- Email: [email protected]
Personalized medicine: individualized care of cancer patients
Personalized medicine: individualized care of cancer patients SCOTT ELY NEW YORK, NY
For most cancer patients today, therapy is chosen and implemented on a watchand-wait basis. Although an individual’s clinical information is used to decide which regimen is likely to work best, we still employ only data referring to outcomes of groups of patients. Currently, an individual patient’s biologic data is rarely employed in a systematic way to predict the best course of therapy. However, the advent of low-cost individual genomic and proteomic analysis provides hope that we are entering a new era of personalized, patient-specific care. This article is an analysis of the current real-life clinical use of individual patient data, a discussion of barriers to personalization, and examples of current success. (Translational Research 2009;154:303–308) Abbreviations: CLL ¼ chronic lymphocytic leukemia; CML ¼ chronic myelogenous leukemia; CR ¼ complete remission; CT ¼ cancer-testis; IHC ¼ immunohistochemistry; MM ¼ multiple myeloma; Rb ¼ retinoblastoma protein
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ersonalized medicine is the use of in-depth biologic information about an individual patient to make decisions about her care. In the realm of cancer, companies are already offering to perform predictive genomic analysis from a patient’s oral swab for a nominal fee. However, no consensus has been reached on how to use that information, and as such, it might cause more harm than good. Because autopsy studies show the actual incidence of cancer to be far greater than that reported in life,1 we must question the utility of using genetic data to predict incidence. If a patient suffers no clinical consequences from living with an undetected cancer, then using personal data to predict cancer
From the Department of Pathology, Weill Medical College of Cornell University, New York Presbyterian Hospital, New York, NY. Submitted for publication August 2, 2009. accepted for publication August 11, 2009. Reprint requests: Scott Ely, MD, Section of Hematopathology, Department of Pathology, Weill Medical College of Cornell University, New York Presbyterian Hospital, 525 E. 68th Street, New York, NY 10065; e-mail: [email protected]. 1931-5244/$ – see front matter Ó 2009 Mosby, Inc. All rights reserved. doi:10.1016/j.trsl.2009.08.001
development and ferret out preclinical lesions is problematic for obvious reasons. Current data suggest that prostate cancer, to name but one example, already is overtreated.2 If, using his genetic profile, a patient were told he had an 80% chance of developing prostate cancer before the age of 60, what would he do with that information? Currently, there is no proven efficacy for preventative therapeutic intervention for most cancers. However, because the technology is here, we need to begin a discussion about how to use it wisely. IDEAL CIRCUMSTANCES FOR PERSONALIZED CARE
As will follow, there are some real success stories in personalized cancer care but only in a few areas. Genomic testing is widely available, and is an area of intense research, but this information is rarely used in the clinical setting for real-life patient care. In this sense, currently, the technology has largely outstripped the usefulness of the data derived from it. Still, we must begin to consider what might be an ideal scenario to work toward. The ultimate goal of personalized cancer care would be that one day, a patient will be able to walk into any doctor’s office with a smart card, which can be swiped to input her medical history and genomic data, or we would have a national database containing this information. She would then tell the physician that she had 303
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noticed a lump while performing a self-examination. The physician would take a blood sample and send it off for rapid proteomic analysis and perform a fine-needle biopsy in the office, both to be sent for analysis at the discretion of a comprehensive pathology laboratory. A pathologist would then do a thorough histologic examination, and based on those findings and the peripheral blood proteomic analysis, she might perform additional studies. The data she would then provide the clinician would be a precise diagnosis, prognosis, and information stating which therapeutic regimens would work and not work against the tumor. The clinician would then institute the appropriate treatment, a targeted regimen effective against the patient’s cancer cells with minimal effects against noncancer cells. The treatment would result in a cure and the patient would be free to resume life as before. Although this outcome does come to fruition today for some patients, because cancer therapy is generally instituted on a best-guess, trial-and-error basis, it occurs less frequently than we would like. This review will focus on where we are today in real-life personalized clinical medicine, the obstacles that must be overcome to reach the ideal of personalized cancer care, and examples of progress. CONSIDERATIONS REGARDING THE USE OF PERSONAL PATIENT DATA
Autopsy studies show that the true incidence of cancer is much greater than what is reported. Proteomic analysis of a patient’s peripheral blood holds promise as a means of early cancer detection.3 However, for the patients who die not having suffered from or known about their cancer, they might be better off without having it detected and certainly would be better if left untreated. Studies of early detection of lung cancer suggest that some patients do not benefit from intervention.4 Before proteomics are employed as a means of noninvasive, lowcost detection, we need data showing which patients will benefit from intervention. A potential use of genomics and proteomics would be not the detection of cancer but, instead, a prediction of which patients might benefit from therapy and which would better be left alone. Although records of cancer therapy date back thousands of years, the practice of modern chemotherapy began with the use of arsenic to treat chronic myelogenous leukemia (CML) in 1865,5 but waned until the use of nitrogen mustard, nearly a century later. Since that time, the holy grail of therapeutics has been finding a way to kill cancer cells without affecting a patient’s normal cells. The inherent conundrum in this venture is illustrated by the fact that publication of the nitrogen mustard data was delayed by years, because it had
Translational Research December 2009
