Abstract
There is an urgent need for blood-based, noninvasive molecular tests to assist in the detection and diagnosis of cancers in a cost-effective manner at an early stage, when curative interventions are still possible. Additionally, blood-based diagnostics can classify tumors into distinct molecular subtypes and monitor disease relapse and response to treatment. Increasingly, biomarker strategies are becoming critical to identify a specific patient subpopulation that is likely to respond to a new therapeutic agent. The improved understanding of the underlying molecular features of common cancers and the availability of a multitude of recently developed technologies to interrogate the genome, transcriptome, proteome and metabolome of tumors and biological fluids have made it possible to develop clinically applicable and cost-effective tests for many common cancers. Overall, the paradigm shift towards personalized and individualized medicine relies heavily on the increased use of diagnostic biomarkers and classifiers to improve diagnosis, management and treatment. International collaborations, involving both the private and public sector will be required to facilitate the development of clinical applications of biomarkers, using rigorous standardized assays. Here, we review the recent technological and scientific advances in this field.
Key Points
-
Future cancer care will rely on the use of biomarkers to detect cancer early and to individualize diagnostics, tumor classification and treatment selection
-
The rich content of blood provides an ideal compartment to develop noninvasive diagnostics for cancer
-
Large numbers of novel potential biomarkers have been discovered, including circulating proteins, nucleic acids, metabolites and tumor cells
-
For most candidate biomarkers, definitive validation studies for specific clinical applications are lacking
-
A side-by-side comparison of the performance of diverse biomarkers and causal networks constructed by integrating multiomic data would be useful to provide a better context for evaluating blood-based biomarkers
-
International collaborations will be required to facilitate the clinical application of biomarkers, using rigorous standardized assays and clinical studies
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Rent or buy this article
Prices vary by article type
from$1.95
to$39.95
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
Croswell, J. M., Baker, S. G., Marcus, P. M., Clapp, J. D. & Kramer, B. S. Cumulative incidence of false-positive test results in lung cancer screening: a randomized trial. Ann. Intern. Med. 152, 505–512 (2010).
Chubak, J., Boudreau, D. M., Fishman, P. A. & Elmore, J. G. Cost of breast-related care in the year following false positive screening mammograms. Med. Care 48, 815–820 (2010).
Henschke, C. I. et al. Early Lung Cancer Action Project: overall design and findings from baseline screening. Lancet 354, 99–105 (1999).
Henschke, C. et al. Early Lung Cancer Action Project: initial findings on repeat screenings. Cancer 92, 153–159 (2001).
Kaneko, M. et al. Peripheral lung cancer: screening and detection with low-dose spiral CT versus radiography. Radiology 201, 798–802 (1996).
Sobue, T. et al. Screening for lung cancer with low-dose helical computed tomography: anti-lung cancer association project. J. Clin. Oncol. 20, 911–920 (2002).
Sone, S. et al. Results of three-year mass screening programme for lung cancer using mobile low-dose spiral computed tomography scanner. Br. J. Cancer 84, 25–32 (2001).
Li, F., Sone, S., Abe, H., Macmahon, H. & Doi, K. Malignant versus benign nodules at CT screenings for lung cancer: comparison of thin-section CT findings. Radiology 223, 793–798 (2004).
Swenson, S. J. et al. Lung cancer screening with CT: Mayo Clinic Experience. Radiology 226, 756–761 (2003).
Swensen, S. J. et al. CT screening for lung cancer: five-year prospective experience. Radiology 235, 259–265 (2005).
Diederich, S. et al. Screening for early lung cancer with low-dose spiral CT: prevalence in 817 asymptomatic smokers. Radiology 222, 773–781 (2002).
Nawa, T. et al. Lung cancer screening using low-dose spiral CT: results of baseline and 1-year follow-up studies. Chest 122, 15–20 (2002).
McWilliams, A. M., Mayo, J. R., Ahn, M. I., MacDonald, S. L. & Lam, S. C. Lung cancer screening using multi-slice thin-section computed tomography and autofluorescence bronchoscopy. J. Thorac. Oncol. 1, 61–68 (2006).
Pastorino, U. et al. Early lung-cancer detection with spiral CT and positron emission tomography in heavy smokers: 2-year results. Lancet 362, 593–597 (2003).
