HIGHLY SPECIFIC PREDICTION OF ANTINEOPLASTIC DRUG-RESISTANCE WITH AN INVITRO ASSAY USING SUPRAPHARMACOLOGIC DRUG EXPOSURES
HIGHLY SPECIFIC PREDICTION OF ANTINEOPLASTIC DRUG-RESISTANCE WITH AN INVITRO ASSAY USING SUPRAPHARMACOLOGIC DRUG EXPOSURES
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DOI：
10.1093/jnci/82.7.582
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发表时间：
1990-04-04
影响因子：
10.3
通讯作者：
WEISENTHAL, LM
中科院分区：
文献类型：
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作者：
KERN, DH;WEISENTHAL, LM
贝叶斯定理被用来描述预测检验的准确性（后测概率）和被测事物的总体发生率（前测概率）之间的关系。贝叶斯定理表明，实验室测定在预测具有高总体响应率的肿瘤中的临床耐药性方面是准确的（例如，先前未治疗的乳腺癌），仅当测定对耐药性具有极高（> 98%）特异性时。我们开发了一种高度特异性的耐药性测定方法，其中将人肿瘤集落培养在软琼脂中，并在高浓度下长时间暴露于药物中进行测试。浓度系数×时间超过同期研究报告的时间约100倍。我们回顾了8年期间450项检测结果与临床反应之间的相关性。结果进行了分析的子集，包括不同的肿瘤组织学，单一药物，药物组合。极端耐药性（测定结果≥SD低于中位数），特异性大于90%。在127名表现出极端耐药性的肿瘤患者中，只有一名对化疗有反应。该可忽略的测试后响应概率与测试前（预期）响应概率无关。一旦确定了显示极端耐药性的肿瘤患者人群，其余患者队列的后测应答概率根据检测结果和前测应答概率而变化，准确地根据基于贝叶斯定理的预测。这一发现允许构建诺模图，用于确定试验预测的响应概率。
Bayes'' theorem has been used to describe the relationship between the accuracy of a predictive test (posttest probability) and the overall incidence of what is being tested (pretest probability). Bayes'' theorem indicates that laboratory assays will be accurate in the prediction of clinical drug resistance in tumors with high overall response rates (e.g., previously untreated breast cancer) only when the assays are extremely (> 98%) specific for drug resistance. We developed a highly specific drug-resistance assay in which human tumor colonies were cultured in soft agar and drugs were tested at high concentrations for long exposure times. Coefficients for concentration .times. time exceeded those reported in contemporaneous studies by about 100-fold. We reviewed 450 correlations between assay results and clinical response over an 8-year period. Results were analyzed by subsets, including different tumor histologies, single agents, and drug combinations. Extreme drug resistance (an assay result .gtoreq. SD below the median) was identified with greater than 90% specificity. Only one of 127 patients with tumors showing extreme drug resistance responded to chemotherapy. This negligible post-test probability of response was independent of pretest (expected) probability of response. Once this population of patients with tumors showing extreme drug resistance had been identified, posttest response probabilities for the remaining cohorts of patients varied according to both assay results and pretest response probabilities, precisely according to predictions based on Bayes'' theorem. This finding allowed the construction of a nomogram for determining assay-predicted probability of response.