been government-classified as a spin off of research being performed for the purpose of producing chemical weapons in World War II.6 Both compounds, arsenic and nitrogen mustard, are chemicals that were first used as poisons, then modified to save patients by killing cancer cells. We attempt to minimize the negative effects on noncancer cells but have not found a way to prevent it entirely. Since the initial publication of the human genome in 2000, there has been a wellspring of hopeful predictions about the impact on medical care of individual patients.7 Several companies now offer services to map and assess an individual’s genome for a low fee. Some institutions have begun anonymous storage and assessment of all patients’ genomic DNA.8 These efforts are very promising, especially in the area of optimizing therapeutics based on individual patients’ drug metabolism. This genomic information could reduce the frequency of adverse drug reactions, which are a leading cause of death in the United States.9 Likewise, genomic data may be valuable in identifying high metabolizers, who might benefit for higher dosages of certain drugs. Also, genomic data can be used to predict the risk of developing a heritable disease. In addition to considering the potential good that can come from using each patient’s data, one potential pitfall needs to be considered. In our overworked health care delivery system, combined with our faith in and love of technology, there is the possibility that using these data may supplant the physician-patient relationship. For minor illnesses, the physician might tell a patient over the phone, ‘‘My data of your proteome suggests that your ache will abate with 650 mg of aspirin. Acetaminophen and ibuprofen will not work as well for you.’’ For serious illness, however, overreliance on genomic or proteomic data can imperil or undermine the perennial value of a thorough examination by a welltrained physician. Inevitably, as we follow the use of such information during the coming years, journals will begin to publish case series of patients whose illness or wellness defied their genomic statistics. When it comes to cancer, the utility of a patient’s genomic data is even less clear.10 Although it might be useful for suggesting the best dosage of a particular drug, it would be far less likely to predict the response of a cancer to any particular regimen. In that regard, useful data could only come from direct evaluation of tumor cells, because oncogenesis results from critical changes to the patient’s underlying genome, from mutations, deletions, translocations, and epigenetic changes. Of the factors that lead to cancer, genetic heritability is only for a minor factor. Genomic analysis has identified hundreds of gene variants strongly associated with cancer.11 Surprisingly, most of the variants are situated in
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non-protein coding (eg, intronic and intergenic) regions. A non-protein coding locus on chromosome 8q24 was one of the first inherited risk factors to be definitively associated with both prostate and colorectal cancers. The mechanism by which the 8q24 variants contribute to disease, however, is unknown, because the variants reside in a gene desert.12 This result presents one of the next major challenges in personalized medicine, to understand the functional consequences of inheriting these loci. Because our understanding of the nonprotein coding regions of the genome is underdeveloped in comparison with our knowledge of protein-coding regions, new strategies must be adopted to discover the genes that these variants are acting through. In the interim, the utility of developing schemes whereby at-risk patients are screened more closely must be assessed. Before using this data, we much consider that each method of assessment has its own limitations. Proteomic analysis is promising for cancer detection and surveylance.3,13 An important limitation of proteomics is its snapshot nature. Many important serum proteins fluctuate greatly over the course of days or even in a single day. An important limitation to genomic analysis is epigenetic influence. If a gene is mutated, but not expressed, the mutation cannot affect the patient. In multiple myeloma (MM), for example, the cyclin dependent kinase inhibitor, p16 has been reported to be mutated,14 but we have shown that p16 is not expressed at the protein level in MM, whether mutated or not.15 BARRIERS TO PERSONALIZED CANCER CARE
Although the use of genomic data to predict cancer development is currently of limited value, using biologic data derived from analysis of a patient’s tumor cells holds promise for the advancement of personalized care in the near future. Some roadblocks must be considered, as well as some true successes. The impact of the imatinib story. In 2001, imatinib (marketed as Gleevec in the United States by Novartis International, AG, Basel, Switzerland) was on the cover of Time magazine with an article describing it as the ‘‘magic bullet’’ to cure cancer. It is used to treat CML. The story that ran in both the general and the medical literature described imatinib as being the prototype of a specific treatment for cancer, which means that it would attack cancer cells while leaving most noncancer cells unscathed. Subsequent research has shown that imatinib is a fairly specific tyrosine kinase inhibitor. However, whereas it was originally thought to specifically inhibit the mutant Bcr-Abl tyrosine kinase, unique to CML, it has subsequently been found also to target many other kinases. Still, it is well tolerated and effective, prolonging the life of CML patients.
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Imatinib sparked hope for the development of other disease-specific cancer drugs that would be effective but with few side effects. The use of trastuzumab (marketed as Herceptin by Genentec, South San Francisco, Calif) to treat HER21 breast cancers also is an example of success in the area of personalized cancer care. In other cancers, however, several hurdles must be crossed. The heterogeneity of cancer subtypes. Outcomes depend not only on the skill of healthcare providers but also on the underlying biology of the patient and of the patient’s cancer. In an attempt toward the standardization of care, we rely on published clinical trials. We begin with that data. For example 70% of patients responded and 30% of responders had a complete remission (CR). Then, we extrapolate to apply these numbers to an individual patient. We tell this patient, ‘‘You have a 70% chance of response and, if you respond, you have a 30% chance of CR.’’ The problem with this scenario is that we do not know at the outset which group this particular patient will fall into, because our data do not usually provide a method for making that prediction. In oncology, this is partly because what we call a diagnostic entity, in fact comprises a biologic array of diseases. In MM, for example, in 9 identical trials on patients with comparable clinical stages, the median survival ranged widely, from 1.6 to 3.5 years.16 Although the median survival is 4 years, 15% of MM patients survive for more than 10 years. Similarly, if followed for 20 years, 25% of patient’s with the precancerous monoclonal gammopathy of undetermined significance (MGUS) will progress to MM, but the rest will not. Diagnostic criteria have changed over the years, but still today, for most cancers, criteria do not provide a way to distinguish between biologically disparate tumors. In some instances, we have found ways to parse out the bad actors from the indolent, but often the information has not enabled us to exploit the differences to provide personalized care. For example, we now know that two varieties of chronic lymphocytic leukemia (CLL) exist, which include17 ZAP70-positive and ZAP70-negative. In the former, the CLL cells show genetic evidence of not having gone through the antigen selection process prior to oncogenesis (they have nonhypermutated immunoglobulin genes); this feature is associated with expression of the ZAP70 protein. The more indolent type of CLL has hypermutated immunoglobulin genes and does not express the ZAP70 protein. Whereas ZAP70 1 CLL has a median survival of 8–9 years, ZAP70-negative CLL patients typically survive 24 years.18 ZAP70 is widely tested in CLL patients, but there is no consensus on what to do with the data, nor is there any specific biologically targeted therapy. Similarly, CLL is the only common lymphoid cancer with
