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.

  • Review Article
  • Published:

Synthetic biomarkers: a twenty-first century path to early cancer detection

Nature Reviews Cancer volume 21, pages 655–668 (2021)Cite this article

Subjects

Abstract

Detection of cancer at an early stage when it is still localized improves patient response to medical interventions for most cancer types. The success of screening tools such as cervical cytology to reduce mortality has spurred significant interest in new methods for early detection (for example, using non-invasive blood-based or biofluid-based biomarkers). Yet biomarkers shed from early lesions are limited by fundamental biological and mass transport barriers — such as short circulation times and blood dilution — that limit early detection. To address this issue, synthetic biomarkers are being developed. These represent an emerging class of diagnostics that deploy bioengineered sensors inside the body to query early-stage tumours and amplify disease signals to levels that could potentially exceed those of shed biomarkers. These strategies leverage design principles and advances from chemistry, synthetic biology and cell engineering. In this Review, we discuss the rationale for development of biofluid-based synthetic biomarkers. We examine how these strategies harness dysregulated features of tumours to amplify detection signals, use tumour-selective activation to increase specificity and leverage natural processing of bodily fluids (for example, blood, urine and proximal fluids) for easy detection. Finally, we highlight the challenges that exist for preclinical development and clinical translation of synthetic biomarker diagnostics.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Challenges associated with detecting early-stage tumours.
Fig. 2: Activity-based synthetic biomarkers enrich tumour protease signatures.
Fig. 3: Small-molecule probes sense tumour-associated enzymatic activity.
Fig. 4: Genetically encoded synthetic biomarkers leverage tumour-specific cues to achieve detectable signals.
Fig. 5: Characteristics of synthetic biomarkers for early-stage cancer detection.

Similar content being viewed by others

References

  1. Siegel, R. L., Miller, K. D. & Jemal, A. Cancer statistics, 2020. CA Cancer J. Clin. 70, 7–30 (2020).

    Article  PubMed  Google Scholar 

  2. National Lung Screening Trial Research Team. Reduced lung-cancer mortality with low-dose computed tomographic screening. N. Engl. J. Med. 365, 395–409 (2011).

    Article  Google Scholar 

  3. Ru Zhao, Y. et al. NELSON lung cancer screening study. Cancer Imaging 11 Spec. No. A, S79–S84 (2011).

  4. Siu, A. L. Screening for breast cancer: U.S. Preventive Services Task Force recommendation statement. Ann. Intern. Med. 164, 279–296 (2016).

    Article  PubMed  Google Scholar 

  5. Bibbins-Domingo, K. et al. Screening for colorectal cancer: US Preventive Services Task Force recommendation statement. JAMA 315, 2564–2575 (2016).

    Article  CAS  PubMed  Google Scholar 

  6. Curry, S. J. et al. Screening for cervical cancer: US Preventive Services Task Force recommendation statement. JAMA 320, 674–686 (2018).

    Article  PubMed  Google Scholar 

  7. Moyer, V. A. Screening for lung cancer: U.S. Preventive Services Task Force recommendation statement. Ann. Intern. Med. 160, 330–338 (2014).

    PubMed  Google Scholar 

  8. Pinsky, P. F., Prorok, P. C. & Kramer, B. S. Prostate cancer screening - a perspective on the current state of the evidence. N. Engl. J. Med. 376, 1285–1289 (2017).

    Article  PubMed  Google Scholar 

  9. Cohen, J. D. et al. Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science 359, 926–930 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Au, S. H. et al. Clusters of circulating tumor cells: a biophysical and technological perspective. Curr. Opin. Biomed. Eng. 3, 13–19 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Bettegowda, C. et al. Detection of circulating tumor DNA in early- and late-stage human malignancies. Sci. Transl. Med. 6, 224ra224 (2014).

    Article  CAS  Google Scholar 

  12. Maheswaran, S. et al. Detection of mutations in EGFR in circulating lung-cancer cells. N. Engl. J. Med. 359, 366–377 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. 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 

  14. Lennon, A. M. et al. Feasibility of blood testing combined with PET-CT to screen for cancer and guide intervention. Science 369, eabb9601 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. De Rubis, G., Rajeev Krishnan, S. & Bebawy, M. Liquid biopsies in cancer diagnosis, monitoring, and prognosis. Trends Pharmacol. Sci. 40, 172–186 (2019).

    Article  PubMed  CAS  Google Scholar 

  16. Sokoll, L. J. et al. A prospective, multicenter, National Cancer Institute Early Detection Research Network study of [-2]proPSA: improving prostate cancer detection and correlating with cancer aggressiveness. Cancer Epidemiol. Biomarkers Prev. 19, 1193–1200 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Karlsen, M. A. et al. Evaluation of HE4, CA125, risk of ovarian malignancy algorithm (ROMA) and risk of malignancy index (RMI) as diagnostic tools of epithelial ovarian cancer in patients with a pelvic mass. Gynecol. Oncol. 127, 379–383 (2012).

