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OPINION article

Front. Oncol., 20 October 2016
Sec. Hematologic Malignancies

Are We Making Personalized Cancer Care Less Personalized?

  • Wake Forest Medical School, Winston Salem, NC, USA

To most physicians and their patients, personalized cancer care typically means that after identifying specific so-called “actionable” molecular changes characterizing a patient’s cancer, a highly effective therapy that specifically targets the expressed product of that abnormality is available. This is now the case for the three most common US causes of cancer death (13). Personalized cancer care also means studying the molecular nature of the tumor to estimate the benefits of adjuvant systemic therapy. For example, after surgery for most breast cancers, the National Comprehensive Cancer Network now endorses quantitative mRNA expression profiling in order to estimate the absolute benefit of adjuvant hormonal therapy or adjuvant chemotherapy followed by hormonal therapy for that patient (4).

William Osler said, “It is much more important to know what sort of person has a disease than what sort of disease a person has.” The other aspect of personalized cancer care is understanding the patient who harbors the cancer. For example, geriatric assessment tools (GATs) can be used to help more precisely estimate the risks and benefits of particular treatments in older patients (5). Similarly, assessing genomic polymorphisms can be used to estimate certain risks and benefits of specific therapies in particular patients (6).

There is a danger that results from relying too heavily on these tools to direct patients in decision making. For example, we have all cared for patients who choose against a highly effective targeted agent and other patients who wish to pursue therapies even when the tools mentioned above offer a far more dysphemistic prediction of efficacy and risk. This is because what informs patient decisions is often far more intangible and relates to the fundamental values, desires, and the nature of a particular patient.

Futurists predict that soon computer applications will be capable of making nearly all medicine-related treatment decisions. However, I doubt there will ever be a laboratory or clinical tool that can estimate the love for family that motivates a patient to pursue options, against all odds, in order to be there for a marriage, graduation, or birth of a grandchild. We will never “personalize” cancer care to help patients quantify the beauty of a poem or song and appreciate the wonder of an intelligent conversation or the joy of sharing laughter with a friend. The promise of truly personalized care rests on taking the time to listen to the value patients place on these intangibles as they consider their options in the context of our increased and remarkable ability to more precisely analyze the tumor and other more concrete patient-related factors.

Author Contributions

The author confirms being the sole contributor of this work and approved it for publication.

Conflict of Interest Statement

There are no relevant conflicts related to this publication.

References

1. Lynch TJ, Bell DW, Sordella R, Gurubhagavatula S, Okimoto RA, Brannigan BW, et al. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N Engl J Med (2004) 350:2129–39. doi:10.1056/NEJMoa040938

PubMed Abstract | CrossRef Full Text | Google Scholar

2. Borghaei H, Paz-Ares L, Horn L, Spigel DR, Steins M, Ready NE, et al. Nivolumab versus docetaxel in advanced nonsquamous non-small-cell lung cancer. N Engl J Med (2015) 373:1627–39. doi:10.1056/NEJMoa1507643

CrossRef Full Text | Google Scholar

3. Le DT, Uram JN, Wang H, Bartlett BR, Kemberling H, Eyring AD, et al. PD-1 blockade in tumors with mismatch-repair deficiency. N Engl J Med (2015) 372:2509–20. doi:10.1056/NEJMoa1500596

CrossRef Full Text | Google Scholar

4. Gradishar WJ, Anderson BO, Balassanian R, et al. Breast Cancer Guideline in the National Comprehensive Cancer Network Guideline Version 2. (2016).

Google Scholar

5. Hurria A, Wildes T, Bammgartner J, et al. Older Adult Oncology Guideline in the National Comprehensive Cancer Network Guidelines Version 1. (2016).

Google Scholar

6. Stoehlmacher J, Park DJ, Zhang W, Yang D, Groshen S, Zahedy S, et al. A multivariate analysis of genomic polymorphisms: prediction of clinical outcome to 5-FU/oxaliplatin combination chemotherapy in refractory colorectal cancer. Br J Cancer (2004) 91:344–54. doi:10.1038/sj.bjc.6601975

CrossRef Full Text | Google Scholar

Keywords: personalized medicine, cancer, therapies, predictive modeling, decision making

Citation: Sorscher S (2016) Are We Making Personalized Cancer Care Less Personalized? Front. Oncol. 6:220. doi: 10.3389/fonc.2016.00220

Received: 29 June 2016; Accepted: 05 October 2016;
Published: 20 October 2016

Edited by:

Varsha Gandhi, University of Texas MD Anderson Cancer Center, USA

Reviewed by:

Pierluigi Porcu, Ohio State University, USA
Jan Trøst Jørgensen, Dx-Rx Institute, Denmark

Copyright: © 2016 Sorscher. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Steven Sorscher, ssorsche@wakehealth.edu

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.