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a strong heritable predisposition, but currently there is no precedent for use of such information. The impact of the drug-development process on personalized care. To assess the promise and limitations
of personalized cancer care, we must consider the process by which drugs become available. In recent decades, the process has often begun with chemical modification of an existing efficacious drug, in an attempt to make it more potent and/or less toxic. New drug in hand, investigators then go on to testing in animal models, then human cell lines and in ex vivo patient samples. If these initial investigations show promise, along with the drug manufacturer, investigators can then seek permission to begin a phase 1 trial, to work out dosing, and then seek permission for phase 2 and 3 trials to test efficacy. If the efficacy to toxicity balance seems favorable and U.S. Food and Drug Administration approval has been granted, then the drug comes to market. The manufacturer’s profits are then dependent on the amount of drug sold, which, in turn, is dependent on the number of patients who get the drug. Narrower, more precise efficacy means smaller groups of patients to treat. In that scheme, there is a powerful built-in financial incentive against personalization. More recently, however, this model has changed somewhat, especially since the success of imatinib for the treatment of CML. Typically, subsequent research reveals that what was thought to be a precise and narrow target is actually broader, so drug use is expanded to other diagnoses and groups of patients.19 For blood cancers, an additonal hurdle in deriving data. Personalized prediction of drug efficacy leads to
a unique hurdle when assessing blood cancers. In solid-organ cancers (eg, lung, colon, and breast), the tumor grows as a nearly pure population of cancer cells. The proportion of cells composed of infiltrating immune cells is typically minimal and easy to assess by routine histology. Stroma and vasculature typically compose relatively minute populations. As such, DNA, RNA, and proteins extracted and assessed from random areas of solid organ tumors are well representative of the tumor as a whole. In blood cancers, however, the cancer cells usually are intimately admixed with large populations of noncancerous hematopoietic cells. Unless special procedures are used, data from blood cancer nucleotides and proteins are contaminated by unknowable quantities of noncancer cells. This additional hurdle can be surmounted by cell sorting, either in a flow cytometer or in a magnetic bead column. However, if the protein in question is present in high quantities in the noncancer cells, even a small amount of contamination can cause large errors. Most chemotherapeutic drugs do not have a precise target. As discussed above, from the perspective of
money spent in drug development, the downside to a pre-
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cise mechanism of action is that there will be relatively limited use for the compound that costs so much to develop. The upside is that the more tumor-specific the effects of a drug are, the fewer the off-target side effects will be. Commonly, the course of events is the discovery of a pathogenic molecule in a cancer, followed by a highthroughput screen of chemical libraries, which results in the identification of compounds that block the effects of the pathogenic molecule. Of the candidates identified, they are then tested for potency and specificity. The best candidate is then tested ex vivo and in animal models and, if successful, depending partly on the size of the target patient market, clinical trials will begin. It is sometimes only through the discovery of clinical side effects that off-target biologic consequences come to light. Typically, the drug that was originally described as having a precise, specific target is discovered to have many targets. In that case of imatinib, in addition to CML, it is now being investigated for the treatment of gastrointestinal tumors, mastocytosis, sarcomas, and even pulmonary hypertension; none of these conditions possess the Bcr-Abl protein, which was originally thought to be the specific target of imatinib. By comparison, however, the effects of most drugs are far less precise even than imatinib. This is a barrier to personalized medicine because if a drug does not have a precise target, there is no sure way to predict, in advance, which patients will benefit from the drug. The mechanism of action for most drug combinations is not known. A common and prevalent characteristic of
cancer cells is instability. Because the processes that trigger apoptosis in normal cells are impaired, mutations are allowed to accumulate. As such, for most cancers, targeting a single pathway results in the development of drug resistance instead of a cure. This is why, for most cancers, there is no single magic bullet. Curing a cancer with a single drug is rare. Because of the consistency of this finding, patients are instead given a battery of clinically synergistic drugs. Instead of sending in a sharp shooter to score with a well-placed bullet, success is more likely with a shotgun approach. This creates a formidable barrier to predicting efficacy. If the proven clinically effective regimen has multiple targets of variable relevance on a patient by patient basis, it is not possible to create a feasible laboratory test to tell us which patient’s cancer will respond. Many published studies show gene chip array data, which correlates response with a certain gene expression profile. However, gene chip testing is costly and labor intensive; even though it has been available for nearly a decade, its current use is strictly limited to the research setting. The importance of uniform testing parameters. For Bcr-Abl testing in CML, a voluntary committee of doctors and scientists works together to create technical