    Article  PubMed  Google Scholar 

  18. Jaiswal, S. et al. Age-related clonal hematopoiesis associated with adverse outcomes. N. Engl. J. Med. 371, 2488–2498 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Prensner, J. R., Rubin, M. A., Wei, J. T. & Chinnaiyan, A. M. Beyond PSA: the next generation of prostate cancer biomarkers. Sci. Transl. Med. 4, 127rv123 (2012).

    Article  CAS  Google Scholar 

  20. Hammarström, S. The carcinoembryonic antigen (CEA) family: structures, suggested functions and expression in normal and malignant tissues. Semin. Cancer Biol. 9, 67–81 (1999).

    Article  PubMed  Google Scholar 

  21. Imperiale, T. F., Ransohoff, D. F. & Itzkowitz, S. H. Multitarget stool DNA testing for colorectal-cancer screening. N. Engl. J. Med. 371, 187–188 (2014).

    CAS  PubMed  Google Scholar 

  22. Serganova, I. & Blasberg, R. G. Molecular imaging with reporter genes: has its promise been delivered? J. Nucl. Med. 60, 1665–1681 (2019).

    Article  CAS  PubMed  Google Scholar 

  23. Gilad, A. A. & Shapiro, M. G. Molecular imaging in synthetic biology, and synthetic biology in molecular imaging. Mol. Imaging Biol. 19, 373–378 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Condeelis, J. & Weissleder, R. In vivo imaging in cancer. Cold Spring Harb. Perspect. Biol. 2, a003848 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Kwong, G. A. et al. Mass-encoded synthetic biomarkers for multiplexed urinary monitoring of disease. Nat. Biotechnol. 31, 63–70 (2013). This study first describes the design of protease-activated synthetic biomarkers for non-invasive detection of colorectal cancer from urine in a mouse model.

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Heitzer, E., Haque, I. S., Roberts, C. E. S. & Speicher, M. R. Current and future perspectives of liquid biopsies in genomics-driven oncology. Nat. Rev. Genet. 20, 71–88 (2019). This comprehensive review highlights the opportunities as well as the many challenges that must be overcome before liquid biopsies can be widely used for cancer detection.

    Article  CAS  PubMed  Google Scholar 

  28. Fleischhacker, M. & Schmidt, B. Circulating nucleic acids (CNAs) and cancer — a survey. Biochim. Biophys. Acta Rev. Cancer 1775, 181–232 (2007).

    Article  CAS  Google Scholar 

  29. Diehl, F. et al. Circulating mutant DNA to assess tumor dynamics. Nat. Med. 14, 985–990 (2008).

    Article  CAS  PubMed  Google Scholar 

  30. Erdi, Y. E. Limits of tumor detectability in nuclear medicine and PET. Mol. Imaging Radionucl. Ther. 21, 23–28 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Lutz, A. M., Willmann, J. K., Cochran, F. V., Ray, P. & Gambhir, S. S. Cancer screening: a mathematical model relating secreted blood biomarker levels to tumor sizes. PLoS Med. 5, e170 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Hori, S. S. & Gambhir, S. S. Mathematical model identifies blood biomarker-based early cancer detection strategies and limitations. Sci. Transl. Med. 3, 109ra116 (2011). In this study, the authors develop a mathematical model to determine how early a clinical blood biomarker can be used to detect cancer.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Hori, S. S., Lutz, A. M., Paulmurugan, R. & Gambhir, S. S. A model-based personalized cancer screening strategy for detecting early-stage tumors using blood-borne biomarkers. Cancer Res. 77, 2570–2584 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Machiraju, G. B., Mallick, P. & Frieboes, H. B. Multicompartment modeling of protein shedding kinetics during vascularized tumor growth. Sci. Rep. 10, 16709 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Birkbak, N. J. & McGranahan, N. Cancer genome evolutionary trajectories in metastasis. Cancer Cell 37, 8–19 (2020).

    Article  CAS  PubMed  Google Scholar 

  36. Lambert, A. W., Pattabiraman, D. R. & Weinberg, R. A. Emerging biological principles of metastasis. Cell 168, 670–691 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Massague, J. & Obenauf, A. C. Metastatic colonization by circulating tumour cells. Nature 529, 298–306 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Turajlic, S. & Swanton, C. Metastasis as an evolutionary process. Science 352, 169–175 (2016).

    Article  CAS  PubMed  Google Scholar 

  39. Labidi-Galy, S. I. et al. High grade serous ovarian carcinomas originate in the fallopian tube. Nat. Commun. 8, 1093 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Conner, J. R. et al. Outcome of unexpected adnexal neoplasia discovered during risk reduction salpingo-oophorectomy in women with germ-line BRCA1 or BRCA2 mutations. Gynecol. Oncol. 132, 280–286 (2014).

    Article  PubMed  Google Scholar 

  41. Lopez-Giacoman, S. & Madero, M. Biomarkers in chronic kidney disease, from kidney function to kidney damage. World J. Nephrol. 4, 57–73 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Ishizawa, T. et al. Real-time identification of liver cancers by using indocyanine green fluorescent imaging. Cancer 115, 2491–2504 (2009).

    Article  PubMed  Google Scholar 

  43. Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).

