Tumor chemosensitivity and chemoresistance assays (1997)

In their article Tumor Chemosensitivity and Chemoresistance Assays, Brown and Markman1 present perhaps the best review of this subject to date. For chemosensitivity assays, the authors correctly state that "the relevant outcome is whether assay-directed chemotherapy regimens actually result in improved survival of the patient." No assay-directed chemosensitivity study has demonstrated a significant improvement in patient survival compared with empirical therapy. Correlations between in vitro sensitivity and clinical response are fair at best. A meta-analysis of chemosensitivity assays revealed an overall positive predictive accuracy of 69%.2 Oncologists have considered this degree of accuracy much too low to be acceptable in routine clinical practice for selecting therapy, especially in diseases for which standard and effective chemotherapy protocols exist. The accuracy of chemosensitivity assays suffers from many factors that are difficult, if not impossible, to simulate in the laboratory. Host elements work strongly against accurate in vitro predictions of clinical response in the following ways. (1) Drug delivery is decreased in poorly perfused tumors and in tumor sanctuaries (blood-brain barrier). (2) Altered metabolism in the host's liver may influence activation of pro-drugs (e.g., cyclophosphamide). (3) Individual host sensitivities of normal tissues (bone marrow or stomach mucosa) can lead to life-threatening toxicity, requiring a cutback in drug dosages. (4) Local areas of hypoxia or acidosis may inactivate certain drugs. (5) Finally, some tumors have host-dependent resistance mechanisms that cause high false-positive predictions of in vitro chemosensitivity.3 Pharmacokinetic modeling is difficult in the laboratory. No in vitro technology is sensitive enough to measure the five to six logs of cell kill required to achieve a clinical response.4 Furthermore, testing of combination chemotherapy is difficult in vitro. Drug scheduling and detection of synergistic or antagonistic interactions are not feasible outside of the research setting. One of the most important factors influencing the accuracy of in vitro predictive assays is the nature of cancer itself. The predictive accuracy of any laboratory test is a function of both the characteristics of the technology and the patient population to which the test is applied, and is expressed by Bayes' theorem.5 The application of Bayes' theorem to chemosensitivity and chemoresistant predictive assays has been reviewed.6-8 Chemosensitivity assays were originally modeled after bacterial culture-and-sensitivity tests. However, infectious diseases are highly curable for the most part, and many effective antibiotics are on the market. In contrast, cancers, especially the common solid tumors, are generally chemoresistant. Outcomes of chemosensitivity assays are "held captive by the general ineffectiveness of chemotherapy for solid tumors."1 Clinical trials to validate chemosensitivity assays have historically been conducted in refractory diseases against which standard first-line and second-line regimens have failed. In prospective, randomized studies, Von Hoff et al.9, 10 found no differences in survival for patients with advanced metastatic cancer who received assay-directed therapy compared with patients treated with "clinician's choice." Could anyone have expected some other outcome? Any quest to find "needle-in-a-haystack" effective salvage therapy by using a chemosensitivity assay is doomed to failure because of the nature of the disease and the dearth of effective chemotherapeutic agents. Chemosensitivity assays would be expected to be most accurate for chemoresponsive diseases, such as testicular cancers and Hodgkin's disease. Paradoxically, therapeutic regimens for these diseases are highly curative, and there is no need for a predictive assay for first-line therapy. In contrast to the poor accuracy of chemosensitivity assays, drug resistance assays have very high accuracy. The chemoresistance assay of Kern, known as the Extreme Drug Resistance (EDR) assay (Oncotech, Inc., Irvine, CA), has been shown in clinical trials to have a greater than 99% accuracy in predicting what drugs will not effect a clinical response.6 This was achieved, in part, by exposing the cancer cells to suprapharmacologic exposures of anticancer drugs. Two independent studies7, 11 have confirmed Kern's original observation that the EDR assay has a greater than 99% negative predictive value. The high accuracy of the EDR assay is independent of disease type and applies to refractory sarcomas and melanomas, as well as to untreated ovarian and breast carcinomas. The underlying disease population strongly affects the accuracy of chemosensitivity assays, limiting the reliability of in vitro assays for selecting clinically active drugs. Host factors, discussed above, render chemosensitivity assays unreliable. By contrast, host factors favor accurate predictions of clinical drug resistance. If cancer cells survive high exposures of drugs in the laboratory, host factors cannot make the cells responsive in vivo. Questions of combination testing and synergy are relevant for chemosensitivity assays. In the EDR assay, however, all drugs are tested as single agents. This is the only reliable way to detect Extreme Drug Resistance. If combinations are tested, only one drug out of two or three in the combination need be active for the combination to show "sensitivity," and drug resistance would be masked. Synergy may play a significant role in some drug combinations, such as has been observed with 5-fluorouracil and methotrexate and with cisplatin and etoposide.12, 13 However, synergy is only found in drugs that have significant activity against cancer cells. When tumor cells have Extreme Drug Resistance to a particular anticancer drug, the modulating effects of other agents are nullified.14, 15 The application of chemosensitivity assays to diseases for which standard treatments are available (e.g., breast carcinoma) may point clinicians toward ad hoc, nonstandard treatments if the two or three "best" drugs are picked for a particular patient. In addition to the poor accuracy of chemosensitivity assays, ethical and medical-legal issues make the clinician wary of this approach. The EDR assay, however, can always be used within standard guidelines of oncology practice to eliminate ineffective single agents when the probability of clinical response is highly remote (less than 1%). In breast carcinoma, for example, patients whose tumors have Extreme Drug Resistance to 5-fluorouracil may be treated with doxorubicin and cyclophosphamide (AC) rather than cyclophosphamide, methotrexate, and 5-fluorouracil (CMF). Patients with doxorubicin-resistant cancers might receive CMF as adjuvant therapy (perhaps paclitaxel for metastatic disease). In terms of clinical validation, the answer to the question "What outcomes are to be expected from the EDR assay?" is simple: avoidance of unnecessary treatments and toxicity. Brown and Markman incorrectly conclude that "when one drug is eliminated on the basis of chemoresistance assays, it is implied that the remaining drugs, which do not show chemoresistance, are potentially more effective."1 However, there are no published data showing improved overall survival for patients who were treated with drugs selected on the basis of nonresistance in vitro. For any particular patient, the EDR assay identifies drugs that will not be clinically effective. Other therapies may also be ineffective in that patient, even if those drugs do not show Extreme Drug Resistance in vitro. Even a drug that kills every last cancer cell in the Petri dish may be ineffective in the patient. Some oncologists argue that improved patient survival is the relevant outcome for chemoresistance assays. In fact, the converse is true. Clinical failure is the relevant outcome for chemoresistance assays. It is well documented that clinical response is necessary, but not sufficient, to achieve prolonged survival in cancer patients. Without a response, therapy is ineffective. The ability to predict clinical failure with 99% accuracy is the distinguishing feature of the EDR assay. If one wanted to conduct a randomized trial of the EDR assay with survival as the outcome, the assay-directed arm would have patients treated only with drugs to which their tumors had Extreme Drug Resistance. A statistically shortened overall survival would then be the relevant endpoint. Should this trial be done? Of course not. It would be unethical and scientifically useless in view of the overwhelming published evidence showing convincing correlations between in vitro resistance and clinical failure. Finally, Brown and Markman ask if the elimination of ineffective therapies is sufficient to justify the routine use of drug resistance assays. The answer depends on the particular clinical situation, as it does for every other test and intervention in medical practice today. The EDR assay finds its greatest utility in settings where (1) the oncologist is considering several drugs or treatment regimens for a particular patient (e.g., CMF vs. AC vs. FAC for breast carcinoma); (2) the treatment options being considered have significantly different toxicities and costs (e.g., cisplatin plus either cyclophosphamide or paclitaxel for ovarian carcinoma); (3) no standard regimens exist (e.g., for nonsmall cell lung carcinoma and tumors of unknown primary site); (4) refractory cancers are being treated, for which options include salvage therapy, experimental therapy, referral to a clinical trial, or supportive care. Smith and Bodurtha16 argue that avoidance of unnecessary suffering, injury, or harm should be considered in oncology decision-making, because "Beneficence, or doing good, is enmeshed with nonmaleficence, avoiding harm". In addition, Smith17 points out that "The coming crunch in health care economics will dictate that we cannot spend money on ineffective therapies." The Extreme Drug Resistance assay accurately identifies ineffective chemotherapy and helps the oncologist avoid unnecessary treatments and toxicity. David H. Kern Ph.D.