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guidelines.20 Although Bcr-Abl techniques and methods of reporting have consequently become fairly standard and uniform, in other areas of testing, considerable variability exists. Gene chip array, for example, is notoriously operator dependent. In another example, anthracyclines, which is a mainstay of adjuvant therapy for breast cancer chemotherapy, have been reported to benefit as few as only 30% to 40% of patients. In an attempt to predict which patients would benefit, several groups have studied correlations between response to anthracyclines, HER2 status and other genetic determinants. These groups have published diametrically opposed results in high ranking periodicals.21-23 Although it is difficult to determine the precise nature of the discrepancies, they are likely to derive at least in part to variability in laboratory techniques, data reporting, and analysis. Inasmuch as progress in personalized medicine will depend on the use of laboratory testing data, standardization will be critical. SUCCESS IN MOVING TOWARD PERSONALIZED CANCER CARE
In reaching the ideal of personalized care outlined in the introduction, one of the most intriguing prospects is immunotherapy, such as for cancer-testis (CT) antigens. CT antigens are expressed only by germ cells and cancers. Because they are not present on any normal somatic cells, they may be an ideal target for immunotherapy. Several studies have defined which CT antigens are expressed in which cancers,24 and testing for expression in a given patient involves only a small battery of relatively inexpensive, standard immunohistochemistry (IHC) tests. Many early clinical trials have confirmed the proof of principle. Similar studies have used other cancer antigens. Patients with widely disseminated metastatic cancers have been given a vaccine containing a CT or other cancer antigen, in hopes of activating a specific antitumor immune response. Although few patients responded well, some had complete remissions.25 In such trials, remission is induced by outpatient immunotherapy, involving only a few shots which cost little. Because it is a tumor specific therapy, it has virtually no side effects. As such, this approach holds great promise for personalized cancer care. Because the principle was proven, work is now being done to determine how to get more patients to respond. In addition to CT antigen immunotherapy, another effort toward personalized therapy in myeloma is a trial centered around the use of PD 0332991, which is a selective cdk4/6 inhibitor.26 The trial employs several of the principles mentioned in the beginning of this article and, as such, is a positive move toward personalized cancer therapy. PD 0332991 prevents phosphorylation of the retinoblastoma protein (Rb) by cdk4/6 in the early
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G1 phase of the cell cycle, and thus it prevents entry into S phase and induces a cell cycle arrest.15 Ex vivo and animal studies have shown that the buildup of early G1 cell cycle molecules makes the MM cells fragile and susceptible to apoptosis when exposed to small amounts of other agents, like proteasome inhibitors. The ongoing phase 1/2 trial is unusual in that, in addition to the typical clinical parameters required for enrollment, the trial uses IHC with an objective and standardized image analysis method to assess a patient’s cancer cells. A patient is only enrolled if the cancer cells express the Rb protein. This method of patient selection, via verifying the potential of response with a single cell-level biochemical assessment, is a positive step toward personalization. It is a significant advancement over the current watch and wait method employed in most other studies. CONCLUSION
Merging the ultimate goal of personalized medicine, knowing in advance what therapy will work, with the ultimate goal of cancer care, eradicating the disease without harming the rest of the body, is a positive step in the right direction. Aside from great benefits to patients’ health, this approach holds the promise of great cost saving, a key goal of healthcare reform. In addition to the advances cited here, much ongoing research and discussion is moving toward personalizing the process of cancer care. REFERENCES
1. Thompson IM, Lucia MS, Tangen CM. Commentary. The ubiquity of prostate cancer: echoes of the past, implications for the present: ‘‘What has been will be again, what has been done will be done again; there is nothing new under the sun.’’ ECCLESIASTES 1:9. Int J Epidemiol 2007;36:287–9. 2. Sanda MG, Dunn RL, Michalski J, et al. Quality of life and satisfaction with outcome among prostate-cancer survivors. N Engl J Med 2008;358:1250–61. 3. Petricoin Iii EF, Ardekani AM, Hitt BA, et al. Use of proteomic patterns in serum to identify ovarian cancer. Lancet 2002;359: 572–7. 4. Bach PB, Jett JR, Pastorino U, Tockman MS, Swensen SJ, Begg CB. Computed tomography screening and lung cancer outcomes. JAMA 2007;297:953–61. 5. Lissauer. Zwei Falle von Leucaeme Berliner Klin Wochnschr 1865;2:403–404. 6. Papac RJ. Origins of cancer therapy. Yale J Biol Med 2001;74: 391–8. 7. Weston AD, Hood L. Systems biology, proteomics, and the future of health care: toward predictive, preventative, and personalized medicine. J Proteome Res 2004;3:179–96. 8. Macmillan L. In pursuit of personalized medicine. Reporter, Vanderbilt Medical Center’s Weekly Paper October 26, 2007. Online publication. 9. Lazarou J, Pomeranz BH, Corey PN. Incidence of adverse drug reactions in hospitalized patients: A meta-analysis of prospective studies. JAMA 1998;279:1200–5.
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10. Goldstein DB. Common genetic variation and human traits. N Engl J Med 2009;360:1696–8. 11. Penney KL, Salinas CA, Pomerantz M, et al. Evaluation of 8q24 and 17q risk loci and prostate cancer mortality. Clin Cancer Res 2009;15:3223–30. 12. Freedman ML, Haiman CA, Patterson N, et al. Admixture mapping identifies 8q24 as a prostate cancer risk locus in AfricanAmerican men. Proc Natl Acad Sci U S A 2006;103:14068–73. 13. Anderson NL, Anderson NG. The human plasma proteome: history, character, and diagnostic prospects. Mol Cell Proteomics 2002;1:845–67. 14. Ng MH, Chung YF, Lo KW, Wickham NW, Lee JC, Huang DP. Frequent hypermethylation of p16 and p15 genes in multiple myeloma. Blood 1997;89:2500–6. 15. Ely S, Di Liberto M, Niesvizky R, et al. Mutually exclusive cyclindependent kinase 4/cyclin D1 and cyclin-dependent kinase 6/cyclin D2 pairing inactivates retinoblastoma protein and promotes cell cycle dysregulation in multiple myeloma. Cancer Res 2005; 65:11345–53. 16. Bergsagel DE. Is aggressive chemotherapy more effective in the treatment of plasma cell myeloma? Eur J Cancer Clin Oncol 1989;25:159–61. 17. Kay NE, O’Brien SM, Pettitt AR, Stilgenbauer S. The role of prognostic factors in assessing ‘‘high-risk’’ subgroups of patients with chronic lymphocytic leukemia. Leukemia 2007;21:1885–91. 18. Wiestner A, Rosenwald A, Barry TS, et al. ZAP-70 expression identifies a chronic lymphocytic leukemia subtype with unmutated immunoglobulin genes, inferior clinical outcome, and distinct gene expression profile. Blood 2003;101:4944–51.