    Article  CAS  Google Scholar 

  44. Kwon, E. J., Dudani, J. S. & Bhatia, S. N. Ultrasensitive tumour-penetrating nanosensors of protease activity. Nat. Biomed. Eng. 1, 0054 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Kessenbrock, K., Plaks, V. & Werb, Z. Matrix metalloproteinases: regulators of the tumor microenvironment. Cell 141, 52–67 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Ribatti, D., Nico, B., Crivellato, E., Roccaro, A. M. & Vacca, A. The history of the angiogenic switch concept. Leukemia 21, 44–52 (2007).

    Article  CAS  PubMed  Google Scholar 

  47. Dudani, J. S., Ibrahim, M., Kirkpatrick, J., Warren, A. D. & Bhatia, S. N. Classification of prostate cancer using a protease activity nanosensor library. Proc. Natl Acad. Sci. USA 115, 8954–8959 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Kirkpatrick, J. D. et al. Urinary detection of lung cancer in mice via noninvasive pulmonary protease profiling. Sci. Transl. Med. 12, eaaw0262 (2020). This study demonstrates the use of a 14-plex library of synthetic biomarkers for early detection of lung cancer in genetically engineered mouse models.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Mac, Q. D. et al. Non-invasive early detection of acute transplant rejection via nanosensors of granzyme B activity. Nat. Biomed. Eng. 3, 281–291 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Lin, K. Y., Kwong, G. A., Warren, A. D., Wood, D. K. & Bhatia, S. N. Nanoparticles that sense thrombin activity as synthetic urinary biomarkers of thrombosis. ACS Nano 7, 9001–9009 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Kwong, G. A. et al. Mathematical framework for activity-based cancer biomarkers. Proc. Natl Acad. Sci. USA 112, 12627–12632 (2015). This study develops a physiologically based pharmacokinetic model to predict the performance of protease-activated synthetic biomarkers for early cancer detection in humans.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Dudani, J. S., Warren, A. D. & Bhatia, S. N. Harnessing protease activity to improve cancer care. Annu. Rev. Canc Biol. 2, 353–376 (2018).

    Article  Google Scholar 

  53. Soo Choi, H. et al. Renal clearance of quantum dots. Nat. Biotechnol. 25, 1165 (2007).

    Article  CAS  Google Scholar 

  54. Dudani, J. S., Jain, P. K., Kwong, G. A., Stevens, K. R. & Bhatia, S. N. Photoactivated spatiotemporally-responsive nanosensors of in vivo protease activity. ACS Nano 9, 11708–11717 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Wittrup, K. D., Thurber, G. M., Schmidt, M. M. & Rhoden, J. J. Practical theoretic guidance for the design of tumor-targeting agents. Methods Enzymol. 503, 255–268 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Mac, Q. D. et al. Activity-based urinary biomarkers of response and resistance to checkpoint blockade immunotherapy. bioRxiv https://doi.org/10.1101/2020.12.10.420265 (2021).

    Article  Google Scholar 

  57. Hao, L., Zhao, R. T., Ngambenjawong, C., Fleming, H. E. & Bhatia, S. N. CRISPR-Cas-amplified urine biomarkers for multiplexed and portable cancer diagnostics. bioRxiv https://doi.org/10.1101/2020.06.17.157180 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Warren, A. D. et al. Disease detection by ultrasensitive quantification of microdosed synthetic urinary biomarkers. J. Am. Chem. Soc. 136, 13709–13714 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Warren, A. D., Kwong, G. A., Wood, D. K., Lin, K. Y. & Bhatia, S. N. Point-of-care diagnostics for noncommunicable diseases using synthetic urinary biomarkers and paper microfluidics. Proc. Natl Acad. Sci. USA 111, 3671–3676 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Chan, L. W. et al. Engineering synthetic breath biomarkers for respiratory disease. Nat. Nanotechnol. 15, 792–800 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Loynachan, C. N. et al. Renal clearable catalytic gold nanoclusters for in vivo disease monitoring. Nat. Nanotechnol. 14, 883–890 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Nishihara, T. et al. Beta-galactosidase-responsive synthetic biomarker for targeted tumor detection. Chem. Commun. 54, 11745–11748 (2018).

    Article  CAS  Google Scholar 

  63. Nishihara, T. et al. Synthetic biomarker design by using analyte-responsive acetaminophen. Chembiochem 18, 910–913 (2017).

    Article  CAS  PubMed  Google Scholar 

  64. Fernandez-Garcia, J., Altea-Manzano, P., Pranzini, E. & Fendt, S. M. Stable isotopes for tracing mammalian-cell metabolism in vivo. Trends Biochem. Sci. 45, 185–201 (2020).

    Article  CAS  PubMed  Google Scholar 

  65. Perets, T. T. et al. Optimization of 13C-urea breath test threshold levels for the detection of Helicobacter pylori infection in a national referral laboratory. J. Clin. Lab. Anal. 33, e22674 (2019).

    Article  PubMed  CAS  Google Scholar 

  66. Gorowska-Kowolik, K., Chobot, A. & Kwiecien, J. 13C methacetin breath test for assessment of microsomal liver function: methodology and clinical application. Gastroenterol. Res. Pract. 2017, 7397840 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Hoteit, M. A. et al. Deterioration in liver function after liver-directed therapy for hepatocellular carcinoma measured by cholate clearance. GastroHep 2, 232–239 (2020).

