Department of Laboratory Medicine, Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, 94115, USA.
Merck Research Laboratories, 213 E Grand Avenue, South San Francisco, CA, 94080, USA.
BMC Cancer. 2021 Mar 1;21(1):212. doi: 10.1186/s12885-021-07912-7.
Information regarding response to past treatments may provide clues concerning the classes of drugs most or least likely to work for a particular metastatic or neoadjuvant early stage breast cancer patient. However, currently there is no systematized knowledge base that would support clinical treatment decision-making that takes response history into account.
To model history-dependent response data we leveraged a published in vitro breast cancer viability dataset (84 cell lines, 90 therapeutic compounds) to calculate the odds ratios (log (OR)) of responding to each drug given knowledge of (intrinsic/prior) response to all other agents. This OR matrix assumes (1) response is based on intrinsic rather than acquired characteristics, and (2) intrinsic sensitivity remains unchanged at the time of the next decision point. Fisher's exact test is used to identify predictive pairs and groups of agents (BH p < 0.05). Recommendation systems are used to make further drug recommendations based on past 'history' of response.
Of the 90 compounds, 57 have sensitivity profiles significantly associated with those of at least one other agent, mostly targeted drugs. Nearly all associations are positive, with (intrinsic/prior) sensitivity to one agent predicting sensitivity to others in the same or a related class (OR > 1). In vitro conditional response patterns clustered compounds into five predictive classes: (1) DNA damaging agents, (2) Aurora A kinase and cell cycle checkpoint inhibitors; (3) microtubule poisons; (4) HER2/EGFR inhibitors; and (5) PIK3C catalytic subunit inhibitors. The apriori algorithm implementation made further predictions including a directional association between resistance to HER2 inhibition and sensitivity to proteasome inhibitors.
Investigating drug sensitivity conditioned on observed sensitivity or resistance to prior drugs may be pivotal in informing clinicians deciding on the next line of breast cancer treatments for patients who have progressed on their current treatment. This study supports a strategy of treating patients with different agents in the same class where an associated sensitivity was observed, likely after one or more intervening treatments.
关于既往治疗反应的信息可能为特定转移性或新辅助早期乳腺癌患者最有可能或最不可能起效的药物类别提供线索。然而，目前尚无系统的知识库来支持考虑既往反应的临床治疗决策。
为了对依赖于时间的反应数据进行建模，我们利用已发表的体外乳腺癌生存力数据集（84 个细胞系，90 种治疗性化合物），计算在已知（固有/先前）对所有其他药物的反应的情况下，每种药物的反应几率（log（OR））。该 OR 矩阵假设：（1）反应基于固有特性而不是获得的特性，并且（2）在下次决策点时固有敏感性保持不变。Fisher 精确检验用于识别具有预测性的药物对和药物组（BH p < 0.05）。推荐系统用于根据过去的“历史”反应进一步推荐药物。
在 90 种化合物中，有 57 种化合物的敏感性谱与至少一种其他药物的敏感性谱显著相关，主要是靶向药物。几乎所有关联都是阳性的，一种药物的（固有/先前）敏感性预测了同一或相关类别的其他药物的敏感性（OR > 1）。体外条件反应模式将化合物聚类为五个预测类别：（1）DNA 损伤剂，（2）Aurora A 激酶和细胞周期检查点抑制剂；（3）微管毒物；（4）HER2/EGFR 抑制剂；和（5）PI3K 催化亚基抑制剂。先验算法的实现进一步做出了预测，包括对 HER2 抑制耐药性与蛋白酶体抑制剂敏感性之间的方向性关联。
在为当前治疗进展的患者决定乳腺癌治疗的下一线方案时，调查基于既往药物敏感性或耐药性的药物敏感性可能对告知临床医生至关重要。本研究支持了一种策略，即在观察到相关性时，用同一类别的不同药物治疗患者，这种相关性可能是在一种或多种干预治疗后出现的。
