, M. The importance of experience in percutaneous liver biopsies guided with ultrasonography: a lesion-focused approach. Acad. Radiol. 19, 256–259 (2012).

    Article  PubMed  Google Scholar 

  54. Grant, A. & Neuberger, J. Guidelines on the use of liver biopsy in clinical practice. British Society of Gastroenterology. Gut 45 (Suppl. 4), IV1–IV11 (1999).

    PubMed  PubMed Central  Google Scholar 

  55. Robertson, E. G. & Baxter, G. Tumour seeding following percutaneous needle biopsy: the real story! Clin. Radiol. 66, 1007–1014 (2011).

    Article  CAS  PubMed  Google Scholar 

  56. Agulnik, M., Oza, A. M., Pond, G. R. & Siu, L. L. Impact and perceptions of mandatory tumor biopsies for correlative studies in clinical trials of novel anticancer agents. J. Clin. Oncol. 24, 4801–4807 (2006).

    Article  PubMed  Google Scholar 

  57. Wolf, S. M. et al. Managing incidental findings and research results in genomic research involving biobanks and archived data sets. Genet. Med. 14, 361–384 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Bredenoord, A. L., Kroes, H. Y., Cuppen, E., Parker, M. & van Delden, J. J. Disclosure of individual genetic data to research participants: the debate reconsidered. Trends Genet. 27, 41–47 (2011).

    Article  CAS  PubMed  Google Scholar 

  59. Gymrek, M., McGuire, A. L., Golan, D., Halperin, E. & Erlich, Y. Identifying personal genomes by surname inference. Science 339, 321–324 (2013).

    Article  CAS  PubMed  Google Scholar 

  60. Aguilar-Mahecha, A. et al. Making personalized medicine a reality: the challenges of a modern translational research biopsy-driven program in an academic setting: the Segal Cancer Center experience. J. Med. Person. 9, 104–111 (2011).

    Article  Google Scholar 

  61. Ricci, D. S. et al. Global requirements for DNA sample collections: results of a survey of 204 ethics committees in 40 countries. Clin. Pharmacol. Ther. 89, 554–561 (2011).

    Article  CAS  PubMed  Google Scholar 

  62. Katona, T. M. et al. Genetically heterogeneous and clonally unrelated metastases may arise in patients with cutaneous melanoma. Am. J. Surg. Pathol. 31, 1029–1037 (2007).

    Article  PubMed  Google Scholar 

  63. Liegl, B. et al. Heterogeneity of kinase inhibitor resistance mechanisms in GIST. J. Pathol. 216, 64–74 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Taniguchi, K., Okami, J., Kodama, K., Higashiyama, M. & Kato, K. Intratumor heterogeneity of epidermal growth factor receptor mutations in lung cancer and its correlation to the response to gefitinib. Cancer Sci. 99, 929–935 (2008).

    Article  CAS  PubMed  Google Scholar 

  65. Yancovitz, M. et al. Intra- and inter-tumor heterogeneity of BRAF(V600E) mutations in primary and metastatic melanoma. PLoS ONE 7, e29336 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Maley, C. C. et al. Genetic clonal diversity predicts progression to esophageal adenocarcinoma. Nat. Genet. 38, 468–473 (2006).

    Article  CAS  PubMed  Google Scholar 

  67. Mehra, R. et al. Characterization of bone metastases from rapid autopsies of prostate cancer patients. Clin. Cancer Res. 17, 3924–3932 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Aguilar-Mahecha, A. et al. Study of pre-analytical variables in plasma and breast biopsies to be used for proteomic and genomic studies [online], Biorepositories and Biospecimen Research Branch http://biospecimens.cancer.gov/meeting/brnsymposium/2011/Posters/Aguilar-508.pdf (2011).

    Google Scholar 

  69. Diaz, Z. et al. Next-generation biobanking of metastases to enable multidimensional molecular profiling in personalized medicine. Mod. Pathol. http://dx.doi.org/10.1038/modpathol.2013.81.

  70. Pusztai, L. et al. Gene expression profiles obtained from fine-needle aspirations of breast cancer reliably identify routine prognostic markers and reveal large-scale molecular differences between estrogen-negative and estrogen-positive tumors. Clin. Cancer Res. 9, 2406–2415 (2003).

    CAS  PubMed  Google Scholar 

  71. Willems, S. M., van Deurzen, C. H. & van Diest, P. J. Diagnosis of breast lesions: fine-needle aspiration cytology or core needle biopsy? A review. J. Clin. Pathol. 65, 287–292 (2012).