    Article  Google Scholar 

  68. Hanna, G. B., Boshier, P. R., Markar, S. R. & Romano, A. Accuracy and methodologic challenges of volatile organic compound-based exhaled breath tests for cancer diagnosis: a systematic review and meta-analysis. JAMA Oncol. 5, e182815 (2019).

    Article  PubMed  Google Scholar 

  69. Djago, F., Lange, J. & Poinot, P. Induced volatolomics of pathologies. Nat. Rev. Chem. 5, 183–196 (2021).

    Article  CAS  Google Scholar 

  70. Lange, J. et al. Volatile organic compound based probe for induced volatolomics of cancers. Angew. Chem. Int. Ed. 58, 17563–17566 (2019). The authors of this study report the use of a deuterated metabolite that is released as a VOC in exhaled breath for cancer diagnosis in mice.

    Article  CAS  Google Scholar 

  71. Bhang, H. E., Gabrielson, K. L., Laterra, J., Fisher, P. B. & Pomper, M. G. Tumor-specific imaging through progression elevated gene-3 promoter-driven gene expression. Nat. Med. 17, 123–129 (2011). In this report of a vector-based synthetic biomarker, the authors demonstrate the use of a tumour-specific promoter to drive the production of a reporter to image disseminated cancer in mouse models of melanoma and breast cancer.

    Article  CAS  PubMed  Google Scholar 

  72. Browne, A. W. et al. Cancer screening by systemic administration of a gene delivery vector encoding tumor-selective secretable biomarker expression. PLoS ONE 6, e19530 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Warram, J. M. et al. Systemic delivery of a breast cancer-detecting adenovirus using targeted microbubbles. Cancer Gene Ther. 19, 545–552 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Warram, J. M., Borovjagin, A. V. & Zinn, K. R. A genetic strategy for combined screening and localized imaging of breast cancer. Mol. Imaging Biol. 13, 452–461 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  75. D’Souza, A. L. et al. A strategy for blood biomarker amplification and localization using ultrasound. Proc. Natl Acad. Sci. USA 106, 17152–17157 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Aalipour, A. et al. Engineered immune cells as highly sensitive cancer diagnostics. Nat. Biotechnol. 37, 531–539 (2019). This study describes engineered macrophages as immune cell sensors that detect cancer after infiltration by releasing a synthetic biomarker in response to metabolic polarization.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Ronald, J. A., Chuang, H.-Y., Dragulescu-Andrasi, A., Hori, S. S. & Gambhir, S. S. Detecting cancers through tumor-activatable minicircles that lead to a detectable blood biomarker. Proc. Natl Acad. Sci. USA 112, 3068–3073 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Niu, G. & Chen, X. Molecular imaging with activatable reporter systems. Theranostics 2, 413–423 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Montano-Samaniego, M., Bravo-Estupinan, D. M., Mendez-Guerrero, O., Alarcon-Hernandez, E. & Ibanez-Hernandez, M. Strategies for targeting gene therapy in cancer cells with tumor-specific promoters. Front. Oncol. 10, 605380 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  80. Tannous, B. A. & Teng, J. Secreted blood reporters: insights and applications. Biotechnol. Adv. 29, 997–1003 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Chao, C. N. et al. Gene therapy for human lung adenocarcinoma using a suicide gene driven by a lung-specific promoter delivered by JC virus-like particles. PLoS ONE 11, e0157865 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  82. Wu, C. et al. Combinatorial control of suicide gene expression by tissue-specific promoter and microRNA regulation for cancer therapy. Mol. Ther. 17, 2058–2066 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Jafri, M. A., Ansari, S. A., Alqahtani, M. H. & Shay, J. W. Roles of telomeres and telomerase in cancer, and advances in telomerase-targeted therapies. Genome Med. 8, 69 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  84. Jiang, H. et al. Arginine deiminase expressed in vivo, driven by human telomerase reverse transcriptase promoter, displays high hepatoma targeting and oncolytic efficiency. Oncotarget 8, 37694–37704 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  85. Kyo, S., Takakura, M., Fujiwara, T. & Inoue, M. Understanding and exploiting hTERT promoter regulation for diagnosis and treatment of human cancers. Cancer Sci. 99, 1528–1538 (2008).

    Article  CAS  PubMed  Google Scholar 

  86. Li, C. et al. MR molecular imaging of tumors based on an optimal hTERT promoter tyrosinase expression system. Oncotarget 7, 42474–42484 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  87. Berger, J., Hauber, J., Hauber, R., Geiger, R. & Cullen, B. R. Secreted placental alkaline phosphatase: a powerful new quantitative indicator of gene expression in eukaryotic cells. Gene 66, 1–10 (1988).

    Article  CAS  PubMed  Google Scholar 

  88. Bettan, M., Darteil, R. & Scherman, D. Secreted human placental alkaline phosphatase as a reporter gene for in vivo gene transfer. Anal. Biochem. 271, 187–189 (1999).

    Article  CAS  PubMed  Google Scholar 

  89. Bao, R., Selvakumaran, M. & Hamilton, T. C. Use of a surrogate marker (human secreted alkaline phosphatase) to monitor in vivo tumor growth and anticancer drug efficacy in ovarian cancer xenografts. Gynecol. Oncol. 78, 373–379 (2000).