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19. Druker BJ, Tamura S, Buchdunger E, et al. Effects of a selective inhibitor of the Abl tyrosine kinase on the growth of Bcr-Abl positive cells. Nat Med 1996;2:561–6. 20. Hughes T, Deininger M, Hochhaus A, et al. Monitoring CML patients responding to treatment with tyrosine kinase inhibitors: review and recommendations for harmonizing current methodology for detecting BCR-ABL transcripts and kinase domain mutations and for expressing results. Blood 2006;108:28–37. 21. Pritchard KI, Shepherd LE, O’Malley FP, et al. HER2 and responsiveness of breast cancer to adjuvant chemotherapy. N Engl J Med 2006;354:2103–11. 22. Bartlett JMS, Munro A, Cameron DA, Thomas J, Prescott R, Twelves CJ. Type 1 receptor tyrosine kinase profiles identify patients with enhanced benefit from anthracyclines in the BR9601 adjuvant breast cancer chemotherapy trial. J Clin Oncol 2008;26:5027–35. 23. O’Malley FP, Chia S, Tu D, et al. Topoisomerase II alpha and responsiveness of breast cancer to adjuvant chemotherapy. J Natl Cancer Inst 2009;101:644–50. 24. Burton JD, Ely S, Reddy PK, et al. CD74 is expressed by multiple myeloma and is a promising target for therapy. Clin Cancer Res 2004;10:6606–11. 25. Riker AIAI, Radfar SS, Liu SS, Wang YY, Khong HTHT. Immunotherapy of melanoma: a critical review of current concepts and future strategies. Expert Opin Biol Ther 2007;7: 345–58. 26. Baughn LB, Di Liberto M, Wu K, et al. A novel orally active small molecule potently induces G1 arrest in primary myeloma cells and prevents tumor growth by specific inhibition of cyclin-dependent kinase 4/6. Cancer Res 2006;66:7661–7.
For most cancer patients today, therapy is chosen and implemented on a watchand-wait basis. Although an individual’s clinical information is used to decide which regimen is likely to work best, we still employ only data referring to outcomes of groups of patients. Currently, an individual patient’s biologic data is rarely employed in a systematic way to predict the best course of therapy. However, the advent of low-cost individual genomic and proteomic analysis provides hope that we are entering a new era of personalized, patient-specific care. This article is an analysis of the current real-life clinical use of individual patient data, a discussion of barriers to personalization, and examples of current success. (Translational Research 2009;154:303–308) Abbreviations: CLL ¼ chronic lymphocytic leukemia; CML ¼ chronic myelogenous leukemia; CR ¼ complete remission; CT ¼ cancer-testis; IHC ¼ immunohistochemistry; MM ¼ multiple myeloma; Rb ¼ retinoblastoma protein
P
ersonalized medicine is the use of in-depth biologic information about an individual patient to make decisions about her care. In the realm of cancer, companies are already offering to perform predictive genomic analysis from a patient’s oral swab for a nominal fee. However, no consensus has been reached on how to use that information, and as such, it might cause more harm than good. Because autopsy studies show the actual incidence of cancer to be far greater than that reported in life,1 we must question the utility of using genetic data to predict incidence. If a patient suffers no clinical consequences from living with an undetected cancer, then using personal data to predict cancer
From the Department of Pathology, Weill Medical College of Cornell University, New York Presbyterian Hospital, New York, NY. Submitted for publication August 2, 2009. accepted for publication August 11, 2009. Reprint requests: Scott Ely, MD, Section of Hematopathology, Department of Pathology, Weill Medical College of Cornell University, New York Presbyterian Hospital, 525 E. 68th Street, New York, NY 10065; e-mail: [email protected]. 1931-5244/$ – see front matter Ó 2009 Mosby, Inc. All rights reserved. doi:10.1016/j.trsl.2009.08.001
development and ferret out preclinical lesions is problematic for obvious reasons. Current data suggest that prostate cancer, to name but one example, already is overtreated.2 If, using his genetic profile, a patient were told he had an 80% chance of developing prostate cancer before the age of 60, what would he do with that information? Currently, there is no proven efficacy for preventative therapeutic intervention for most cancers. However, because the technology is here, we need to begin a discussion about how to use it wisely. IDEAL CIRCUMSTANCES FOR PERSONALIZED CARE
As will follow, there are some real success stories in personalized cancer care but only in a few areas. Genomic testing is widely available, and is an area of intense research, but this information is rarely used in the clinical setting for real-life patient care. In this sense, currently, the technology has largely outstripped the usefulness of the data derived from it. Still, we must begin to consider what might be an ideal scenario to work toward. The ultimate goal of personalized cancer care would be that one day, a patient will be able to walk into any doctor’s office with a smart card, which can be swiped to input her medical history and genomic data, or we would have a national database containing this information. She would then tell the physician that she had 303
304
Ely
noticed a lump while performing a self-examination. The physician would take a blood sample and send it off for rapid proteomic analysis and perform a fine-needle biopsy in the office, both to be sent for analysis at the discretion of a comprehensive pathology laboratory. A pathologist would then do a thorough histologic examination, and based on those findings and the peripheral blood proteomic analysis, she might perform additional studies. The data she would then provide the clinician would be a precise diagnosis, prognosis, and information stating which therapeutic regimens would work and not work against the tumor. The clinician would then institute the appropriate treatment, a targeted regimen effective against the patient’s cancer cells with minimal effects against noncancer cells. The treatment would result in a cure and the patient would be free to resume life as before. Although this outcome does come to fruition today for some patients, because cancer therapy is generally instituted on a best-guess, trial-and-error basis, it occurs less frequently than we would like. This review will focus on where we are today in real-life personalized clinical medicine, the obstacles that must be overcome to reach the ideal of personalized cancer care, and examples of progress. CONSIDERATIONS REGARDING THE USE OF PERSONAL PATIENT DATA
