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.

  • Analysis
  • Published:

Quantifying factors for the success of stratified medicine

Nature Reviews Drug Discovery volume 10, pages 817–833 (2011)Cite this article

Subjects

Key Points

  • Co-developing a drug with a diagnostic to create a stratified medicine presents challenges for product developers, regulators, payers and clinicians. With the aim of developing a shared framework and tools for understanding the impact of these challenges, here we present an analysis using data and modelling from case studies in oncology and Alzheimer's disease. Key findings are summarized below.

  • A prospective stratified development and market-based approach created positive net economic value for the drug developer in all three case studies examined.

  • A stratification approach following an all-comers trial does not always create value owing to the time delays and increased costs it incurs.

  • Three key factors from a stratified medicine approach serve as the most crucial determinants of the potential economic value for a developer: the therapeutic effect, predictive biomarker prevalence and the clinical performance of the companion diagnostic.

  • Relatively small study sizes (< 300 patients) are required to discover predictive biomarkers with similar performance characteristics of the KRAS mutational status (for epidermal growth factor receptor inhibitors) or HER2 gene expression level (for trastuzumab).

  • Co-developing a stratified medicine presents multiple challenges, including an inherent timing mismatch, because the science underlying the predictive biomarker usually trails that of the therapeutic, and a relatively low economic value is typically associated with the companion diagnostic.

  • Multiple variable simulations demonstrate that developmental, regulatory and commercial factors are frequently multiplicative rather than additive, resulting in complementary virtuous or spiralling negative cascades of the economic value for the developer of the therapeutic.

  • This article illustrates how such analyses can aid the coordination of diagnostic and drug development, and the selection of optimal development and commercialization strategies. It also illustrates the interplay of key factors on the economic feasibility of a stratified medicine, which may have important implications for public policy makers.

Abstract

Co-developing a drug with a diagnostic to create a stratified medicine — a therapy that is targeted to a specific patient population on the basis of a clinical characteristic such as a biomarker that predicts treatment response — presents challenges for product developers, regulators, payers and physicians. With the aim of developing a shared framework and tools for addressing these challenges, here we present an analysis using data from case studies in oncology and Alzheimer's disease, coupled with integrated computational modelling of clinical outcomes and developer economic value, to quantify the effects of decisions related to key issues such as the design of clinical trials. This illustrates how such analyses can aid the coordination of diagnostic and drug development, and the selection of optimal development and commercialization strategies. It also illustrates the impact of the interplay of these factors on the economic feasibility of stratified medicine, which has important implications for public policy makers.

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

Figure 1: Oncology case studies.
Figure 2: Alzheimer's disease case study.
Figure 3: Alternative scenarios for the Alzheimer's disease case study.
Figure 4: Alternative scenarios for oncology case studies.
Figure 5: Scenario-based analysis of development path and biomarker attributes.
Figure 6: Simulations of contrasting futures for stratified medicine.

Similar content being viewed by others

References

  1. Trusheim, M., Berndt, E. & Douglas, F. Stratified medicine: strategic and economic implications of combining drugs and clinical biomarkers. Nature Rev. Drug Discov. 6, 287–293 (2007).

    Article  CAS  Google Scholar 

  2. Hu, S. X. et al. Pharmacogenomics and personalized medicine: mapping of future value creation. BioTechniques 39, S1–S6 (2005).

    Article  PubMed  Google Scholar 

  3. Aspinall, M. G. & Hamermesh, R. G. Realizing the promise of personalized medicine. Harvard Bus. Rev. 85, 108–117 (2007).

    Google Scholar 

  4. Phillips, K. A. et al. Diagnostics and biomarker development: priming the pipeline. Nature Rev. Drug Discov. 5, 463–469 (2006).

    Article  CAS  Google Scholar 

  5. US Food and Drug Administration. Table of Pharmacogenomic Biomarkers in Drug Labels. FDA website [online], (2011).

  6. Loupakis, F. et al. EGF-receptor targeting with monoclonal antibodies in colorectal carcinomas: rationale for a pharmacogenomic approach. Pharmacogenomics 9, 55–69 (2008).

    Article  CAS  PubMed  Google Scholar 

  7. Hughes, B. Developing tools for stratified medicine. Nature Rev. Drug Discov. 8, 919–920 (2009).

    Article  CAS  Google Scholar 

  8. Hamburg, M. A. & Collins, F. S. The path to personalized medicine. N. Eng. J. Med. 363, 301–304 (2010).

    Article  CAS  Google Scholar 

  9. Jensen, E. V. et al. Estrogen receptors and breast cancer response to adrenalectomy. Natl Cancer Inst. Monogr. 34, 55–70 (1971).

    CAS  PubMed  Google Scholar 

  10. Sawyers, C. L. The cancer biomarker problem. Nature 452, 548–552 (2008).

    Article  CAS  PubMed  Google Scholar 

  11. Hudis, C. A. Trastuzumab — mechanism of action and use in clinical practice. N. Engl. J. Med. 357, 39–51 (2007).

    Article  CAS  PubMed  Google Scholar 

  12. Slamon, D. J. et al. Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2. N. Engl. J. Med. 344, 783–792 (2001).

    Article  CAS  PubMed  Google Scholar 

  13. Vogel, C. L. & Franco, S. X. Clinical experience with trastuzumab (Herceptin). Breast J. 9, 452–462 (2003).

    Article  CAS  PubMed  Google Scholar 

  14. US Food and Drug Administration. FDA labeling information — Trastuzumab. FDA website [online], (2008).

  15. Romond, E. H. et al. Trastuzumab plus adjuvant chemotherapy for operable HER2-positive breast cancer. N. Engl. J. Med. 353, 1673–1684 (2005).

    Article  CAS  PubMed  Google Scholar 

  16. Piccart-Gebhart, M. J. Trastuzumab after adjuvant chemotherapy in HER2-positive breast cancer. N. Engl. J. Med. 353, 1659–1672 (2005).

    Article  CAS  PubMed  Google Scholar 

  17. US Food and Drug Administration. FDA labeling information — Panitumumab. FDA website [online], (2009).

  18. Cunningham, D. et al. Cetuximab monotherapy and cetuximab plus irinotecan in irinotecan-refractory metastatic colorectal cancer. N. Engl. J. Med. 351, 337–345 (2004).

    Article  CAS  PubMed  Google Scholar 

  19. Adams, R. & Maughan, T. Predicting response to epidermal growth factor receptor-targeted therapy in colorectal cancer. Expert Rev. Anticancer Ther. 7, 503–518 (2007).

    Article  CAS  PubMed  Google Scholar 

  20. Chung, K. Y. et al. Cetuximab shows activity in colorectal cancer patients with tumors that do not express the epidermal growth factor receptor by immunohistochemistry. J. Clin. Oncol. 23, 1803–1810 (2005).

    Article  CAS  PubMed  Google Scholar 

  21. Mitchell, E. P. et al. Panitumumab activity in metastatic colorectal cancer (mCRC) patients (pts) with low or negative tumor epidermal growth factor receptor (EGFr) levels: an updated analysis. J. Clin. Oncol. Abstr. 25, 4082 (2007).

    Google Scholar 

  22. Amado, R. G. et al. Wild-type KRAS is required for panitumumab efficacy in patients with metastatic colorectal cancer. J. Clin. Oncol. 26, 1626–1634 (2008).

    Article  CAS  PubMed  Google Scholar 

  23. Davis, J. C. et al. The microeconomics of personalized medicine: today's challenge and tomorrow's promise. Nature Rev. Drug. Discov. 8, 279–286 (2009).

    Article  CAS  Google Scholar 

  24. Jackson, C. & Snyder, P. Electroencephalography and event-related potentials as biomarkers of mild cognitive impairment and mild Alzheimer's disease. Alzheimers Dement. 4, S137–S143 (2008).

    Article  PubMed  Google Scholar 

  25. Alzheimer's Association. 2010 Alzheimer's Disease Facts and Figures. Alzheimers Dement. 6, 158–194 (2010).

  26. Geldmacher, D. S. Treatment guidelines for Alzheimer's disease: redefining perceptions in primary care. Prim. Care Companion J. Clin. Psychiatry 9, 113–121 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Rockwood, K. et al. The clinical meaningfulness of ADAS-cog changes in Alzheimer's disease patients treated with donepezil in an open-label trial. BMC Neurol. 30, 26 (2007).

    Article  Google Scholar 

  28. Dorfman, N. The Age of Alzheimer's. Medical Marketing & Media [online], (2006).

    Google Scholar 

  29. Mattson, N. et al. CSF biomarkers and incipient Alzheimer disease in patients with mild cognitive impairment. JAMA 302, 385–393 (2009).

    Article  Google Scholar 

  30. Blennow, K. & Zetterberg, H. Use of CSF biomarkers in Alzheimer's disease clinical trials. J. Nutr. Health Aging 13, 358–361 (2009).

    Article  CAS  PubMed  Google Scholar 

  31. Thai, L. J. et al. The role of biomarkers in clinical trials for Alzheimer's Disease. Alzheimer Dis. Assoc. Disord. 20, 6–15 (2006).

    Article  Google Scholar 

  32. Salloway, S. et al. A phase 2 multiple ascending dose trial of bapineuzumab in mild to moderate Alzheimer disease. Neurology 73, 2061–2070 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Risner, M. et al. Efficacy of rosiglitazone in a genetically defined population with mild-to-moderate Alzheimer's disease. Pharmacogenomics J. 6, 246–254 (2006).

    Article  CAS  PubMed  Google Scholar 

  34. Slamon, D. J. et al. Human breast cancer: correlation of relapse and survival with amplification of the HER-2/neu oncogene. Science 235, 177–182 (1987).

    Article  CAS  PubMed  Google Scholar 

  35. Turner, N., Tutt, A. & Ashworth, A. Hallmarks of “BRCAness” in sporadic cancers. Nature Rev. Cancer 4, 1–6 (2004).

    Article  Google Scholar 

  36. Soda, M. et al. Identification of the transforming EML4–ALK fusion gene in non-small cell lung cancer. Nature 448, 561–566 (2007).

    Article  CAS  PubMed  Google Scholar 

  37. Chiarle, R. et al. The anaplastic lymphoma kinase in the pathogenesis of cancer. Nature Rev. Cancer 8, 11–23 (2008).

    Article  CAS  Google Scholar 

  38. Kwak, E. L. et al. Anaplastic lymphoma kinase inhibition in non-small-cell lung cancer. N. Engl. J. Med. 363, 1693–1703 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Butrynski. et al. Crizotinib in ALK-rearranged inflammatory myofibroblastic tumor. N. Engl. J. Med. 363, 1727–1733 (2010).

  40. US Food and Drug Administration. FDA labeling information — Daptomycin. FDA website [online], (2010).

  41. Mermel, L. A. et al. Clinical practice guidelines for the diagnosis and management of intravascular catheter-related infection: 2009 Update by the Infectious Diseases Society of America. Clin. Infect. Dis. 49, 1–45 (2009).

    Article  CAS  PubMed  Google Scholar 

  42. Pratilas, C. A. & Solit, D. B. Targeting the mitogen-activated protein kinase pathway: physiological feedback and drug response. Clin. Cancer Res. 16, 3329–3334 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Solit, D. B. et al. BRAF mutation predicts sensitivity to MEK inhibitors. Nature 439, 358–362 (2006).

    CAS  PubMed  Google Scholar 

  44. Kwak, E. L. et al. Clinical activity of the oral ALK inhibitor PF-02341066 in ALK-positive patients with non-small cell lung cancer (NSCLC). J. Clin. Oncol. Abstr. 28, 3 (2010).

    Article  Google Scholar 

  45. McDermott, U. et al. Genomic alterations of anaplastic lymphoma kinase may sensitize tumors to anaplastic lymphoma kinase inhibitors. Cancer Res. 68, 3389–3395 (2008).

    Article  CAS  PubMed  Google Scholar 

  46. Fine, B. M. & Amler, L. Predictive biomarkers in the development of oncology drugs: a therapeutic industry perspective. Clin. Pharmacol. Ther. 85, 535–538 (2009).

    Article  CAS  PubMed  Google Scholar 

  47. Taube, S. 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 

  48. Mandrekar, S. J. & Sargent, D. J. Clinical trial designs for predictive biomarker validation: theoretical considerations and practical challenges. J. Clin. Oncol. 27, 4027–4034 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Simon, R. The use of genomics in clinical trial design. Clin. Cancer Res. 14, 5984–5993 (2008).

    Article  PubMed  Google Scholar 

  50. Peeters, M. et al. Use of massively parallel, next-generation sequencing to identify gene mutations beyond KRAS that predict response to panitumumab in a randomized, phase 3, monotherapy study of metastatic colorectal cancer (mCRC). in Proceedings of the 101st Annual Meeting of the American Association for Cancer Research (17–21 Apr 2010; Washington DC; Abstract LB-174).

    Google Scholar 

  51. Nixon, R. M. et al. The rheumatoid arthritis drug development model: a case study in Bayesian clinical trial simulation. Pharm. Stat. 8, 371–389 (2009).

    Article  PubMed  Google Scholar 

  52. Simon, R. M., Paik, S & Hayes, D. F. Use of archived specimens in evaluation of prognostic and predictive biomarkers. J. Natl Cancer Inst. 101 1446–1452 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Raponi, M. et al. KRAS mutations predict response to EGFR inhibitors. Curr. Opin. Pharmacol. 8, 413–418 (2008).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors would like to thank L. Surh, now retired from GlaxoSmithKline, who helped guide and review their efforts. The authors would also like to thank I. Saulea and D. Smith, both of SDG Life Sciences, for their support in the analysis of the trastuzumab case study.

Author information

Author notes

  1. Sean Xinghua Hu, Aiden A. Flynn, Abhijit Mazumder & Federico Goodsaid

    Present address: Present addresses: Bionest Partners, 14 Wall Street, 20th floor, New York 10005, USA (S.X.H.); Exploristics, Cromac Square, Belfast BT24 8LA, Northern Ireland, UK (A.A.F.); Janssen Pharmaceutical Companies of Johnson & Johnson, 700 US Highway 202, Raritan, New Jersey 08869, USA (A.M.); Vertex Pharmaceuticals, 1201 Maryland Avenue, SW Suite 850, Washington DC 20024, USA (F.G.).,

  2. Mark R. Trusheim, Breon Burgess, Sean Xinghua Hu and Theresa Long: These authors contributed equally to this work.

Authors and Affiliations

  1. Massachusetts Institute of Technology, Sloan School of Management and Center for Biomedical Innovation, 100 Main Street, Cambridge, 02139, Massachusetts, USA

    Mark R. Trusheim & Ernst R. Berndt

  2. Merck & Co., 351 N. Sumneytown Pike North Wales, Pennsylvania, 19454, USA

    Breon Burgess & Abhijit Mazumder

  3. IMS Health, 485 Lexington Avenue, New York, 10017, USA

    Sean Xinghua Hu

  4. Chinese National Human Genome Center at Shanghai, Chinese Academy of Sciences, 250 BiBo Road, Zhangjiang District, Shanghai, China

    Sean Xinghua Hu

  5. Beijing Genomics Institute, Beishan Industrial Zone, Yantian District, Shenzhen, China

    Sean Xinghua Hu

  6. Van Andel Institute, 333 Bostwick Avenue Northeast, Grand Rapids, 49503, Michigan, USA

    Theresa Long

  7. Bristol-Myers Squibb, Route 206 & Province Line Road, Princeton, 08543, New Jersey, USA

    Steven D. Averbuch & David Swank

  8. GlaxoSmithKline UK, Stockley Park West, Uxbridge, UB11 1BT, Middlesex, UK

    Aiden A. Flynn & Alfons Lieftucht

  9. Merck & Co., One Merck Drive, Whitehouse Station, 08889, New Jersey, USA

    Judy Milloy

  10. Merck & Co., P.O. BOX 4, 770 Sumneytown Pike, West Point, 19486, Pennsylvania, USA

    Peter M. Shaw

  11. Eli Lilly & Company, Lilly Corporate Center, Indianapolis, 46285, Indiana, USA

    Jian Wang

  12. US Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, 20903, Maryland, USA

    Federico Goodsaid

  13. Adaptive Pharmacogenomics, 45 South Park Place, PMB 178, Morristown, 07960, New Jersey, USA

    Michael C. Palmer

Authors
  1. Mark R. Trusheim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Breon Burgess
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sean Xinghua Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Theresa Long
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Steven D. Averbuch
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Aiden A. Flynn
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Alfons Lieftucht
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Abhijit Mazumder
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Judy Milloy
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Peter M. Shaw
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. David Swank
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Jian Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Ernst R. Berndt
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Federico Goodsaid
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Michael C. Palmer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mark R. Trusheim.

Ethics declarations

Competing interests

Some authors, as noted by their affiliations, are employed by pharmaceutical developers, diagnostic developers or by consulting firms that have such firms as clients. The authors who are affiliated with the Massachusetts Institute of Technology have received funding support from pharmaceutical firms for other work, but not this manuscript.

Supplementary information

Supplementary information S1 (box)

Modelling tools used (PDF 1767 kb)

Supplementary information S2 (table)

Herceptin adjuvant therapy economic modeling input assumptions and sources used in IMS and MIT model comparison (PDF 318 kb)

Supplementary information S3 (model)

(XLS 1285 kb)

Supplementary information S4 (model)

(XLS 1284 kb)

Related links

Related links

FURTHER INFORMATION

ClinicalTrials.gov website (list results for search 'bapineuzumab')

EvaluatePharma website

Eisai press release (U.S. FDA Approves Aricept For Treatment of Severe Alzheimer's Disease)

MIT Center for Biomedical Innovation

PCSD tool website

Glossary

Stratified medicine

A therapeutic combined with a companion diagnostic that targets a patient subpopulation for treatment.

Companion diagnostic

A predictive biomarker that is developed into a regulatory approved and/or a commercially available diagnostic test, and may be included in a drug label.

Predictive biomarker

A baseline characteristic that categorizes patients by their likelihood of responding to a particular treatment. It may predict a favourable response or an unfavourable response (for example, an adverse event).

KRAS

An oncogene that is implicated in several types of cancer. Patients with mutations in KRAS are likely to be poor responders to EGFR inhibitors such as cetuximab and panitumumab.

Monte Carlo simulation

A stochastic modelling approach in which values for some variables are independently selected at random from defined distributions. After many iterations, distributions for resulting variables — such as the net present value — are created.

Net present value

(NPV). The sum of the discounted cash flow from a multiyear activity. A positive value indicates a value-adding investment.

Expected net present value

(eNPV). The risk-adjusted net present value accounting for the probability of technical and regulatory success.

HER2

Also known as ERBB2. HER2 is the target of the monoclonal antibody drug trastuzumab, which is indicated for patients with breast cancer on the basis of tumour HER2 expression status as assessed by companion diagnostics.

Epidermal growth factor receptor

(EGFR). A member of the human epidermal growth factor receptor family of cell-surface receptor tyrosine kinases. EGFR has an important role in cell growth, proliferation and survival. It is the target of the monoclonal antibody drugs cetuximab and panitumumab.

Time to progression

An end point of a cancer trial. Defined as the time from randomization to objective tumour progression, not including deaths. Taken from the US Food and Drug Administration Guidance: Clinical Trial Endpoints for the Approval of Cancer Drugs and Biologics, May 2007.

Overall response rate

Also known as the objective response rate. The proportion of patients with a reduction in tumour size of a predefined amount and for a minimum time period. Taken from the US Food and Drug Administration Guidance: Clinical Trial Endpoints for the Approval of Cancer Drugs and Biologics, May 2007.

Deterministic waterfall analysis

A multivariable sensitivity exercise in which selected variables are sequentially changed, with the incremental impact of each variable shown as a step from the original case to the final case in which all sensitivity variables have been changed.

ADAS-cog

The Alzheimer's Disease Assessment Scale-cognitive subscale. Consists of 11 tasks that evaluate memory, language, attention and other cognitive abilities affected by the disease.

Apolipoprotein E

(APOE). A major component of chylomicron. The APOE4 allele has been associated with a higher risk and increased severity of Alzheimer's disease. APOE3 is the normal allele, and the APOE2 allele may have some protective effects with regard to Alzheimer's disease.

Aβ42

The amyloid-β protein fragment that is a major component of the plaques that are a characteristic hallmark of Alzheimer's disease.

Magnetic resonance imaging

A medical imaging technology that uses high magnetic fields and radio frequencies to create images of internal body structures.

Positron emission tomography

A medical imaging technique that produces three-dimensional images of internal body structures by detecting γ-rays emitted by a radionucleotide-tagged biologically active molecule. Images can be sequenced to create a time series of moving images. This technique often uses a glucose analogue to measure relative metabolism rates.

Mini-mental state examination

Also known as the Folstein test, this is a brief questionnaire that is used to assess cognitive impairment.

Probability of technical success

The likelihood that a candidate product will meet its developer-prescribed efficacy end point and safety criteria.

Probability of regulatory success

The likelihood that a candidate product will be approved by a regulatory agency, given its technical performance.

Probability of technical and regulatory success

(PTRS). A probability value calculated by multiplying the probability of regulatory success with the probability of technical success.

Isoquant NPV curve

A contour line on a two-dimensional chart to show the constant value of a third dimension, in this case the net present value (NPV).

Poly(ADP-ribose) polymerase inhibitors

Drugs that interfere with poly(ADP-ribose) polymerase, a protein that is involved in DNA repair and apoptosis.

BRCA1

The gene encoding breast cancer type 1 susceptibility protein. BRCA1 and BRCA2 genes are implicated as predictors of breast and ovarian cancer.

Anaplastic lymphoma kinase

(ALK). Also known as ALK tyrosine kinase receptor or CD246 antigen. Direct mutations in the ALK gene or fusion of the ALK sequence with other genes are implicated in some forms of cancer.

ALK–EML4-positive

A term used to describe patients who express the anaplastic lymphoma kinase (ALK) gene fused with the echinoderm microtubule-associated protein-like 4 (EML4) gene.

BRAF

A gene that encodes the serine/threonine protein kinase BRAF protein. Inherited mutations in this gene can cause birth defects, whereas spontaneous mutations can cause cancer. The BRAF gene has a role in regulating mitogen-activated protein kinase kinase (MEK).

MEK

Mitogen-activated protein kinase kinase.

Positive predictive values

The proportion of patients who test positive who are truly positive.

Probability of success

The probability that a clinical trial design will detect a therapeutic effect of a given size. Also referred to as the power of the clinical trial design.

SNP

Single nucleotide polymorphism.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Trusheim, M., Burgess, B., Hu, S. et al. Quantifying factors for the success of stratified medicine. Nat Rev Drug Discov 10, 817–833 (2011). https://doi.org/10.1038/nrd3557

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrd3557

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