Abstract：
The causes of variation in toxicity to the same treatment regimen among seemingly similar patients remain largely unknown. There was tremendous optimism that the patient\'s germline genome would be strongly predictive of treatment-related toxicity and could be used to personalize treatment and improve therapeutic outcomes. However, there has been limited success in discovering robust pharmacogenetic predictors of treatment-related toxicity and even less progress in translating the few validated predictors into clinical practice. It is apparent that identification of toxicity predictors that can be used to predict and prevent treatment-related toxicity will require thinking beyond germline genomics. To that end, we propose an integrated biomarker discovery approach that recognizes that a patient\'s toxicity risk is determined by the cumulative effects of a broad range of \"omic\" and non-omic factors. This commentary describes the limited success in discovering and translating clinical and pharmacogenetic toxicity predictors into clinical practice. We illustrate the evolution of cancer toxicity biomarker discovery and translation through studies of taxane-induced peripheral neuropathy, which is one of the most common and debilitating side effects of cancer treatment. We then discuss the opportunities for discovering non-genomic (e.g., metabolomic, lipidomic, transcriptomic, proteomic, microbiomic, medical, behavioral, environmental) and integrated biomarkers that may be more strongly predictive of toxicity risk and the potential challenges with translating integrated biomarkers into clinical practice. This integrated biomarker discovery approach may circumvent some of the major limitations in toxicity biomarker science and move precision oncology treatment forward so that patients receive maximum treatment benefit with minimal toxicity.
摘要：
在看似相似的患者中，相同治疗方案的毒性变化的原因仍然未知。人们非常乐观地认为，患者的种系基因组将有力地预测治疗相关的毒性，并可用于个性化治疗和改善治疗结果。然而,在发现治疗相关毒性的可靠药物遗传学预测因子方面取得的成功有限,在将少数经过验证的预测因子转化为临床实践方面进展甚微.显然,鉴定可用于预测和预防治疗相关毒性的毒性预测因子将需要超越种系基因组学的思考。为此,我们提出了一种综合的生物标志物发现方法，该方法认识到患者的毒性风险是由广泛的“组学”和非组学因素的累积效应决定的。本评论描述了在发现临床和药物遗传毒性预测因子并将其转化为临床实践方面的有限成功。我们通过紫杉烷诱导的周围神经病变的研究来说明癌症毒性生物标志物发现和翻译的演变，这是癌症治疗中最常见和最令人衰弱的副作用之一。然后，我们讨论发现非基因组的机会(例如，代谢组学，脂质体，转录组，蛋白质组学，微生物组学，medical,行为，环境）和整合的生物标志物，这些生物标志物可能更有力地预测毒性风险和将整合的生物标志物转化为临床实践的潜在挑战。这种整合的生物标志物发现方法可以规避毒性生物标志物科学中的一些主要限制，并向前推进精确肿瘤学治疗，以便患者在最小毒性的情况下获得最大的治疗益处。