    Article  CAS  PubMed  Google Scholar 

  90. Nilsson, E. E. et al. An in vivo mouse reporter gene (human secreted alkaline phosphatase) model to monitor ovarian tumor growth and response to therapeutics. Cancer Chemother. Pharmacol. 49, 93–100 (2002).

    Article  CAS  PubMed  Google Scholar 

  91. Richter, J. R., Mahoney, M., Warram, J. M., Samuel, S. & Zinn, K. R. A dual-reporter, diagnostic vector for prostate cancer detection and tumor imaging. Gene Ther. 21, 897–902 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Hiramatsu, N. et al. Alkaline phosphatase vs luciferase as secreted reporter molecules in vivo. Anal. Biochem. 339, 249–256 (2005).

    Article  CAS  PubMed  Google Scholar 

  93. Tannous, B. A. Gaussia luciferase reporter assay for monitoring biological processes in culture and in vivo. Nat. Protoc. 4, 582–591 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Wurdinger, T. et al. A secreted luciferase for ex vivo monitoring of in vivo processes. Nat. Methods 5, 171–173 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Alessandrini, F., Ceresa, D., Appolloni, I., Marubbi, D. & Malatesta, P. Noninvasive monitoring of glioma growth in the mouse. J. Cancer 7, 1791–1797 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Chung, E. et al. Secreted Gaussia luciferase as a biomarker for monitoring tumor progression and treatment response of systemic metastases. PLoS ONE 4, e8316 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  97. Tseng, A. W., Akerstrom, V., Chen, C., Breslin, M. B. & Lan, M. S. Detection of neuroendocrine tumors using promoter-specific secreted Gaussia luciferase. Int. J. Oncol. 48, 173–180 (2016).

    Article  CAS  PubMed  Google Scholar 

  98. Liu, S. H. et al. BIRC5 is a target for molecular imaging and detection of human pancreatic cancer. Cancer Lett. 457, 10–19 (2019).

    Article  CAS  PubMed  Google Scholar 

  99. Fang, Y., Wolfson, B. & Godbey, W. T. Non-invasive detection of bladder cancer via expression-targeted gene delivery. J. Gene Med. 19, 366–375 (2017).

    Article  CAS  PubMed  Google Scholar 

  100. Almeida, A. M., Queiroz, J. A., Sousa, F. & Sousa, A. Minicircle DNA: the future for DNA-based vectors? Trends Biotechnol. 38, 1047–1051 (2020).

    Article  CAS  PubMed  Google Scholar 

  101. Oliveira, P. H. & Mairhofer, J. Marker-free plasmids for biotechnological applications - implications and perspectives. Trends Biotechnol. 31, 539–547 (2013).

    Article  CAS  PubMed  Google Scholar 

  102. Huyn, S. T. et al. A potent, imaging adenoviral vector driven by the cancer-selective mucin-1 promoter that targets breast cancer metastasis. Clin. Cancer Res. 15, 3126–3134 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Wang, T., Chen, Y. & Ronald, J. A. A novel approach for assessment of prostate cancer aggressiveness using survivin-driven tumour-activatable minicircles. Gene Ther. 26, 177–186 (2019).

    Article  CAS  PubMed  Google Scholar 

  104. Reagan, M. R. & Kaplan, D. L. Concise review: mesenchymal stem cell tumor-homing: detection methods in disease model systems. Stem Cell 29, 920–927 (2011).

    Article  CAS  Google Scholar 

  105. Liu, L. et al. Exogenous marker-engineered mesenchymal stem cells detect cancer and metastases in a simple blood assay. Stem Cell Res. Ther. 6, 181 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  106. Karp, J. M. & Leng Teo, G. S. Mesenchymal stem cell homing: the devil is in the details. Cell Stem Cell 4, 206–216 (2009).

    Article  CAS  PubMed  Google Scholar 

  107. Droujinine, I. A., Eckert, M. A. & Zhao, W. To grab the stroma by the horns: from biology to cancer therapy with mesenchymal stem cells. Oncotarget 4, 651–664 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  108. Kouidhi, S., Noman, M. Z., Kieda, C., Elgaaied, A. B. & Chouaib, S. Intrinsic and tumor microenvironment-induced metabolism adaptations of T cells and impact on their differentiation and function. Front. Immunol. 7, 114 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  109. Somasundaram, R. et al. Tumor-associated B-cells induce tumor heterogeneity and therapy resistance. Nat. Commun. 8, 607 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  110. Vitale, M., Cantoni, C., Pietra, G., Mingari, M. C. & Moretta, L. Effect of tumor cells and tumor microenvironment on NK-cell function. Eur. J. Immunol. 44, 1582–1592 (2014).