    Article  CAS  PubMed  Google Scholar 

  72. von Renteln, D. et al. A novel flexible cryoprobe for EUS-guided pancreatic biopsies. Gastrointest. Endosc. 77, 784–792 (2013).

    Article  PubMed  Google Scholar 

  73. Hetzel, J. et al. Cryobiopsy increases the diagnostic yield of endobronchial biopsy: a multicentre trial. Eur. Respir. J. 39, 685–690 (2012).

    Article  CAS  PubMed  Google Scholar 

  74. Schumann, C. et al. Cryoprobe biopsy increases the diagnostic yield in endobronchial tumor lesions. J. Thorac. Cardiovasc. Surg. 140, 417–421 (2010).

    Article  PubMed  Google Scholar 

  75. Masuda, N., Ohnishi, T., Kawamoto, S., Monden, M. & Okubo, K. Analysis of chemical modification of RNA from formalin-fixed samples and optimization of molecular biology applications for such samples. Nucleic Acids Res. 27, 4436–4443 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Srinivasan, M., Sedmak, D. & Jewell, S. Effect of fixatives and tissue processing on the content and integrity of nucleic acids. Am. J. Pathol. 161, 1961–1971 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Klockenbusch, C., O'Hara, J. E. & Kast, J. Advancing formaldehyde cross-linking towards quantitative proteomic applications. Anal. Bioanal. Chem. 404, 1057–1067 (2012).

    Article  CAS  PubMed  Google Scholar 

  78. Reis, P. P. et al. mRNA transcript quantification in archival samples using multiplexed, color-coded probes. BMC Biotechnol. 11, 46 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Malkov, V. A. et al. Multiplexed measurements of gene signatures in different analytes using the Nanostring nCounter Assay System. BMC Res. Notes 2, 80 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  80. Holley, T. et al. Deep clonal profiling of formalin fixed paraffin embedded clinical samples. PLoS ONE 7, e50586 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Tuononen, K. et al. Comparison of targeted next-generation sequencing (NGS) and real-time PCR in the detection of EGFR, KRAS, and BRAF mutations on formalin-fixed, paraffin-embedded tumor material of non-small cell lung carcinoma-superiority of NGS. Genes Chromosomes Cancer 52, 503–511 (2013).

    Article  CAS  PubMed  Google Scholar 

  82. Florell, S. R. et al. Preservation of RNA for functional genomic studies: a multidisciplinary tumor bank protocol. Mod. Pathol. 14, 116–128 (2001).

    Article  CAS  PubMed  Google Scholar 

  83. Hatzis, C. et al. Effects of tissue handling on RNA integrity and microarray measurements from resected breast cancers. J. Natl Cancer Inst. 103, 1871–1883 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Chowdary, D. et al. Prognostic gene expression signatures can be measured in tissues collected in RNAlater preservative. J. Mol. Diagn. 8, 31–39 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Przybytkowski, E., Aguilar-Mahecha, A., Nabavi, S., Tonellato, P. J. & Basik, M. Ultradense array CGH and discovery of micro-copy number alterations and gene fusions in the cancer genome. Methods Mol. Biol. 973, 15–38 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Belloni, B. et al. Will PAXgene substitute formalin? A morphological and molecular comparative study using a new fixative system. J. Clin. Pathol. 66, 124–135 (2013).

    Article  CAS  PubMed  Google Scholar 

  87. Kap, M. et al. Histological assessment of PAXgene tissue fixation and stabilization reagents. PLoS ONE 6, e27704 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Groelz, D. et al. Non-formalin fixative versus formalin-fixed tissue: a comparison of histology and RNA quality. Exp. Mol. Pathol. 94, 188–194 (2013).

    Article  CAS  PubMed  Google Scholar 

  89. Botling, J. & Micke, P. Biobanking of fresh frozen tissue from clinical surgical specimens: transport logistics, sample selection, and histologic characterization. Methods Mol. Biol. 675, 299–306 (2011).

    Article  CAS  PubMed  Google Scholar 

  90. Ellis, M. et al. Development and validation of a method for using breast core needle biopsies for gene expression microarray analyses. Clin. Cancer Res. 8, 1155–1166 (2002).

    CAS  PubMed  Google Scholar 

  91. Ono, M. et al. Tumor-infiltrating lymphocytes are correlated with response to neoadjuvant chemotherapy in triple-negative breast cancer. Breast Cancer Res. Treat. 132, 793–805 (2012).

    Article  CAS  PubMed  Google Scholar 

  92. Loi, S. et al. Prognostic and predictive value of tumor-infiltrating lymphocytes in a phase III randomized adjuvant breast cancer trial in node-positive breast cancer comparing the addition of docetaxel to doxorubicin with doxorubicin-based chemotherapy: BIG 02–98 J. Clin. Oncol. 31, 860–867 (2013).

    Article  CAS  PubMed  Google Scholar 

  93. Cancer Genome Atlas Research Network. Integrated genomic analyses of ovarian carcinoma. Nature 474, 609–615 (2011).

  94. Cancer Genome Atlas Research Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature 487, 330–337 (2012).

  95. Biankin, A. V. et al. Pancreatic cancer genomes reveal aberrations in axon guidance pathway genes. Nature 491, 399–405 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Esgueva, R. et al. Next-generation prostate cancer biobanking: toward a processing protocol amenable for the International Cancer Genome Consortium. Diagn. Mol. Pathol. 21, 61–68 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Golubeva, Y., Salcedo, R., Mueller, C., Liotta, L. A. & Espina, V. Laser capture microdissection for protein and NanoString RNA analysis. Methods Mol. Biol. 931, 213–257 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Yuan, Y. et al. Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling. Sci. Transl. Med. 4, 157ra143 (2012).

    Article  PubMed  Google Scholar 

  99. Ki, D. H. et al. Whole genome analysis for liver metastasis gene signatures in colorectal cancer. Int. J. Cancer 121, 2005–2012 (2007).

    Article  CAS  PubMed  Google Scholar 

  100. Türeci, O. et al. Computational dissection of tissue contamination for identification of colon cancer-specific expression profiles. FASEB J. 17, 376–385 (2003).

    Article  PubMed  Google Scholar 

  101. Kotorashvili, A. et al. Effective DNA/RNA co-extraction for analysis of microRNAs, mRNAs, and genomic DNA from formalin-fixed paraffin-embedded specimens. PLoS ONE 7, e34683 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Soares, A. R., Pereira, P. M. & Santos, M. A. Next-generation sequencing of miRNAs with Roche 454 GS-FLX technology: steps for a successful application. Methods Mol. Biol. 822, 189–204 (2012).

    Article  CAS  PubMed  Google Scholar 

  103. Moore, H. M., Compton, C. C., Alper, J. & Vaught, J. B. International approaches to advancing biospecimen science. Cancer Epidemiol. Biomarkers Prev. 20, 729–732 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  104. Pazzagli, M. et al. SPIDIA-RNA: first external quality assessment for the pre-analytical phase of blood samples used for RNA based analyses. Methods 59, 20–31 (2013).

    Article  CAS  PubMed  Google Scholar 

  105. Moore, H. M. The NCI Biospecimen Research Network. Biotech. Histochem. 87, 18–23 (2012).

    Article  CAS  PubMed  Google Scholar 

  106. National Cancer Institute. Biospecimen Research Database [online], (2013).

  107. Nature Publishing Group. About protocol exchange. Protocol Exchange [online], (2013).

  108. Aguilar-Mahecha, A., Kuzyk, M. A., Domanski, D., Borchers, C. H. & Basik, M. The effect of pre-analytical variability on the measurement of MRM-MS-based mid- to high-abundance plasma protein biomarkers and a panel of cytokines. PLoS ONE 7, e38290 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Begley, C. G. & Ellis, L. M. Drug development: Raise standards for preclinical cancer research. Nature 483, 531–533 (2012).

    Article  CAS  PubMed  Google Scholar 

  110. Simeon-Dubach, D., Burt, A. D. & Hall, P. A. Quality really matters: the need to improve specimen quality in biomedical research. J. Pathol. http://dx.doi.org/10.1002/path.4117 (2012).

  111. Ransohoff, D. F. & Gourlay, M. L. Sources of bias in specimens for research about molecular markers for cancer. J. Clin. Oncol. 28, 698–704 (2010).

    Article  PubMed  Google Scholar 

  112. Taube, S. E. et al. A perspective on challenges and issues in biomarker development and drug and biomarker codevelopment. J. Natl Cancer Inst. 101, 1453–1463 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  113. Silberman, S. Libraries of flesh: the sorry state of human tissue storage. WIRED Magazine (24 May 2010).

    Google Scholar 

  114. Massett, H. A. et al. Assessing the need for a standardized cancer HUman Biobank (caHUB): findings from a national survey with cancer researchers. J. Natl Cancer Inst. Monogr. 2011, 8–15 (2011).

    Article  PubMed  Google Scholar 

  115. Betsou, F. et al. Standard preanalytical coding for biospecimens: defining the sample PREanalytical code. Cancer Epidemiol. Biomarkers Prev. 19, 1004–1011 (2010).

    Article  CAS  PubMed  Google Scholar 

  116. Moore, H. M. et al. Biospecimen reporting for improved study quality (BRISQ). Cancer Cytopathol. 119, 92–101 (2011).

    Article  PubMed  Google Scholar 

  117. Strand, C., Enell, J., Hedenfalk, I. & Fernö, M. RNA quality in frozen breast cancer samples and the influence on gene expression analysis--a comparison of three evaluation methods using microcapillary electrophoresis traces. BMC Mol. Biol. 8, 38 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  118. Kennedy, R. D. et al. Development and independent validation of a prognostic assay for stage II colon cancer using formalin-fixed paraffin-embedded tissue. J. Clin. Oncol. 29, 4620–4626 (2011).

    Article  PubMed  Google Scholar 

  119. Georgiou, C. D., Papapostolou, I. & Grintzalis, K. Protocol for the quantitative assessment of DNA concentration and damage (fragmentation and nicks). Nat. Protoc. 4, 125–131 (2009).

    Article  CAS  PubMed  Google Scholar 

  120. Esserman, L. J. et al. Chemotherapy response and recurrence-free survival in neoadjuvant breast cancer depends on biomarker profiles: results from the I-SPY 1 TRIAL (CALGB 150007/150012; ACRIN 6657). Breast Cancer Res. Treat. 132, 1049–1062 (2012).

    Article  CAS  PubMed  Google Scholar 

  121. Nadkarni, P. M., Kemp, R. & Parikh, C. R. Leveraging a clinical research information system to assist biospecimen data and workflow management: a hybrid approach. J. Clin. Bioinforma 1, 22 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  122. Welinder, C. et al. Establishing a Southern Swedish Malignant Melanoma OMICS and biobank clinical capability. Clin. Transl. Med. 2, 7 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  123. National Cancer Informatics Program. NCIP Launch Meeting Summary Report (05/31/2012). National Cancer Informatics Program Launch Meeting [online], (2012).

  124. UC Santa Cruz. UCSC Cancer Genomics Browser [online], (2013).

  125. Goldman, M. et al. The UCSC Cancer Genomics Browser: update 2013. Nucleic Acids Res. 41, D949–D954 (2013).

    Article  CAS  PubMed  Google Scholar 

  126. Schroeder, M. P., Gonzalez-Perez, A. & Lopez-Bigas, N. Visualizing multidimensional cancer genomics data. Genome Med. 5, 9 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  127. Gomez-Roca, C. A. et al. Sequential research-related biopsies in phase I trials: acceptance, feasibility and safety. Ann. Oncol. 23, 1301–1306 (2012).

    Article  CAS  PubMed  Google Scholar 

  128. Baker, M. Biorepositories: Building better biobanks. Nature 486, 141–146 (2012).

    Article  CAS  PubMed  Google Scholar 

  129. Matzke, E. A. M. et al. Certification for biobanks: the program developed by the Canadian Tumor Repository Network (CTRNet). Biopreserv. Biobank. 10, 426–432 (2012).

    Article  PubMed  Google Scholar 

  130. Harris, J. R. et al. Toward a roadmap in global biobanking for health. Eur. J. Hum. Genet. 20, 1105–1111 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  131. Watson, R. W., Kay, E. W. & Smith, D. Integrating biobanks: addressing the practical and ethical issues to deliver a valuable tool for cancer research. Nat. Rev. Cancer 10, 646–651 (2010).

    Article  CAS  PubMed  Google Scholar 

  132. Fenstermacher, D. A., Wenham, R. M., Rollison, D. E. & Dalton, W. S. Implementing personalized medicine in a cancer center. Cancer J. 17, 528–536 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  133. Yachida, S. & Iacobuzio-Donahue, C. A. Evolution and dynamics of pancreatic cancer progression. Oncogene http://dx.doi.org/10.1038/onc.2013.29 (2013).