Autopsy studies show that the true incidence of cancer is much greater than what is reported. Proteomic analysis of a patient’s peripheral blood holds promise as a means of early cancer detection.3 However, for the patients who die not having suffered from or known about their cancer, they might be better off without having it detected and certainly would be better if left untreated. Studies of early detection of lung cancer suggest that some patients do not benefit from intervention.4 Before proteomics are employed as a means of noninvasive, lowcost detection, we need data showing which patients will benefit from intervention. A potential use of genomics and proteomics would be not the detection of cancer but, instead, a prediction of which patients might benefit from therapy and which would better be left alone. Although records of cancer therapy date back thousands of years, the practice of modern chemotherapy began with the use of arsenic to treat chronic myelogenous leukemia (CML) in 1865,5 but waned until the use of nitrogen mustard, nearly a century later. Since that time, the holy grail of therapeutics has been finding a way to kill cancer cells without affecting a patient’s normal cells. The inherent conundrum in this venture is illustrated by the fact that publication of the nitrogen mustard data was delayed by years, because it had
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been government-classified as a spin off of research being performed for the purpose of producing chemical weapons in World War II.6 Both compounds, arsenic and nitrogen mustard, are chemicals that were first used as poisons, then modified to save patients by killing cancer cells. We attempt to minimize the negative effects on noncancer cells but have not found a way to prevent it entirely. Since the initial publication of the human genome in 2000, there has been a wellspring of hopeful predictions about the impact on medical care of individual patients.7 Several companies now offer services to map and assess an individual’s genome for a low fee. Some institutions have begun anonymous storage and assessment of all patients’ genomic DNA.8 These efforts are very promising, especially in the area of optimizing therapeutics based on individual patients’ drug metabolism. This genomic information could reduce the frequency of adverse drug reactions, which are a leading cause of death in the United States.9 Likewise, genomic data may be valuable in identifying high metabolizers, who might benefit for higher dosages of certain drugs. Also, genomic data can be used to predict the risk of developing a heritable disease. In addition to considering the potential good that can come from using each patient’s data, one potential pitfall needs to be considered. In our overworked health care delivery system, combined with our faith in and love of technology, there is the possibility that using these data may supplant the physician-patient relationship. For minor illnesses, the physician might tell a patient over the phone, ‘‘My data of your proteome suggests that your ache will abate with 650 mg of aspirin. Acetaminophen and ibuprofen will not work as well for you.’’ For serious illness, however, overreliance on genomic or proteomic data can imperil or undermine the perennial value of a thorough examination by a welltrained physician. Inevitably, as we follow the use of such information during the coming years, journals will begin to publish case series of patients whose illness or wellness defied their genomic statistics. When it comes to cancer, the utility of a patient’s genomic data is even less clear.10 Although it might be useful for suggesting the best dosage of a particular drug, it would be far less likely to predict the response of a cancer to any particular regimen. In that regard, useful data could only come from direct evaluation of tumor cells, because oncogenesis results from critical changes to the patient’s underlying genome, from mutations, deletions, translocations, and epigenetic changes. Of the factors that lead to cancer, genetic heritability is only for a minor factor. Genomic analysis has identified hundreds of gene variants strongly associated with cancer.11 Surprisingly, most of the variants are situated in
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non-protein coding (eg, intronic and intergenic) regions. A non-protein coding locus on chromosome 8q24 was one of the first inherited risk factors to be definitively associated with both prostate and colorectal cancers. The mechanism by which the 8q24 variants contribute to disease, however, is unknown, because the variants reside in a gene desert.12 This result presents one of the next major challenges in personalized medicine, to understand the functional consequences of inheriting these loci. Because our understanding of the nonprotein coding regions of the genome is underdeveloped in comparison with our knowledge of protein-coding regions, new strategies must be adopted to discover the genes that these variants are acting through. In the interim, the utility of developing schemes whereby at-risk patients are screened more closely must be assessed. Before using this data, we much consider that each method of assessment has its own limitations. Proteomic analysis is promising for cancer detection and surveylance.3,13 An important limitation of proteomics is its snapshot nature. Many important serum proteins fluctuate greatly over the course of days or even in a single day. An important limitation to genomic analysis is epigenetic influence. If a gene is mutated, but not expressed, the mutation cannot affect the patient. In multiple myeloma (MM), for example, the cyclin dependent kinase inhibitor, p16 has been reported to be mutated,14 but we have shown that p16 is not expressed at the protein level in MM, whether mutated or not.15 BARRIERS TO PERSONALIZED CANCER CARE
Although the use of genomic data to predict cancer development is currently of limited value, using biologic data derived from analysis of a patient’s tumor cells holds promise for the advancement of personalized care in the near future. Some roadblocks must be considered, as well as some true successes. The impact of the imatinib story. In 2001, imatinib (marketed as Gleevec in the United States by Novartis International, AG, Basel, Switzerland) was on the cover of Time magazine with an article describing it as the ‘‘magic bullet’’ to cure cancer. It is used to treat CML. The story that ran in both the general and the medical literature described imatinib as being the prototype of a specific treatment for cancer, which means that it would attack cancer cells while leaving most noncancer cells unscathed. Subsequent research has shown that imatinib is a fairly specific tyrosine kinase inhibitor. However, whereas it was originally thought to specifically inhibit the mutant Bcr-Abl tyrosine kinase, unique to CML, it has subsequently been found also to target many other kinases. Still, it is well tolerated and effective, prolonging the life of CML patients.
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Imatinib sparked hope for the development of other disease-specific cancer drugs that would be effective but with few side effects. The use of trastuzumab (marketed as Herceptin by Genentec, South San Francisco, Calif) to treat HER21 breast cancers also is an example of success in the area of personalized cancer care. In other cancers, however, several hurdles must be crossed. The heterogeneity of cancer subtypes. Outcomes depend not only on the skill of healthcare providers but also on the underlying biology of the patient and of the patient’s cancer. In an attempt toward the standardization of care, we rely on published clinical trials. We begin with that data. For example 70% of patients responded and 30% of responders had a complete remission (CR). Then, we extrapolate to apply these numbers to an individual patient. We tell this patient, ‘‘You have a 70% chance of response and, if you respond, you have a 30% chance of CR.’’ The problem with this scenario is that we do not know at the outset which group this particular patient will fall into, because our data do not usually provide a method for making that prediction. In oncology, this is partly because what we call a diagnostic entity, in fact comprises a biologic array of diseases. In MM, for example, in 9 identical trials on patients with comparable clinical stages, the median survival ranged widely, from 1.6 to 3.5 years.16 Although the median survival is 4 years, 15% of MM patients survive for more than 10 years. Similarly, if followed for 20 years, 25% of patient’s with the precancerous monoclonal gammopathy of undetermined significance (MGUS) will progress to MM, but the rest will not. Diagnostic criteria have changed over the years, but still today, for most cancers, criteria do not provide a way to distinguish between biologically disparate tumors. In some instances, we have found ways to parse out the bad actors from the indolent, but often the information has not enabled us to exploit the differences to provide personalized care. For example, we now know that two varieties of chronic lymphocytic leukemia (CLL) exist, which include17 ZAP70-positive and ZAP70-negative. In the former, the CLL cells show genetic evidence of not having gone through the antigen selection process prior to oncogenesis (they have nonhypermutated immunoglobulin genes); this feature is associated with expression of the ZAP70 protein. The more indolent type of CLL has hypermutated immunoglobulin genes and does not express the ZAP70 protein. Whereas ZAP70 1 CLL has a median survival of 8–9 years, ZAP70-negative CLL patients typically survive 24 years.18 ZAP70 is widely tested in CLL patients, but there is no consensus on what to do with the data, nor is there any specific biologically targeted therapy. Similarly, CLL is the only common lymphoid cancer with
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a strong heritable predisposition, but currently there is no precedent for use of such information. The impact of the drug-development process on personalized care. To assess the promise and limitations
of personalized cancer care, we must consider the process by which drugs become available. In recent decades, the process has often begun with chemical modification of an existing efficacious drug, in an attempt to make it more potent and/or less toxic. New drug in hand, investigators then go on to testing in animal models, then human cell lines and in ex vivo patient samples. If these initial investigations show promise, along with the drug manufacturer, investigators can then seek permission to begin a phase 1 trial, to work out dosing, and then seek permission for phase 2 and 3 trials to test efficacy. If the efficacy to toxicity balance seems favorable and U.S. Food and Drug Administration approval has been granted, then the drug comes to market. The manufacturer’s profits are then dependent on the amount of drug sold, which, in turn, is dependent on the number of patients who get the drug. Narrower, more precise efficacy means smaller groups of patients to treat. In that scheme, there is a powerful built-in financial incentive against personalization. More recently, however, this model has changed somewhat, especially since the success of imatinib for the treatment of CML. Typically, subsequent research reveals that what was thought to be a precise and narrow target is actually broader, so drug use is expanded to other diagnoses and groups of patients.19 For blood cancers, an additonal hurdle in deriving data. Personalized prediction of drug efficacy leads to
a unique hurdle when assessing blood cancers. In solid-organ cancers (eg, lung, colon, and breast), the tumor grows as a nearly pure population of cancer cells. The proportion of cells composed of infiltrating immune cells is typically minimal and easy to assess by routine histology. Stroma and vasculature typically compose relatively minute populations. As such, DNA, RNA, and proteins extracted and assessed from random areas of solid organ tumors are well representative of the tumor as a whole. In blood cancers, however, the cancer cells usually are intimately admixed with large populations of noncancerous hematopoietic cells. Unless special procedures are used, data from blood cancer nucleotides and proteins are contaminated by unknowable quantities of noncancer cells. This additional hurdle can be surmounted by cell sorting, either in a flow cytometer or in a magnetic bead column. However, if the protein in question is present in high quantities in the noncancer cells, even a small amount of contamination can cause large errors. Most chemotherapeutic drugs do not have a precise target. As discussed above, from the perspective of
money spent in drug development, the downside to a pre-
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cise mechanism of action is that there will be relatively limited use for the compound that costs so much to develop. The upside is that the more tumor-specific the effects of a drug are, the fewer the off-target side effects will be. Commonly, the course of events is the discovery of a pathogenic molecule in a cancer, followed by a highthroughput screen of chemical libraries, which results in the identification of compounds that block the effects of the pathogenic molecule. Of the candidates identified, they are then tested for potency and specificity. The best candidate is then tested ex vivo and in animal models and, if successful, depending partly on the size of the target patient market, clinical trials will begin. It is sometimes only through the discovery of clinical side effects that off-target biologic consequences come to light. Typically, the drug that was originally described as having a precise, specific target is discovered to have many targets. In that case of imatinib, in addition to CML, it is now being investigated for the treatment of gastrointestinal tumors, mastocytosis, sarcomas, and even pulmonary hypertension; none of these conditions possess the Bcr-Abl protein, which was originally thought to be the specific target of imatinib. By comparison, however, the effects of most drugs are far less precise even than imatinib. This is a barrier to personalized medicine because if a drug does not have a precise target, there is no sure way to predict, in advance, which patients will benefit from the drug. The mechanism of action for most drug combinations is not known. A common and prevalent characteristic of
cancer cells is instability. Because the processes that trigger apoptosis in normal cells are impaired, mutations are allowed to accumulate. As such, for most cancers, targeting a single pathway results in the development of drug resistance instead of a cure. This is why, for most cancers, there is no single magic bullet. Curing a cancer with a single drug is rare. Because of the consistency of this finding, patients are instead given a battery of clinically synergistic drugs. Instead of sending in a sharp shooter to score with a well-placed bullet, success is more likely with a shotgun approach. This creates a formidable barrier to predicting efficacy. If the proven clinically effective regimen has multiple targets of variable relevance on a patient by patient basis, it is not possible to create a feasible laboratory test to tell us which patient’s cancer will respond. Many published studies show gene chip array data, which correlates response with a certain gene expression profile. However, gene chip testing is costly and labor intensive; even though it has been available for nearly a decade, its current use is strictly limited to the research setting. The importance of uniform testing parameters. For Bcr-Abl testing in CML, a voluntary committee of doctors and scientists works together to create technical
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guidelines.20 Although Bcr-Abl techniques and methods of reporting have consequently become fairly standard and uniform, in other areas of testing, considerable variability exists. Gene chip array, for example, is notoriously operator dependent. In another example, anthracyclines, which is a mainstay of adjuvant therapy for breast cancer chemotherapy, have been reported to benefit as few as only 30% to 40% of patients. In an attempt to predict which patients would benefit, several groups have studied correlations between response to anthracyclines, HER2 status and other genetic determinants. These groups have published diametrically opposed results in high ranking periodicals.21-23 Although it is difficult to determine the precise nature of the discrepancies, they are likely to derive at least in part to variability in laboratory techniques, data reporting, and analysis. Inasmuch as progress in personalized medicine will depend on the use of laboratory testing data, standardization will be critical. SUCCESS IN MOVING TOWARD PERSONALIZED CANCER CARE
In reaching the ideal of personalized care outlined in the introduction, one of the most intriguing prospects is immunotherapy, such as for cancer-testis (CT) antigens. CT antigens are expressed only by germ cells and cancers. Because they are not present on any normal somatic cells, they may be an ideal target for immunotherapy. Several studies have defined which CT antigens are expressed in which cancers,24 and testing for expression in a given patient involves only a small battery of relatively inexpensive, standard immunohistochemistry (IHC) tests. Many early clinical trials have confirmed the proof of principle. Similar studies have used other cancer antigens. Patients with widely disseminated metastatic cancers have been given a vaccine containing a CT or other cancer antigen, in hopes of activating a specific antitumor immune response. Although few patients responded well, some had complete remissions.25 In such trials, remission is induced by outpatient immunotherapy, involving only a few shots which cost little. Because it is a tumor specific therapy, it has virtually no side effects. As such, this approach holds great promise for personalized cancer care. Because the principle was proven, work is now being done to determine how to get more patients to respond. In addition to CT antigen immunotherapy, another effort toward personalized therapy in myeloma is a trial centered around the use of PD 0332991, which is a selective cdk4/6 inhibitor.26 The trial employs several of the principles mentioned in the beginning of this article and, as such, is a positive move toward personalized cancer therapy. PD 0332991 prevents phosphorylation of the retinoblastoma protein (Rb) by cdk4/6 in the early
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G1 phase of the cell cycle, and thus it prevents entry into S phase and induces a cell cycle arrest.15 Ex vivo and animal studies have shown that the buildup of early G1 cell cycle molecules makes the MM cells fragile and susceptible to apoptosis when exposed to small amounts of other agents, like proteasome inhibitors. The ongoing phase 1/2 trial is unusual in that, in addition to the typical clinical parameters required for enrollment, the trial uses IHC with an objective and standardized image analysis method to assess a patient’s cancer cells. A patient is only enrolled if the cancer cells express the Rb protein. This method of patient selection, via verifying the potential of response with a single cell-level biochemical assessment, is a positive step toward personalization. It is a significant advancement over the current watch and wait method employed in most other studies. CONCLUSION
Merging the ultimate goal of personalized medicine, knowing in advance what therapy will work, with the ultimate goal of cancer care, eradicating the disease without harming the rest of the body, is a positive step in the right direction. Aside from great benefits to patients’ health, this approach holds the promise of great cost saving, a key goal of healthcare reform. In addition to the advances cited here, much ongoing research and discussion is moving toward personalizing the process of cancer care. REFERENCES
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19. Druker BJ, Tamura S, Buchdunger E, et al. Effects of a selective inhibitor of the Abl tyrosine kinase on the growth of Bcr-Abl positive cells. Nat Med 1996;2:561–6. 20. Hughes T, Deininger M, Hochhaus A, et al. Monitoring CML patients responding to treatment with tyrosine kinase inhibitors: review and recommendations for harmonizing current methodology for detecting BCR-ABL transcripts and kinase domain mutations and for expressing results. Blood 2006;108:28–37. 21. Pritchard KI, Shepherd LE, O’Malley FP, et al. HER2 and responsiveness of breast cancer to adjuvant chemotherapy. N Engl J Med 2006;354:2103–11. 22. Bartlett JMS, Munro A, Cameron DA, Thomas J, Prescott R, Twelves CJ. Type 1 receptor tyrosine kinase profiles identify patients with enhanced benefit from anthracyclines in the BR9601 adjuvant breast cancer chemotherapy trial. J Clin Oncol 2008;26:5027–35. 23. O’Malley FP, Chia S, Tu D, et al. Topoisomerase II alpha and responsiveness of breast cancer to adjuvant chemotherapy. J Natl Cancer Inst 2009;101:644–50. 24. Burton JD, Ely S, Reddy PK, et al. CD74 is expressed by multiple myeloma and is a promising target for therapy. Clin Cancer Res 2004;10:6606–11. 25. Riker AIAI, Radfar SS, Liu SS, Wang YY, Khong HTHT. Immunotherapy of melanoma: a critical review of current concepts and future strategies. Expert Opin Biol Ther 2007;7: 345–58. 26. Baughn LB, Di Liberto M, Wu K, et al. A novel orally active small molecule potently induces G1 arrest in primary myeloma cells and prevents tumor growth by specific inhibition of cyclin-dependent kinase 4/6. Cancer Res 2006;66:7661–7.