    Article  CAS  PubMed  Google Scholar 

  111. Smith, T. T. et al. In situ programming of leukaemia-specific T cells using synthetic DNA nanocarriers. Nat. Nanotechnol. 12, 813–820 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Perez, C., Gruber, I. & Arber, C. Off-the-shelf allogeneic T cell therapies for cancer: opportunities and challenges using naturally occurring “universal” donor T cells. Front. Immunol. 11, 583716 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Forbes, N. S. Engineering the perfect (bacterial) cancer therapy. Nat. Rev. Cancer 10, 785–794 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Zheng, J. H. et al. Two-step enhanced cancer immunotherapy with engineered Salmonella Typhimurium secreting heterologous flagellin. Sci. Transl. Med. 9, eaak9537 (2017).

    Article  PubMed  CAS  Google Scholar 

  115. Jiang, S. N. et al. Inhibition of tumor growth and metastasis by a combination of Escherichia coli-mediated cytolytic therapy and radiotherapy. Mol. Ther. 18, 635–642 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Zhou, S., Gravekamp, C., Bermudes, D. & Liu, K. Tumour-targeting bacteria engineered to fight cancer. Nat. Rev. Cancer 18, 727–743 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Panteli, J. T., Van Dessel, N. & Forbes, N. S. Detection of tumors with fluoromarker-releasing bacteria. Int. J. Cancer 146, 137–149 (2020).

    Article  CAS  PubMed  Google Scholar 

  118. Panteli, J. T., Forkus, B. A., Van Dessel, N. & Forbes, N. S. Genetically modified bacteria as a tool to detect microscopic solid tumor masses with triggered release of a recombinant biomarker. Integr. Biol. 7, 423–434 (2015).

    Article  CAS  Google Scholar 

  119. Danino, T. et al. Programmable probiotics for detection of cancer in urine. Sci. Transl. Med. 7, 289ra284–289ra284 (2015). The authors of this study engineer the probiotic E. coli Nissle to colonize and report on the presence of liver tumours in mice by producing a colorimetric readout in urine.

    Article  CAS  Google Scholar 

  120. Slomovic, S., Pardee, K. & Collins, J. J. Synthetic biology devices for in vitro and in vivo diagnostics. Proc. Natl Acad. Sci. USA 112, 14429–14435 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Danino, T., Mondragon-Palomino, O., Tsimring, L. & Hasty, J. A synchronized quorum of genetic clocks. Nature 463, 326–330 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Prindle, A. et al. A sensing array of radically coupled genetic ‘biopixels’. Nature 481, 39–44 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  123. Din, M. O. et al. Synchronized cycles of bacterial lysis for in vivo delivery. Nature 536, 81–85 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. Chowdhury, S. et al. Programmable bacteria induce durable tumor regression and systemic antitumor immunity. Nat. Med. 25, 1057–1063 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Gurbatri, C. R. et al. Engineered probiotics for local tumor delivery of checkpoint blockade nanobodies. Sci. Transl. Med. 12, eaax0876 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Ho, C. L. et al. Engineered commensal microbes for diet-mediated colorectal-cancer chemoprevention. Nat. Biomed. Eng. 2, 27–37 (2018).

    Article  CAS  PubMed  Google Scholar 

  127. Brown, P. O. & Palmer, C. The preclinical natural history of serous ovarian cancer: defining the target for early detection. PLoS Med. 6, e1000114 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  128. Rakhit, C. P. et al. Early detection of pre-malignant lesions in a KRASG12D-driven mouse lung cancer model by monitoring circulating free DNA. Dis. Model Mech. 12, dmm036863 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. Whitney, M. et al. Parallel in vivo and in vitro selection using phage display identifies protease-dependent tumor-targeting peptides. J. Biol. Chem. 285, 22532–22541 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. Ruoslahti, E. Tumor penetrating peptides for improved drug delivery. Adv. Drug Deliv. Rev. 110–111, 3–12 (2017).

    Article  PubMed  CAS  Google Scholar 

  131. Jones, S. et al. Comparative lesion sequencing provides insights into tumor evolution. Proc. Natl Acad. Sci. USA 105, 4283–4288 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. Meza, R., Jeon, J., Moolgavkar, S. H. & Luebeck, E. G. Age-specific incidence of cancer: phases, transitions, and biological implications. Proc. Natl Acad. Sci. USA 105, 16284–16289 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. Luebeck, E. G. Cancer: genomic evolution of metastasis. Nature 467, 1053–1055 (2010).

    Article  CAS  PubMed  Google Scholar 

  135. Ittmann, M. et al. Animal models of human prostate cancer: the consensus report of the new york meeting of the mouse models of human cancers consortium prostate pathology committee. Cancer Res. 73, 2718–2736 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Puente, X. S., Sanchez, L. M., Overall, C. M. & Lopez-Otin, C. Human and mouse proteases: a comparative genomic approach. Nat. Rev. Genet. 4, 544–558 (2003).

    Article  CAS  PubMed  Google Scholar 

  137. Choi, B., Rempala, G. A. & Kim, J. K. Beyond the Michaelis–Menten equation: accurate and efficient estimation of enzyme kinetic parameters. Sci. Rep. 7, 17018 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  138. Keu, K. V. et al. Reporter gene imaging of targeted T cell immunotherapy in recurrent glioma. Sci. Transl. Med. 9, eaag2196 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  139. Widen, J. C. et al. AND-gate contrast agents for enhanced fluorescence-guided surgery. Nat. Biomed. Eng. 5, 264–277 (2021). The authors of this article show that an AND gate optical imaging probe that requires two distinct protease cleavage events significantly increased specificity and sensitivity in the detection of tumour tissue.