Roberts, H. C. et al. Lung cancer screening with low dose computed tomography: Canadian experience. Can. Assoc. Radiol. J. 58, 225–235 (2007).
Pepe, M. S., Feng, Z., Janes, H., Bossuyt, P. M. & Potter, J. D. Pivotal evaluation of the accuracy of a biomarker used for classification of prediction: standards for study design. J. Natl Cancer Inst. 100, 1432–1438 (2008).
Kulasingam, V. & Diamandis, E. P. Strategies for discovering novel cancer biomarkers through utilization of emerging technologies. Nat. Clin. Pract. Oncol. 5, 588–599 (2008).
Sung, H. & Cho, J. Y. Biomarkers for the lung cancer diagnosis and their advances in proteomics. BMB Rep. 41, 615–625 (2008).
Greenberg, A. K. & Lee, M. S. Biomarkers for lung cancer: clinical uses. Curr. Opin. Pulm. Med. 13, 249–255 (2007).
Kellar, K. L. & Douglass, J. P. Multiplexed microsphere-based flow cytometric immunoassays for human cytokines. J. Immunol. Methods 279, 277–285 (2003).
Lee, G. et al. Blood-based biomarker profiles for detecting lung cancer [abstract]. J. Thorac. Oncol. 5 (Suppl. 3), S220–S221 (2010).
Yee, J. et al. Connective tissue-activating peptide III: a novel blood biomarker for early lung cancer detection. J. Clin. Oncol. 27, 2787–2792 (2009).
Kulasingam, V., Pavlou, M. P. & Diamandis, E. P. Integrating high-throughput technologies in the quest for effective biomarkers for ovarian cancer. Nat. Rev. Cancer 10, 371–378 (2010).
Moore, R. G. et al. A novel multiple marker bioassay utilizing HE4 and CA125 for the prediction of ovarian cancer in patients with a pelvic mass. Gynecol. Oncol. 112, 40–46 (2009).
U.S. Food and Drug Administration. Product classification: test, epithelial ovarian tumor associated antigen (HE4) [online], (2010).
National Institutes of Health. Clinical proteomic technologies for cancer initiative (CPTC): proteome characterization centers (U24) [online], (2010).
Tan, H. T., Low, J., Lim, S. G. & Chung, M. C. M. Serum autoantibodies as biomarkers for early cancer detection. FEBS J. 276, 6880–6904 (2009).
Chapman, C. et al. Autoantibodies in breast cancer: their use as an aid to early diagnosis. Ann. Oncol. 18, 868–873 (2007).
Brichory, F., Beer, D., Le Naour, F., Giordano, T. & Hanash, S. Proteomics-based identification of protein gene product 9.5 as a tumor antigen that induces a humoral immune response in lung cancer. Cancer Res. 61, 7908–7912 (2001).
Brichory, F. M. et al. An immune response manifested by the common occurrence of annexins I and II autoantibodies and high circulating levels of IL-6 in lung cancer. Proc. Natl Acad. Sci. USA 98, 9824–9829 (2001).
Pereira-Faca, S. R. et al. Identification of 14-3-3 theta as an antigen that induces a humoral response in lung cancer. Cancer Res. 67, 12000–12006 (2007).
Qiu, J. et al. Occurrence of autoantibodies to annexin I, 14-3-3 theta and LAMR1 in prediagnostic lung cancer sera. J. Clin. Oncol. 26, 5060–5066 (2008).
International Cancer Genome Consortium. International Cancer Genome Consortium [online], (2010).
The International Cancer Genome Consortium. International network of cancer genome projects. Nature 464, 993–998 (2010).
Wellcome Trust Sanger Institute. The Cancer Genome Project [online], (2010).
National Cancer Institute. The Cancer Genome Atlas [online], (2010).
Davies, H. et al. Mutations of the BRAF gene in human cancer. Nature 417, 949–954 (2002).
Jahr, S. et al. DNA fragments in the blood plasma of cancer patients: quantitations and evidence for their origin from apoptotic and necrotic cells. Cancer Res. 61, 1659–1665 (2001).
Pathak, A. K., Bhutani, M., Kumar, S., Mohan, A. & Guleria, R. Circulating cell-free DNA in plasma/serum of lung cancer patients as a potential screening and prognostic tool. Clin. Chem. 52, 1833–1842 (2006).
Kaye, F. J. Mutation-associated fusion cancer genes in solid tumors. Mol. Cancer Ther. 8, 1399–1408 (2009).
Leary, R. J. et al. Development of personalized tumor biomarkers using massively parallel sequencing. Sci. Transl. Med. 2, 20ra14 (2010).
McBride, D. J. et al. Use of cancer-specific genomic rearrangements to quantify disease burden in plasma from patients with solid tumors. Genes Chromosomes Cancer 49, 1062–1069 (2010).
Vlassov, V. V., Laktionov, P. P. & Rykova, E. Y. Circulating nucleic acids as a potential source for cancer biomarkers. Curr. Mol. Med. 10, 142–165 (2010).
Dianxu, F. et al. A prospective study of detection of pancreatic carcinoma by combined plasma K-ras mutations and serum CA19–9 analysis. Pancreas 25, 336–341 (2002).
Maire, F. et al. Differential diagnosis between chronic pancreatitis and pancreatic cancer: value of the detection of KRAS2 mutations in circulating DNA. Br. J. Cancer 87, 551–554 (2002).
Marchese, R. et al. Low correspondence between K-ras mutations in pancreatic tissue and detection of K-ras mutations in circulating DNA. Pancreas 32, 171–177 (2006).
Fujiwara, K. et al. Identification of epigenetic aberrant promoter methylation in serum DNA is useful for early detection of lung cancer. Clin. Cancer Res. 11, 1219–1225 (2005).
Shames, D. S. et al. A genome-wide screen for promoter methylation in lung cancer identifies novel methylation markers for multiple malignancies. PLoS Med. 3, e486 (2006).
Shivapurkar, N. & Gazdar, A. F. DNA methylation based biomarkers in non-invasive cancer screening. Curr. Mol. Med. 10, 123–132 (2010).
Fleischhacker, M. & Schmidt, B. Free circulating nucleic acids in plasma and serum (CNAPS)—useful for the detection of lung cancer patients? Cancer Biomark. 6, 211–219 (2010).
Croce, C. M. & Calin, G. A. miRNAs, cancer and stem cell division. Cell 122, 6–7 (2005).
Cortez, M. A. & Calin, G. A. MicroRNA identification in plasma and serum: a new tool to diagnose and monitor diseases. Expert Opin. Biol. Ther. 9, 703–711 (2009).
Chen, X. et al. Characterization of microRNAs in serum: a novel class of biomarkers for diagnosis of cancer and other diseases. Cell Res. 18, 997–1006 (2008).
Ng, E. K. et al. Differential expression of microRNAs in plasma of patients with colorectal cancer: a potential marker for colorectal cancer screening. Gut 58, 1375–1381 (2009).
Mitchell, P. S. et al. Circulating microRNAs as stable blood-based markers for cancer detection. Proc. Natl Acad. Sci. USA 105, 10513–10518 (2008).
Hunter, M. P. et al. Detection of microRNA expression in human peripheral blood microvesicles. PLoS ONE 3, e3694 (2008).
Kosaka, N., Iguchi, H. & Ochiya, T. Circulating microRNA in body fluid: a new potential biomarker for cancer diagnosis and prognosis. Cancer Sci. 101, 2087–2092 (2010).
Claudino, W. M. et al. Metabolomics: available results, current research projects in breast cancer, and future applications. J. Clin. Oncol. 25, 2840–2846 (2007).
Serkova, N. J. & Glunde, K. in Tumor Biomarker Discovery Vol. 520 Ch. 20 (ed. Tainsky, M. A.) 273–295 (Humana Press, Totowa, 2009).
Sreekumar, A. et al. Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature 457, 910–914 (2009).
Jenzmik, F. et al. Sarcosine in urine after digital rectal examination fails as a marker in prostate cancer detection and identification of aggressive tumours. Eur. Urol. 58, 12–18 (2010).
Nordström, A. & Lewensohn, R. Metabolomics: moving to the clinic. J. Neuroimmune Pharmacol. 5, 4–17 (2010).
Schadt, E. E. et al. An integrative genomics approach to infer causal associations between gene expression and disease. Nat. Genet. 37, 710–717 (2005).
Yang, X. et al. Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks. Nat. Genet. 41, 415–423 (2009).
Pantel, K. & Alix-Panabières, C. Circulating tumour cells in cancer patients: challenges and perspectives. Trends Mol. Med. 16, 398–406 (2010).
Mego, M., Mani, S. A. & Cristofanilli, M. Molecular mechanisms of metastasis in breast cancer—clinical applications. Nat. Rev. Clin. Oncol. 7, 693–701 (2010).
Chen, Y. et al. Detection of cytokeratin 19, human mammaglobin, and carcinoembryonic antigen-positive circulating tumor cells by three-marker reverse transcription-PCR assay and its relation to clinical outcome in early breast cancer. Int. J. Biol. Markers 25, 59–68 (2010).
Chen, C. C. et al. Combination of multiple mRNA markers (PTTG1, Survivin, UbcH10 and TK1) in the diagnosis of Taiwanese patients with breast cancer by membrane array. Oncology 70, 438–446 (2006).
Shen, C., Hu, L., Xia, L. & Li, Y. The detection of circulating tumor cells of breast cancer patients by using multimarker (Survivin, hTERT and hMAM) quantitative real-time PCR. Clin. Biochem. 42, 194–200 (2009).
Alunni-Fabbroni, M. & Sandri, M. T. Circulating tumour cells in clinical practice: methods of detection and possible characterization. Methods 50, 289–297 (2010).
Pantel, K., Alix-Panabières, C. & Riethdorf, S. Cancer micrometastases. Nat. Rev. Clin. Oncol. 6, 339–351 (2009).
Baccarani, M. et al. Chronic myeloid leukemia: an update of concepts and management recommendations of European LeukemiaNet. J. Clin. Oncol. 27, 6041–6051 (2009).
Hudis, C. A. Trastuzumab—mechanism of action and use in clinical practice. N. Engl. J. Med. 357, 39–51 (2007).
Herbst, R. S., Heymach, J. V. & Lippman, S. M. Lung cancer. N. Engl. J. Med. 359, 1367–1380 (2008).
Lynch, T. J. et al. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N. Engl. J. Med. 350, 2129–2139 (2004).
Bang, Y. et al. Clinical activity of the oral ALK inhibitor PF-02341066 in ALK-positive patients with non-small cell lung cancer (NSCLC) [abstract]. J. Clin. Oncol. 28 (Suppl. 18), a3 (2010).
Tufman, A. & Huber, R. M. Biological markers in lung cancer: a clinician's perspective. Cancer Biomark. 6, 123–135 (2010).
Bearz, A. et al. MUC-1 (CA 15-3 antigen) as a highly reliable predictor of response to EGFR inhibitors in patients with bronchioalveolar carcinoma: an experience on 26 patients. Int. J. Biol. Markers 22, 307–311 (2007).
Kasahara, K. et al. Impact of serum hepatocyte growth factor on treatment response to epidermal growth factor receptor tyrosine kinase inhibitors in patients with non-small cell lung adenocarcinoma. Clin. Cancer Res. 16, 4616–4624 (2010).
Fujiwara, Y. et al. Elevated serum level of sialylated glycoprotein KL-6 predicts a poor prognosis in patients with non-small cell lung cancer treated with gefitinib. Lung Cancer 59, 81–87 (2008).
Taguchi, F. et al. Mass spectrometry to classify non-small-cell lung cancer patients for clinical outcome after treatment with epidermal growth factor receptor tyrosine kinase inhibitors: a multicohort cross-institutional study. J. Natl. Cancer Inst. 99, 838–846 (2007).
Chung, C. H. et al. Detection of tumor epidermal growth factor receptor pathway dependence by serum mass spectrometry in cancer patients. Cancer Epidemiol. Biomarkers Prev. 19, 358–365 (2010).
Amann, J. M. et al. Genetic and proteomic features associated with survival after treatment with erlotinib in first-line therapy of non-small cell lung cancer in Eastern Cooperative Oncology Group 3503. J. Thorac. Oncol. 5, 169–178 (2010).
Maheswaran, S. et al. Detection of mutations in EGFR in circulating lung-cancer cells. N. Engl. J. Med. 359, 366–377 (2008).
Sequist, L. V. et al. First-line gefitinib in patients with advanced non-small-cell lung cancer harboring somatic EGFR mutations. J. Clin. Oncol. 26, 2442–2449 (2008).
Pao, W. et al. Acquired resistance of lung adenocarcinomas to gefitinib or erlotinib is associated with a second mutation in the EGFR kinase domain. PloS Med. 2, e73 (2005).
van't Veer, L. J. et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 415, 530–536 (2002).
Wang, Y. et al. Gene-expression profiles to predict distant metastasis of lymph-node negative primary breast cancer. Lancet 365, 671–679 (2005).
Chuang, H. Y., Lee, E., Liu, Y. T., Lee, D. & Ideker, T. Network-based classification of breast cancer metastasis. Mol. Syst. Biol. 3, 140 (2007).
Director's Challenge Consortium for the Molecular Classification of Lung Adenocarcinoma et al. Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study. Nat. Med. 14, 822–827 (2008).
Raponi, M. et al. Gene expression signatures for predicting prognosis of squamous cell and adenocarcinomas of the lung. Cancer Res. 66, 7466–7472 (2006).
Zhu, J. et al. Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks. Nat. Genet. 40, 854–861 (2008).
U.S. National Institutes of Health. ClinicalTrials.gov [online], (2010).
Kimura, H. et al. Evaluation of epidermal growth factor receptor mutation status in serum DNA as a predictor of response to gefitinib (IRESSA). Br. J. Cancer 97, 778–784 (2007).
Jian, G. et al. Prediction of epidermal growth factor receptor mutations in the plasma/pleural effusion to efficacy of gefitinib treatment in advanced non-small cell lung cancer. J. Cancer Res. Clin. Oncol. 136, 1341–1347 (2010).
Iwanicki-Caron, I. et al. Usefulness of the serum carcinoembryonic antigen kinetic for chemotherapy monitoring in patients with unresectable metastasis of colorectal cancer. J. Clin. Oncol. 26, 3681–3686 (2008).
Dudek, A. Z. et al. Phase II trial of neoadjuvant therapy with carboplatin, gemcitabine plus thalidomide for stages IIB and III non-small cell lung cancer. J. Thorac. Oncol. 4, 969–975 (2009).
Karihtala, P., Mäenpää, J., Turpeenniemi-Hujanen, T. & Puistola, U. Front-line bevacizumab in serous epithelial ovarian cancer: biomarker analysis of the FINAVAST trial. Anticancer Res. 30, 1001–1006 (2010).
Hanrahan, E. O. et al. Baseline vascular endothelial growth factor concentration as a potential predictive marker of benefit from vandetanib in non-small cell lung cancer. Clin. Cancer Res. 15, 3600–3609 (2009).
Worldwide Innovative Networking in personalized cancer medicine. WIN Consortium [online], (2010).
Patz, E. F. Jr et al. Panel of serum biomarkers for the diagnosis of lung cancer. J. Clin. Oncol. 25, 5578–5583 (2007).
Fan, T. W. et al. Altered regulation of metabolic pathways in human and lung cancer discerned by (13)C stable isotope-resolved metabolomics (SIRM). Mol. Cancer 8, 41 (2009).
Author information
Authors and Affiliations
Contributions
All authors contributed to researching the data for the article, discussion of content, and writing, editing and reviewing the manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing financial interests.
Rights and permissions
About this article
Cite this article
Hanash, S., Baik, C. & Kallioniemi, O. Emerging molecular biomarkers—blood-based strategies to detect and monitor cancer. Nat Rev Clin Oncol 8, 142–150 (2011). https://doi.org/10.1038/nrclinonc.2010.220
Published:
Issue Date:
DOI: https://doi.org/10.1038/nrclinonc.2010.220
This article is cited by
-
Mass spectrometry-based proteomics as an emerging tool in clinical laboratories
Clinical Proteomics (2023)
-
Identification and evaluation of novel serum autoantibody biomarkers for early diagnosis of gastric cancer and precancerous lesion
Journal of Cancer Research and Clinical Oncology (2023)
-
Carnitine palmitoyl transferase 1A is a novel diagnostic and predictive biomarker for breast cancer
BMC Cancer (2021)
-
Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance
Nature Communications (2021)
-
A systematic review of extracellular vesicles as non-invasive biomarkers in glioma diagnosis, prognosis, and treatment response monitoring
Molecular Biology Reports (2021)