Introduction Breast cancer is a heterogeneous disease with diverse morpholo- gies, molecular characteristics, clinical behavior, and response to therapeutics. As we delve more into the complex nature of this disease, it becomes imperative to determine appropriate prog- nostic and predictive markers that can be used by physicians and patients for informed decision making. The concept of personalized medicine recognizes this heterogeneity and considers each patient as having a unique tumor. Under this new paradigm, the cancer community foresees the possibility of predictive and prognostic parameters associated with recurrence and/or response to specific therapies that are unique to each individual. This review summa- rizes the various tools used for prognostication in breast cancer, the developing methods, and speculates on the characteristics necessary for an “ideal” future tool. Classic Prognostic Tools The most important prognostic markers for any cancer are tumor size, lymph node status, and histologic features, and this is no different for breast cancer. 1 Tumor size and nodal status are time-dependent variables and reflect the duration for which the tumor has been present. Tumor size is a good prognostic marker for distant relapse in lymph node–negative patients. 2,3 However, even patients with small tumors (< 1 cm) have a 12% chance of recur- rence. 4,5 In addition, tumor size measurement is confounded by several factors such as stromal desmoplastic reaction, coexisting in in situ lesions and tumor multicentricity. 6 Nodal status is predictive of both disease-free and overall survival. 7-9 In addition, the number of positive lymph nodes directly predicts survival. 2,6 However, 30% of node-negative patients still develop recurrences by 10 years. 6 Histologic classification is the morphologic assessment of inva- sive carcinomas for the degree of differentiation and the “proxim- ity of resemblance” to normal tissue. 1 There are 2 components to histologic classification: type and grade. 1,10 The predominant type, invasive ductal of no special type, accounts for up to 75% of all breast cancers; this significantly limits the use of type as a prognostic factor. 1,10 Grade provides a qualitative assessment of the biologic characteristics of the tumor; eg, high-grade tumors behave aggressively and have poor prognosis while low grade tumors have better prognosis. 11-14 However, there is only a modest Abstract Personalized Medicine: The Road Ahead Rutika Mehta, Rohit K. Jain, Sunil Badve With breast cancer now being recognized as a heterogeneous disease, the concept of personalized medicine de- mands that the tumor of every individual be treated uniquely. This has lead to ever-expanding use of existing prognos- tic and predictive markers, and the search for better ones is ongoing. The classic prognostic tools such as tumor size, lymph node status, grade, hormone receptors, and HER2 status are now supplemented by gene expression–based tools such as PAM50 and MammaPrint. However, the overdependence of these tools on proliferation-related genes is a signiicant handicap. Although pathway-based signatures hold great promise in future breast cancer prognos- tication, the fact that every tumor has multiple functional pathways signiicantly limits the utility of this approach. Developed by the integration of estrogen receptor (ER), HER2, proliferation-related, and other genes, the Oncotype DX assay has been able to provide valuable prognostic information for ER-positive tumors. Newer molecular markers based on cancer stem cells, single-nucleotide polymorphisms (SNPs), and miRNAs are becoming available, but their importance needs to be validated. It is clear that breast cancer is a multifaceted process and that none of the tools can reliably predict a binary outcome (recurrence or no recurrence). The breast cancer community is still awaiting an ideal prognostic tool that can integrate knowledge from classic variables such as tumor size and grade with new throughput technology and principles of pharmacogenomics. Such a tool will not only deine prognostic subgroups but also be able to predict therapeutic eficacy and/or resistance based on molecular proiling. Clinical Breast Cancer, Vol. 11, No. 1, 20-26, 2011; DOI: 10.3816/CBC.2011.n.004 Keywords: Future directions, Prognostic factors Review Department of Pathology, Indiana University School of Medicine, Indianapolis Submitted: Jun 29, 2010; Revised: Aug 16, 2010; Accepted: Aug 18, 2010 Address for correspondence: Sunil Badve, MD, FRCPath, Department of Pathology, Indiana University School of Medicine, 350 W 11th Street, IUHPL 4050, Indianapolis, IN 46202 Fax: 317-491-6419; e-mail: sbadve@iupui.edu 1526-8209/$ - see frontmatter © 2011 Elsevier Inc. All rights reserved. 20 | Clinical Breast Cancer February 2011 The summary may include the discussion of investigational and/or unlabeled uses of drugs and/or devices that may not be approved by the FDA. Electronic forwarding or copying is a violation of US and International Copyright Laws. For authorization to photocopy items for internal or personal use, visit www.elsevier.com/permissions.