    Article  CAS  PubMed  Google Scholar 

  140. Ronald, J. A., D’Souza, A. L., Chuang, H. Y. & Gambhir, S. S. Artificial microRNAs as novel secreted reporters for cell monitoring in living subjects. PLoS ONE 11, e0159369 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  141. Roybal, K. T. et al. Precision tumor recognition by T cells with combinatorial antigen-sensing circuits. Cell 164, 770–779 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  142. Morsut, L. et al. Engineering customized cell sensing and response behaviors using synthetic notch receptors. Cell 164, 780–791 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  143. Holt, B. A. & Kwong, G. A. Protease circuits for processing biological information. Nat. Commun. 11, 5021 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  144. Cazanave, S. et al. SAT-281-protease activity sensors for non-invasive monitoring of NASH. J. Hepatol. 70, e760 (2019).

    Article  Google Scholar 

  145. Azeem, R. et al. Safety and tolerability in healthy volunteers of the Glympse bio test system-NASH diagnostic. Hepatology 72, 941A–942A (2020).

    Google Scholar 

  146. Whitley, M. J. et al. A mouse-human phase 1 co-clinical trial of a protease-activated fluorescent probe for imaging cancer. Sci. Transl. Med. 8, 320ra324 (2016).

    Article  CAS  Google Scholar 

  147. Unkart, J. T. et al. Intraoperative tumor detection using a ratiometric activatable fluorescent peptide: a first-in-human phase 1 study. Ann. Surg. Oncol. 24, 3167–3173 (2017).

    Article  PubMed  Google Scholar 

  148. Smith, B. L. et al. Feasibility study of a novel protease-activated fluorescent imaging system for real-time, intraoperative detection of residual breast cancer in breast conserving surgery. Ann. Surg. Oncol. 27, 1854–1861 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  149. Desnoyers, L. R. et al. Tumor-specific activation of an EGFR-targeting probody enhances therapeutic index. Sci. Transl. Med. 5, 207ra144 (2013).

    Article  PubMed  CAS  Google Scholar 

  150. US National Library of Medicine. ClinicalTrials.gov, https://ClinicalTrials.gov/show/NCT03993379 (2019).

  151. US National Library of Medicine. ClinicalTrials.gov, https://ClinicalTrials.gov/show/NCT03013491 (2017).

  152. Austin, R. J. et al. TriTACs, a novel class of T-cell-engaging protein constructs designed for the treatment of solid tumors. Mol. Cancer Ther. 20, 109–120 (2021).

    Article  CAS  PubMed  Google Scholar 

  153. US National Library of Medicine. ClinicalTrials.gov, https://ClinicalTrials.gov/show/NCT03577028 (2018).

  154. Horwitz, S. et al. Brentuximab vedotin with chemotherapy for CD30-positive peripheral T-cell lymphoma (ECHELON-2): a global, double-blind, randomised, phase 3 trial. Lancet 393, 229–240 (2019).

    Article  CAS  PubMed  Google Scholar 

  155. Duong, M. T., Qin, Y., You, S. H. & Min, J. J. Bacteria–cancer interactions: bacteria-based cancer therapy. Exp. Mol. Med. 51, 1–15 (2019).

    Article  CAS  PubMed  Google Scholar 

  156. Rozenblatt-Rosen, O. et al. The human tumor atlas network: charting tumor transitions across space and time at single-cell resolution. Cell 181, 236–249 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  157. Rajkomar, A., Dean, J. & Kohane, I. Machine learning in medicine. N. Engl. J. Med. 380, 1347–1358 (2019). This article provides a conceptual overview of the use of machine learning and its applications in medicine.

    Article  PubMed  Google Scholar 

  158. Sidey-Gibbons, J. A. M. & Sidey-Gibbons, C. J. Machine learning in medicine: a practical introduction. BMC Med. Res. Methodol. 19, 64 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  159. Madabhushi, A. & Lee, G. Image analysis and machine learning in digital pathology: challenges and opportunities. Med. Image Anal. 33, 170–175 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  160. Cui, M. & Zhang, D. Y. Artificial intelligence and computational pathology. Lab. Invest. 101, 412–422 (2021).

    Article  PubMed  Google Scholar 

  161. Golub, T. R. et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531 (1999).

    Article  CAS  PubMed  Google Scholar 

  162. Perakakis, N., Yazdani, A., Karniadakis, G. E. & Mantzoros, C. Omics, big data and machine learning as tools to propel understanding of biological mechanisms and to discover novel diagnostics and therapeutics. Metabolism 87, A1–A9 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  163. Grapov, D., Fahrmann, J., Wanichthanarak, K. & Khoomrung, S. Rise of deep learning for genomic, proteomic, and metabolomic data integration in precision medicine. OMICS 22, 630–636 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  164. Douglas, G. M. et al. Multi-omics differentially classify disease state and treatment outcome in pediatric Crohn’s disease. Microbiome 6, 13 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  165. Tasaki, S. et al. Multi-omics monitoring of drug response in rheumatoid arthritis in pursuit of molecular remission. Nat. Commun. 9, 2755 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  166. Pavel, A. B., Sonkin, D. & Reddy, A. Integrative modeling of multi-omics data to identify cancer drivers and infer patient-specific gene activity. BMC Syst. Biol. 10, 16–16 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  167. Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).