  134. Nik-Zainal, S. et al. The life history of 21 breast cancers. Cell 149, 994–1007 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  135. Giordano, A. & Cristofanilli, M. CTCs in metastatic breast cancer. Recent Results Cancer Res. 195, 193–201 (2012).

    Article  PubMed  Google Scholar 

  136. Jacot, W. et al. Lack of EGFR-activating mutations in European patients with triple-negative breast cancer could emphasise geographic and ethnic variations in breast cancer mutation profiles. Breast Cancer Res. 13, R133 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  137. Rodig, S. J. et al. Unique clinicopathologic features characterize ALK-rearranged lung adenocarcinoma in the western population. Clin. Cancer Res. 15, 5216–5223 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  138. Calvo, E. & Baselga, J. Ethnic differences in response to epidermal growth factor receptor tyrosine kinase inhibitors. J. Clin. Oncol. 24, 2158–2163 (2006).

    Article  CAS  PubMed  Google Scholar 

  139. Mudur, G. Indian scientists object to export of human biological material for research. BMJ 325, 990 (2002).

    Article  PubMed Central  Google Scholar 

  140. Barker, A. D. et al. I-SPY 2: an adaptive breast cancer trial design in the setting of neoadjuvant chemotherapy. Clin. Pharmacol. Ther. 86, 97–100 (2009).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors would like to recognize the Interventional Radiology Department at the Jewish General Hospital (especially Andre Constantin and Errol Camlioglu), the Pathology Group from Hôpital du Saint-Sacrement (especially Benoit Têtu and Michèle Orain) and the Jewish General Hospital (Adrian Gologan and Tina Haliotis) and the laboratory of Koren Mann, for contributing their expertise to the collection and processing of high-quality biospecimens. The authors would like to thank both Thérèse Gagnon-Kugler and Suzan McNamara of the Québec Clinical Research Organization in Cancer for their valuable comments in drafting the manuscript. We would like to acknowledge support from the FRQS-Réseau de Recherche sur le Cancer, Genome Québec and the Québec Breast Cancer Foundation.

Author information

Authors and Affiliations

  1. Departments of Surgery and Oncology, Jewish General Hospital, McGill University, 3755 Côte Sainte Catherine Road, Montreal, H3T 1E2, QC, Canada

    Mark Basik

  2. Department of Oncology, Jewish General Hospital, McGill University, 3755 Côte Sainte Catherine Road, Montreal, H3T 1E2, QC, Canada

    Adriana Aguilar-Mahecha

  3. Québec Clinical Research Organization in Cancer (Q-CROC), Jewish General Hospital, McGill University, 3755 Côte Sainte Catherine Road, Montreal, H3T 1E2, QC, Canada

    Caroline Rousseau & Zuanel Diaz

  4. Department of Pathology, Jewish General Hospital, McGill University, 3755 Côte Sainte Catherine Road, Montreal, H3T 1E2, QC, Canada

    Alan Spatz

  5. Departments of Oncology, Epidemiology, Biostatistics and Occupational Health, and Human Genetics, Jewish General Hospital, McGill University, 3755 Côte Sainte Catherine Road, Montreal, H3T 1E2, QC, Canada

    Celia M. T. Greenwood

  6. Department of Oncology, Segal Cancer Centre, Jewish General Hospital, McGill University, 3755 Côte Sainte Catherine Road, Montreal, H3T 1E2, QC, Canada

    Gerald Batist

  7. Department of Digestive Oncology and Center for Human Genetics, University Hospital Gasthuisberg, Herestraat 49, Leuven, B-3000, Belgium

    Sabine Tejpar

Authors
  1. Mark Basik
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Adriana Aguilar-Mahecha
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Caroline Rousseau
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zuanel Diaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sabine Tejpar
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Alan Spatz
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Celia M. T. Greenwood
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Gerald Batist
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

A. Aguila-Mahecha, M. Basik, G. Batist, Z. Diaz and C. Rousseau researched data and wrote the manuscript. C. M. T. Greenwood, A. Spatz and S. Tejpar made substantial contribution to discussion of content. All authors reviewed and edited the manuscript before submission.

Corresponding author

Correspondence to Gerald Batist.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Basik, M., Aguilar-Mahecha, A., Rousseau, C. et al. Biopsies: next-generation biospecimens for tailoring therapy. Nat Rev Clin Oncol 10, 437–450 (2013). https://doi.org/10.1038/nrclinonc.2013.101

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrclinonc.2013.101

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing