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Generalizable Biomarkers in Critical Care: Toward Precision Medicine Sweeney, Timothy E. MD, PhD; Khatri, Purvesh PhD Critical Care Medicine: June 2017 - Volume 45 - Issue 6 - p 934–939 doi: 10.1097/CCM.0000000000002402 http://journals./ccmjournal/Fulltext/2017/06000/Generalizable_Biomarkers_in_Critical_Care___Toward.2.aspx REPRODUCIBILITY AND GENERALIZABILITY 可重复性与普适性 The sequencing of the human genome and the subsequent availability of inexpensive, robust methods for “omics” profiling (e.g., genome-wide association studies, gene expression microarrays, and metabolomics) have led to optimism of a new era of biomarkers that would allow for a “precision medicine” approach to critical care. Unfortunately, this promise has yielded few tangible results, as the general biomedical reproducibility crisis (1–3) is particularly troublesome in critical care (4–8) and in omics biomarker studies (9–11). There are two broad problems that lead to seemingly similar studies of biomarkers in critical care producing different results. One problem is traditional nonreproducibility due to false positive biomarker selection or nonrobust statistical models. The other, more importantly, is a lack of generalizability in moving from a narrow study population into broader applications in critical care. We present here a contextual framework for addressing these problems and for assessing new biomarker studies. 可重复性与普适性：人类基因组测序和随后廉价及自动化的“组学”分析（例如，全基因组关联研究，基因表达微阵列和代谢组学）为生物标志物迎来了一个时代，使其能以“精准医学”的方式为重症监护服务。不幸的是，这一愿景取得的实质性成果并不多，主要是因为生物标志物的可重复性难题在重症监护和生物标志物的组学研究中尤其显著。有两个广泛存在的隐患导致相似的重症医学生物标志物研究产生不同的结果： 其一是传统的不可重复性问题，主要是由于选择了假阳性的生物标志物或非强效的统计模型。 另一个，更重要的，相对狭隘的重症医学研究向更广泛的重症监护转化应用时缺乏普适性。 为此作者在此介绍一个解决这些问题及评估新的生物标志物的背景框架。 SYNDROMIC ILLNESS AND GENERALIZABLE BIOMARKERS 综合征性疾患与普适化的生物标志物 Many critical illnesses are defined syndromically, such as sepsis, acute kidney injury (AKI), acute respiratory distress syndrome (ARDS), and delirium. These syndromes typically have clear, though changing, clinical criteria. Still, a syndrome may arise from multiple causes; as a result, it is unclear whether all cases of the syndrome really represent the same disease. Such uncertainty raises a major problem in the field. For example, if a positive clinical trial for adults with ARDS defined by Berlin criteria has failed to reproduce in an independent population of children with ARDS also defined by Berlin criteria, was the original finding a false positive or do adults and children have a “different” version of ARDS? Our reliance on syndromic definitions and the lack of clear gold-standard diagnostics linked to pathophysiology thus makes it difficult to assess clinical trial results. In theory, if the entire clinical spectrum of a disease has a common molecular pathophysiology, then a molecular biomarker should exist that is generalizable to the disease. Thus, finding a generalizable biomarker can help to define the disease, improving both patient care and clinical trial design, and potentially moving a whole field of study forward. 许多重症疾病被定义为综合征，如脓毒症，急性肾损伤（AKI），急性呼吸窘迫综合征（ARDS）和谵妄。 这些综合征通常具有明确的临床标准，尽管其常有变动。然而综合征可能由多种原因引起，因此目前尚不清楚综合征是否真的代表同一种疾病。 这种不确定性是这一领域的一个主要问题。 例如，根据柏林标准定义的成年ARDS临床研究获得的阳性结论，未能在同样根据柏林标准定义的儿童ARDS群体中重现，是成人研究出现了假阳性亦或成年人与儿童属于不同的“ARDS“？ 因缺乏与病理生理学相关的明确的金标准，我们依赖症状性的定义，而而会造成难以评价临床试验。 理论上说，如果一种疾病的全部临床谱具有共同的分子病理生理学机制，那么应该存在一种可广泛用于该疾病的分子生物标志物。 因此，寻找一个普适的生物标志物将有助于确定疾病，改善患者治疗和临床试验设计，并可能推动整个领域的研究。 There are other practical reasons to search for biomarkers that are generalizable. First, requiring context-specific biomarkers for every variant on a clinical condition (e.g., a different biomarker for different sources of sepsis, or for each different cause of kidney injury) could end up requiring dozens of tests for each critical syndrome. Tests indicated for such increasingly fragmented populations will fail to overcome barriers to market entry. In addition, those that do make it may have overly specific indications for use, leaving many patients without help. Finally, since off-label uses of tests and therapies are common, if biomarkers fail to deliver similar performance in seemingly similar conditions, patients will be harmed. 寻找可普适的生物标志物还有其他较为实际的 理由。首先，针对疾患的每种变化对应的专有生物标志物（context-specific biomakers，例如针对脓毒症不同来源或肾损伤不同病因而有不同生物标志物），能够终结每个重症综合征需要一大堆检验的局面。对日益分散的人群进行多项试验会因进入市场的阻碍而失败；此外，要这么做的话显然需要很多特定的适应征，这造成很多患者不会获检。最后，现实中很多检测或治疗超适应征应用很常见，如果生物标志物在相近的病况下不能有类似的表现，那对患者是十分不利的。 We thus argue that research should focus first on finding generalizable, disease-defining molecular biomarkers for syndromes in critical illness, or alternatively on showing that such biomarkers do not exist (evidence-of-absence studies). If no generalizable biomarker exists, then more context-specific biomarkers can direct the effort to accurately characterize clinically actionable syndromic subtypes. In other words, we need to clearly define a disease before we begin to divide it into subtypes. Both are necessary components of a precision medicine approach, but due to the high heterogeneity of critical illness, research of both types can be challenging. 因此，我们认为对于重症领域的综合征而言，应首先重点寻找具有普遍性的，且疾病特异的分子生物标志物，或反过来证明不存在这种生物标志物（无证据支持研究）。 若没有普适性的生物标志物，则应寻找更多的专有标志物（ context-specific biomarkers）以准确地确定综合征的临床亚型。换句话说，在分型之前我们应该清晰地确定疾病。两者都是精准医学的必要组成部分，但由于重症疾病的异质性高，这两种研究都是极具挑战性。 HETEROGENEITY IN CRITICAL ILLNESS AND THE CHALLENGE OF CLINICAL TRIALS 重症疾病的异质性与临床研究的挑战性 Clinical trials in the critical care setting are among the hardest to carry out, for reasons of practicality, patient protection, and patient heterogeneity; this leads to smaller, mostly homogeneous cohorts that do not represent the broad spectrum of critical illness. First, as described above, similar acute syndromes (such as sepsis, AKI, and ARDS) often have multiple possible definitions and span a range of severities. Second, critically ill patients span the entire range of ages, comorbid conditions, and demographics. Third, the medical, surgical, neurologic, and pediatric pathways of critical illness have widely varying primary problems. Fourth, the practicality of conducting a trial leads to differing sampling times and stages of disease at trial enrollment. Finally, the changing treatment patterns over time (such as the change in early sepsis resuscitation with early goal-directed therapy) can lead to different outcomes for the same intervention. The logistical and budgetary constraints of trying to represent all of these sources of heterogeneity means that most single-cohort studies cannot capture the broad spectrum of critical illness, and thus may have difficulty producing generalizable results. 由于实际性、保护患者和异质性的原因，重症监护病房中的临床试验是最难实施的；这导致规模较小，且绝大多数较为同质性的队列研究无法代表危重疾病的广泛的临床表现谱。 首先，如上所述，类似的急性综合征（例如败血症，AKI和ARDS）通常具有多种可能的定义且其严重性也不同； 第二，危重病人的年龄范围，合并疾病和人口特点也完全不同； 第三，内科、外科、神经学和儿科中由原发病导致重症的发病途径明显不同； 第四，实际上开展临床试验招募时也存在采样时机与疾病分期的不同； 最后，不断变化的治疗方式随着时间的推移（例如在脓毒症早期实施复苏的早期目标导向治疗的变化）可能导致相同干预措施的不同结果。 试图囊括所有异质性病源存在着逻辑和预算的限制，这也意味着大多数单中心队列研究不能代表重症疾病的广泛谱系，因此也就难以产生可普适化的结果。 Still, the bedrock of continued progress toward generalizable biomarkers is continued publication of clinical trials. One way to improve trials is to focus on not just size but also heterogeneity. Single-cohort studies are more likely to yield reproducible results when they are appropriately powered, and are more likely to yield generalizable results when they are designed with broad inclusion criteria that attempt to match the full spectrum of the condition under study. Thus, a biomarker that has been tested in 500 adults with pneumonia and ARDS at admission is more reliable than one that has been tested in only 50 such patients. However, until it is tested in children, or in ARDS arising from other causes, or at other clinical timepoints, its generalizability is unknown. We thus caution against the false security of solely relying on a high sample size in evaluating the robustness of a single study. 诚然，通过临床试验仍然不断有普适性的生物标志物研究进展发表。 改进试验的一个方法就是不仅要注重研究规模，而且要注重异质性。若效能合适单中心队列研究很容易产生具有重复性的结果，若宽泛的纳入标准是为了纳入疾病的全部谱系，则更有可能产生具有普适性的结果。 也就是说，在500例住院成人肺炎和ARDS患者之间测试的生物标志物要比仅在50例此类患者中测试的生物标志物更可靠，然而，只有这些检验也在儿童中、在ARDS的不同病因中或在不同的临床时间点进行过，否则其普适性还是未知的。因此，我们在此要提醒谨防虚假的严谨性，即评价单中心研究的效能时仅仅依靠大样本量。 MULTICOHORT ANALYSIS AND DATA SHARING 多队列研究与数据分享 An efficient, inexpensive way of tackling the problem of heterogeneity is to combine studies that represent the broad spectrum of disease. At a fixed total sample size, greater reproducibility is gained when the samples are integrated from a greater number of smaller sized studies, rather than vice versa (15). Our group has worked with many collaborators in repeatedly demonstrating that leveraging biological and technical heterogeneity across multiple cohorts can identify generalizable diagnostic and prognostic biomarkers in a diverse set of diseases including organ transplant, cancer, and autoimmune and infectious diseases (16–24). These early successes of multicohort analysis are firmly rooted in the hypothesis that although a broad representation of a disease could make the discovery of a biomarker challenging; such biomarkers are more likely to be reproducible and generalizable when tested in novel circumstances (Fig. 1). On the other hand, making full use of these studies often requires making imperfect comparisons (e.g., integrating datasets that use multiple different definitions for AKI). Although no hard rule can be set, we feel it both reasonable and pragmatic to use data to their fullest extent, even if the statistical methods are simple or some assumptions are slightly violated, as long as such caveats are fully explained and discussed. 解决异质性问题的一种有效，廉价的方法是对纳入多种疾病谱系的研究的合并。 在研究规模固定的情况下，若是由较小规模的研究合并出较大的样本量时，则研究的可重复性越高，而不是反之亦然。 我们小组与许多合作者的研究多次表明，在包括器官移植，癌症和自身免疫和传染病等多种疾病的队列研究中，生物学与技术的异质性有所增强，但仍能发现具有普适性的诊断与预后标志物。这些早期多队列研究分析的成功是源自这样一个假设：虽然疾病的广泛表现给生物标志物的发现带来挑战，但在新的环境下这样的生物标志物更可能是可重复性及普适性（ 图1 ）。 另一方面，要把这些研究全都用上会造成不完美的比较（例如，整合使用多种不同定义的数据集用于AKI研究）。虽然不能设定硬性规则，但我们认为最大程度地使用数据不仅合理而且是实用的，尽管统计学上较为简单或者某些假设略有偏倚，只要尽可能全面地解释和讨论这些要点即可。 Figure 1. The benefit of incorporating heterogeneity（合并异质性的好处）. Biomarkers discovered in a homogeneous cohort are highly likely to work in external cohorts that are similar to the original cohort, but less likely to work in different settings. Biomarkers that are discovered in heterogeneous cohorts are more likely to be generalizable across a broad spectrum of patients. However, multicohort studies are only possible when data are shared (such as is now required for most genome-wide expression studies). We thus argue for the increased appropriate sharing of molecular data from clinical trials, so that multiple cohorts can be combined in the discovery of new biomarkers (25). In many research areas, data are held privately, preventing such reuse. For instance, we searched the literature for metabolomics, clinical studies in critical care and identified 28 studies (total n = 2,322), out of which only two studies made their raw data publically available (Table 1) (26–53). Public sharing of these data would allow for meta-analysis and data-driven hypotheses generation, avoiding the need for each new cohort to “reinvent the wheel.” It is clear that studies that are performed on single cohorts can be successful at producing robust biomarkers if pitfalls are avoided; but we propose that these investigators make their data available for (and themselves take part in) efforts at later meta-analysis. 不过，多队列研究只有在共享数据时才能进行（如大多数全基因组表达研究所需要的）。 因此，我们认为临床试验的分子数据应适当共享，从而可以将多个队列合并以发现新的生物标志物。 在许多研究领域，数据是私人保有的，无法如此再次使用。 例如，我们在文献中搜索了重症监护代谢组学的临床研究，并确定了28项研究（合计n = 2,322），其中只有两项研究的原始数据为公开可用（ 表1 ）的。 这些数据的公开分享将利于荟萃分析和基于数据的假设，避免了所谓“重新发明轮毂”的新队列研究。显而易见的是，只要能避免缺陷，这些由单一队列研究构成的荟萃将可以成功地产生强大的生物标志物； 我们也建议研究人员们能为之后荟萃分析提供数据 。 TABLE 1. Metabolomics Studies in Critical Care To aid the broader community in this effort, we have made available on our website (http://khatrilab./sepsis) a large number of existing studies of gene expression in sepsis along with source code for analysis (20, 21). This is one resource any researcher can use to further explore their biomarkers in broader clinical context and to test their generalizability in silico prior to embarking on a clinical trial. 为了帮助更广泛的研究团体进行这项工作，我们已经在自己的网站（ http://khatrilab./sepsis ）上提供了大量已有的脓毒症基因表达研究以及分析源代码。 这是研究人员可以用来在更广泛的临床背景下进一步探索其生物标志物的一个资源，并可早与临床试验之前通过电脑模拟其普适性。 BIG DATA AND BIOMARKERS 大数据与生物标志物 One of the biggest benefits of the data-driven omics approach to biomarker discovery is the possibility of discovering novel pathobiology in the heterogeneity of critical illness. Although hypothesis-driven studies of familiar cytokines (e.g., those resulting from activation of the nuclear factor-kB or interferon pathways) may be warranted by preclinical models, many common pathways are activated by multiple stimuli at a cellular level (54) and so are unlikely to be highly specific for a given syndrome. Similarly, clinical scores that use similar data available in an electronic health record (vitals, common laboratories, etc.) are unlikely to be highly specific for multiple conditions. An omics approach, by contrast, can sift through thousands or tens of thousands of candidate biomarkers to find the best fit for a given condition. Unfortunately, the promise of omics is also thus its major pitfall: false positives are likely when there are many more variables than samples in a study. This has contributed to some early failures in the field. It is thus worthwhile to have a general framework with which to approach biomarker development studies. 数据驱动的组学对发现生物标志物最大的好处之一是在危重疾病的异质性中发现新的病理生物学机制。 尽管假说驱动的类似细胞因子的研究（例如激活核因子-kB或干扰素途径的多种研究）可通过临床前模型证明，许多常见的发病途径是在细胞水平上由多重刺激激活，但在指定的综合征中却不一定具有高度特异性。 同样，在电子健康记录中使用类似数据进行临床评分（生命体征，常用实验室检查等），但其对多种疾病不太可能是高度特异性的。 相比之下，一个组学方法可以筛选成千上万个候选生物标志物，从而找出最适合于给定疾病的。 不幸的是，组学能够达成的也正是它的主要缺陷：当一项研究中的变量参数多于样本量时，可能会出现假阳性。 这导致了早期该领域的一些失败。 因此，有必要制定一个总体框架来应对生物标志物开发研究。 BIOMARKER STUDIES: A CONCEPTUAL FRAMEWORK 生物标志物研究：概念架构 There are two excellent guidelines for how to determine the rigor of multivariable prediction models (the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis [TRIPOD] statement [www.tripod-statement.org] [55]) and of diagnostic accuracy studies generally (the Standards for Reporting Diagnostic accuracy studies [STARD] statement [www.stard-statement.org] [56]). In addition to these reporting guidelines, we suggest the following list of questions that we use to help place a study in context: 已有两个极佳的指南用于确定多变量预测模型的精准性（即TRIPOD声明www.tripod-statement.org 和STARD声明 www.stard-statement.org）。 除了这些指南之外，我们还提出下列问题，以帮助您在规定范畴内进行研究： 1. What is the context of the reported diagnostic comparison? Are there existing comparators/gold standards for this question, and what is their diagnostic accuracy in practice? Ideally, a study will compare a new biomarker to a gold standard, possibly with a net reclassification, but this is often not possible and only reported in later validations. 所报告的诊断比较的范畴是什么？ 这个问题是否存在参照物/金标准，在实践中他们的诊断准确度是多少？ 理想情况下，研究中新的生物标志物将与金标准进行比较，可能需要重新分类，但这通常是不可能的，而且仅在以后的验证中报告。 2. How “locked down” is the reported biomarker? Are the biomarkers themselves being selected? Is a statistical model being retrained in the new cohort (i.e., if using a regression model, were the coefficients determined prior to testing)? If a cutoff is used, was it determined prospectively, and is it standard? 2.报告的生物标志物如何“恰中要害”？ 生物标志物是被挑选的吗？ 是否在新队列中重新验证了统计模型（即如果使用回归模型，是否是在测试之前确定的系数）？ 如果使用阈值，是否前瞻性地确定过，是标准的？ 3. How generalizable is the validation cohort being studied? Is this merely a random held-out set of the original discovery cohort? Is it from the same center as the discovery cohort? Does it represent a new area of application? 3.研究验证队列的普适性如何？ 这只是一个随机推出的原始队列吗？ 是与队列相同的中心吗？ 它是否代表了新的应用领域？ 4. Is this biomarker useful? If applied clinically, would it change practice? 4.这个生物标志物有用吗？ 如果临床应用，会改变临床实践吗？ 5. Is there a link to known biology? In our opinion, this may not be necessary at first, especially if the study is searching in spaces that are not well-studied (outside the “street lamp” of common studies). Procalcitonin, for instance, had not been well-characterized as part of the immune response (and the biology remains somewhat unclear today) at its first testing as a biomarker for bacterial infection . 5.与已知生物学有联系吗？ 在我们看来，这可能是没有必要的，特别是如果这项研究处于未被很好研究过的领域（在普通研究的“路灯”之外）。 例如，在降钙素原第一次被作为细菌感染的生物标志物的检查中就没有被充分定性为免疫应答的一部分（并且其生物学至今仍然有些不清楚）。 6. Can the biomarker be measured in a reasonable amount of time to make it useful in critical care? Although not a reason to dismiss results, many of the diagnostic applications in critical care require a rapid turnaround time. A more complex process, or one that relies on new technologies, may take longer to be clinically translated, and will be harder to replicate in validation studies. For example, neutrophilic CD64 as measured by flow cytometry is highly diagnostic for sepsis but has a turnaround time of several hours . 6.生物标志物能否在合理的时间量中检测，以使其在重症医学可用？ 尽管没有理由抛弃研究结果，但重症监护中的许多诊断应用需要快速的周转时间。若监测过程更复杂，或需依赖新技术则其临床转化可能需要更长的时间，并且验证研究中更加难以重现。 例如，通过流式细胞术测量的嗜中性白细胞CD64对于败血症是具有高度诊断性的，但需要数小时的试验周转时间。 In addition, it is helpful to put a study into context in terms of biomarker development (Fig. 2). Early validation may be simply the generation of receiver-operating characteristic curves in similar cohorts to the initial discovery cohort. As evidence accumulates, however, such studies should 1) investigate the application of the biomarker in a broader variety of cohorts that represent the full spectrum of disease and 2) compare the test to known standards for easy comparison. For instance, the later-stage validation of a biomarker for the prognosis of sepsis that is not compared to either lactate or clinical severity scores (e.g., Sequential Organ Failure Assessment) is unhelpful. Similarly, a study examining the diagnostic power for a locked, commercially available biomarker is important, but not as helpful as one examining outcome after intervention. 此外，将研究至于生物标志物的研发会比较好（ 图2 ）。 早期验证可能只是通过与初始研究队列相似的研究中生成操作者特征曲线。 然而，随着证据的积累，这样的研究应该：1）调查生物标志物在更广泛的各种群体中的应用，代表疾病的全部范围; 2）将检测与已知标准进行比较。 例如，在脓毒症预后生物标志物的后期验证中，不与乳酸或临床严重性评分（例如，序贯器官功能衰竭评估）进行比较将是无益的。 类似地，考核一个给定的商业生物标志物的诊断效能是重要的，但不会比考察干预治疗后结更有价值。 Figure 2. Maturity of biomarkers: a conceptual framework. ROC = receiver-operating characteristic. MOVING FORWARD IN THE BIG DATA ERA 走向大数据时代！ The promise of precision medicine is to have the right treatment for the right patient at the right time. In critical care, our immediate need is to get the basics right. For instance, we should first try to answer urgent clinical questions (such as which patients need antibiotics), and then pose new ones that may not have been previously answerable (such as whether there are molecular subtypes of sepsis). As omics and big data technologies proliferate, so too will studies utilizing them as biomarkers in critical illness (studying the genome, epigenome, transcriptome, proteome, metabolome, lipidome, microbiome, and quantified self, to name a few). In all cases, we must remember the extreme heterogeneity of critical illness, and strive for generalizable disease-defining diagnostics and robust biomarkers that can help the entire spectrum of critical care research and delivery. 精准医学承诺的是在正确的时间对正确的患者进行正确的治疗。 在重症医学中，我们迫切需要的是打好基础。例如，我们应首先解决急迫的临床问题（例如哪些患者需要抗生素），然后对以前无法解决的问题提出新的方案（如是否存在脓毒症的分子亚型）。 随着组学和大数据技术的兴起，许多研究也会利用其开展重症疾病的生物标志物的考查（基因组，表观基因组，转录组，蛋白质组学，代谢组学，脂质体，微生物学和自身定量等）。 无论怎样，我们必须记住危重疾病的极端异质性，并致力于探寻具有普适性的判定疾病的诊断方法和强力的生物标志物，以帮助整个重症医学研究和推广。 |
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