    Article  Google Scholar 

  168. van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).

    Google Scholar 

Download references

Acknowledgements

The US National Cancer Institute organized the Synthetic Biomarkers for Detection of Cancers at Incipient and Early Stages (SYNDICATE) Think Tank Meeting in 2019, where bioengineers, biologists and clinicians discussed the promises and challenges of synthetic biomarkers from development to preclinical models to scale up to meeting the ultimate goal of safe use in the clinic. The authors express their gratitude to all SYNDICATE meeting participants for their thoughts, expertise and insightful comments. They thank T. Danino (Columbia University) and L. Chan (Georgia Tech & Emory University) for insightful discussions. This work was funded by the US NIH Director’s New Innovator Award (DP2HD091793) and the National Cancer Institute R01 grant 5R01CA237210 to G.A.K. S.N.B. is a Howard Hughes Medical Institute investigator. L.G. was supported by the US National Science Foundation Graduate Research Fellowship Program (DGE-1451512). This work was performed in part at the Georgia Tech Institute for Electronics and Nanotechnology, a member of the National Nanotechnology Coordinated Infrastructure, which is supported by the National Science Foundation (grant ECCS-1542174). This content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Author information

Authors and Affiliations

  1. Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory School of Medicine, Atlanta, GA, USA

    Gabriel A. Kwong & Lena Gamboa

  2. Parker H. Petit Institute of Bioengineering and Bioscience, Atlanta, GA, USA

    Gabriel A. Kwong

  3. Institute for Electronics and Nanotechnology, Georgia Tech, Atlanta, GA, USA

    Gabriel A. Kwong

  4. The Georgia Immunoengineering Consortium, Emory University and Georgia Tech, Atlanta, GA, USA

    Gabriel A. Kwong

  5. Winship Cancer Institute, Emory University, Atlanta, GA, USA

    Gabriel A. Kwong

  6. Division of Cancer Prevention, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

    Sharmistha Ghosh, Christos Patriotis & Sudhir Srivastava

  7. Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA

    Sangeeta N. Bhatia

  8. Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA

    Sangeeta N. Bhatia

  9. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA

    Sangeeta N. Bhatia

  10. Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Sangeeta N. Bhatia

  11. Howard Hughes Medical Institute, Chevy Chase, MD, USA

    Sangeeta N. Bhatia

Authors
  1. Gabriel A. Kwong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sharmistha Ghosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lena Gamboa
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Christos Patriotis
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sudhir Srivastava
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sangeeta N. Bhatia
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

G.A.K., S.G., L.G., S.S. and S.N.B. researched the data for the article, provided substantial contributions to discussions of the content and contributed to writing the article. G.A.K., S.G., L.G., C.P., S.S. and S.N.B. reviewed and/or edited the manuscript before submission.

Corresponding authors

Correspondence to Gabriel A. Kwong, Sharmistha Ghosh, Sudhir Srivastava or Sangeeta N. Bhatia.

Ethics declarations

Competing interests

G.A.K. is a co-founder of Glympse Bio, and consults for Glympse Bio and Satellite Bio. S.N.B. is a director of Vertex, is a co-founder of and consultant for Glympse Bio, Satellite Bio and CEND Therapeutics, is a consultant for Moderna, and receives sponsored research funds from Johnson & Johnson. S.G., C.P., S.S. and L.G. declare no conflicts of interest.

Additional information

Dedication

This article is dedicated to the late Dr Sanjiv Sam Gambhir, a visionary pioneer and thought leader in bioengineering who devoted his career to developing methods for early disease detection.

Peer review information

Nature Reviews Cancer thanks the anonymous reviewers for their contribution to the peer review of this work.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Glossary

Multicompartment models

A mathematical modelling technique whereby distinct compartments are used to represent organs, tissues, blood or lymph to predict how an administered drug is absorbed, distributed, metabolized or excreted.

Hydrodynamic radius

For a macromolecule in solution, the radius of an equivalent hard sphere diffusing at the same rate as the macromolecule.

Deuterated metabolite

A compound in which one or more hydrogen atoms have been replaced by the stable isotope deuterium to distinguish it from its unmodified counterpart.

Bio-orthogonal reporters

Non-native reporters that do not interfere with biological functions.

Herpes simplex virus 1 thymidine kinase

(HSV1-TK). The enzyme expressed by the reporter gene phosphorylates radiolabelled purine and pyrimidine nucleoside analogues to trap the probe within cells and thereby allow visualization by positron emission tomography (PET).

AND gate

A Boolean logic gate operation that outputs a value of 1 if and only if both inputs are 1; otherwise it outputs 0.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kwong, G.A., Ghosh, S., Gamboa, L. et al. Synthetic biomarkers: a twenty-first century path to early cancer detection. Nat Rev Cancer 21, 655–668 (2021). https://doi.org/10.1038/s41568-021-00389-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41568-021-00389-3

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer