Personalized cancer care: Opportunities and challenges in pediatric neuro‐oncology

A s the mysteries underlying the mechanisms of disease unravel, the potential for matching molecularly targeted therapies to an individual’s cancer fingerprint has emerged. Patients are clamoring for the perceived cure such an approach must hold, matched in tempo by the marketing of technologies that enable ‘‘customized’’ medicine. Health care providers, patients, pharmaceutical companies, and the biotechnology sector each have their own definition of personalized medicine. Here we discuss the current report by Wolff et al. on personalized therapy for pediatric brain tumors in the context of the past and future of personalized cancer medicine. The concept of personalized medicine is not a new one. The Mayo Clinic, for example, has implemented the principle of integrated care for the individual over decades [1]. The beginnings of modern personalized medicine, however, initiated with the discovery of biomarkers and the subsequent development of drugs that target the functionally significant biomarkers. Since the approval of imatinib (Gleevec) for chronic myelogenous leukemia (CML) in 2001, non-chemotherapy, molecularly targeted drugs have promised to revolutionize clinical oncology. Numerous examples of clinical trials exist that have utilized biomarkers to stratify enrolled patients into targeted therapy cohorts. Indeed, Phase III clinical trials conducted on a generalized cancer population are becoming the exception rather than the rule. That clinical trials should be designed to incorporate biologic markers is an encouraged if not required practice. This process began with the development of imatinib as a potent inhibitor of BCR-ABL in CML and has become a frequently modeled approach over the last decade [2]. Therapy for intermediate risk breast cancers was revolutionized by the results of the B20 trial and the development of OncotypeDx, a commercially available assay that analyzes a 21-gene profile in an individual’s tumor in order to calculate a validated recurrence score, which is now the standard of care for guiding therapy decisions [3]. Numerous other examples of biologically stratified targeted therapy trials exist, although almost exclusively in adult cohorts for diseases such as melanoma, colon cancer, and gastric cancer [4–6]. Perhaps the most elegant example to date of biomarker stratification is the BATTLE trial in non-small cell lung cancer [7]. In that recent trial of chemotherapy-refractory patients, the genetic profile of each tumor was used to match EGFR, KRAS/Braf, VEGF, and Cyclin D1 status with targeted drug therapy. The BATTLE trial was the first completed prospective adaptively randomized study in oncology and is considered a logistical success despite disappointing response rates. The above trials approach personalized therapy by splitting the larger population into smaller and smaller subsets using currently known biomarkers to categorize patients. A new generation of trials, including the cohort described here by Wolff et al., uses a true personalized approach by considering each individual tumor uniquely. In this issue of Pediatric Blood & Cancer, Wolff et al. designed individual treatment protocols for eight patients with relapsed pediatric brain tumors using morphoproteomomic (immunohistochemical) information to guide the selection of relevant molecularly targeted therapeutic agents. These agents were then incorporated into treatment regimens that included traditional cytotoxic agents. Each patient’s treatment regimen was highly personalized, taking into consideration past therapies, currently recommended secondand third-line drugs, as well as the tumor’s expression of biomarkers. In order to assess the clinical efficacy of this approach, Wolff et al. selected 325 treatment protocols from 129 individual patients to form a historical comparison group. Predicted event-free survival and tumor response probabilities for the eight relapsed patients were calculated from these historical controls. The authors then compared each individual patient’s actual outcome with the individual’s outcome prediction as an indication of clinical efficacy of the individual targeted therapy approach. (Commentary on Wolff et al., page 27)