《自然》：EGFR突变分组可以更好地为癌症患者匹配最佳治疗药物

资讯作者：University of Texas M.D. Anderson Cancer Center

编辑翻译：奇奇

本文献于2021年9月15日发表在国际著名杂志《自然》上。文中来自德克萨斯大学MD安德森癌症中心的科研人员研究了表皮生长因子受体(EGFR)突变，这将为非小细胞肺癌患者的治疗药物匹配提供一个标准，也有助于开发新的癌症治疗策略。

德克萨斯大学MD安德森癌症中心的研究人员发现，根据结构和功能对表皮生长因子受体(EGFR)突变进行分组，为非小细胞肺癌(NSCLC)患者与正确的药物匹配提供了一个精准的框架。发表在《自然》杂志上的研究确定了4个突变亚群，并引入了一种测试酪氨酸激酶抑制剂(TKIs)的新策略，以及获得批准的靶向治疗的即时临床机会。

该研究的资深作者、胸头颈肿瘤医学主席John Heymach医学博士说：“已经在患者中发现了70多种不同的EGFR突变，但药物只批准用于其中少数。我们的研究的直接意义之一是发现现有的治疗方法可能对许多这些突变有效。对于某些突变，较早的药物可能效果更好，而对于其他突变，较新的药物效果更好。目前，在缺乏指导的情况下，临床医生经常使用最新的治疗方法来治疗所有的EGFR突变。这种模型可以帮助我们立即为患者选择更好的治疗方法，并有希望为特定的突变亚群开发更好的药物。”

第一代、第二代和第三代TKIs使用不同的机制靶向EGFR蛋白。Heymach和他的团队发现，基于特定组内的突变如何在功能上影响蛋白质上的药物结合袋，药物对某些特定亚组的效果更好。

该团队确定了4个EGFR突变的NSCLC亚组，它们分别是：

1.典型突变，对药物结合几乎没有影响

2.T790M型突变，在疏水性裂隙中包含至少一个突变，通常在对第一代靶向治疗产生耐药性后获得

3.外显子20环状插入型突变，其特征是在外显子20的αc-螺旋的c端后环状插入了额外的氨基酸

4.P-loopαC-螺旋压缩(PACC)型突变位于ATP结合口袋的内表面或αC-螺旋的C-末端

目前在EGFR突变型NSCLC中测试新药的方法是基于外显子编号，这表明突变发生在DNA的线性部分内。在临床研究和实验室模型中，通过外显子对突变进行分组的结果大多不一致。作者指出，这似乎表明外显子数量与药物敏感性或耐药性之间的相关性较差。

Heymach说：“在给定的外显子内，突变的差异很大。我们根据它们如何影响EGFR结构和药物结合来组织突变，这允许同时在结构相似的突变组中测试药物。我们相信，这可能成为对突变进行分类和描述，然后将它们与正确的药物配对的新标准方法。”

在这项研究中，研究人员分析了来自五个不同患者数据库的16175名EGFR突变NSCLC患者的数据。记录了11619名患者的原发性和共发性突变。其中，67.1%为典EGFR 突变，30.8%为非典型EGFR突变，2.2%为两者共有突变。

对于典型和非典型EGFR突变，研究小组分析了治疗失败时间(TTF)，这是癌症对治疗产生耐药性的一个指标。研究人员发现，不管治疗类型如何，非典型突变患者的TTF较短，总生存率较低。经第一代和第三代TKIs治疗的典型突变患者的TTF更长。

研究人员随后创建了一个由76个EGFR突变的细胞系组成的小组，并针对18种EGFR抑制剂筛选了这些细胞系，发现了四个不同的亚组。通过比较亚组和外显子与药物敏感性的相关性，表明基于结构的亚组比基于外显子的组更具有预测性。

典型突变对所有类型的TKIs都敏感，特别是第三代TKIs。外显子20环插入型突变仍然是最具异质性的亚组，某些突变对第二代TKIs的响应最好。T790M型突变对ALK和PKC抑制剂敏感，部分突变对第三代TKIs保持敏感性。PACC突变对第二代TKIs最敏感。

该研究还强调了对所有新诊断或复发的NSCLC患者进行生物标志物检测的重要性。当前的下一代测序方法有能力检测已知的致癌因子EGFR突变的全谱，几乎所有这些都属于四个基于结构的亚组之一。作者指出，这对罕见突变尤其重要，因为通过基于个体突变的传统临床试验方法很难研究罕见突变。未来的前瞻性研究将有助于完善和告知亚组的框架。

Heymach说：“这对患者来说是一个重要的进步，因为目前还没有FDA批准的针对大多数EGFR突变的靶向疗法，这让临床医生无法确定哪种突变应该使用什么药物治疗。现在，根据突变所处的结构群，我们可以更好地为给定的突变匹配最佳药物。进一步来说，通过测试针对整个结构相似的突变组的药物，而不是针对单个突变，这可能还有助于集中药物开发工作。”

英语原文

Classifying EGFR Mutations by Structure Offers Better Way to Match Non-small Cell Lung Cancer Patients to Treatments

Researchers from The University of Texas MD Anderson Cancer Center have discovered that grouping epidermal growth factor receptor (EGFR) mutations by structure and function provides an accurate framework to match patients with non-small cell lung cancer (NSCLC) to the right drugs. The findings, published in Nature, identify four subgroups of mutations and introduce a new strategy for testing tyrosine kinase inhibitors (TKIs), as well as instant clinical opportunities for approved targeted therapies.

"More than 70 different EGFR mutations have been identified in patients, but drugs have only been approved for a handful of them. One of the immediate implications of our research is the discovery that therapies we already have may work for many of these mutations. For some mutations, older drugs may actually work better, and for other mutations, newer drugs work better," said John Heymach, M.D., Ph.D., chair of Thoracic/Head & Neck Medical Oncology and senior author of the study. "Right now, in the absence of guidance, clinicians often use the newest treatment for all EGFR mutations. This model can help us pick better therapies for patients immediately and hopefully develop better drugs for specific subgroups of mutations."

First-, second- and third-generation TKIs use different mechanisms to target the EGFR protein. Heymach and his team found that drugs work better for certain subgroups based on how the mutations within a given group functionally impact the drug-binding pocket on the protein.

The four EGFR-mutant NSCLC subgroups identified by the team are:

1.Classical-like mutations, with little to no impact on drug binding

2.T790M-like mutations, which contain at least one mutation in the hydrophobic cleft and often are acquired after resistance to a first-generation targeted therapy

3.Exon 20 loop insertion mutations, characterized by insertions of additional amino acids in the loop after the C-terminal end of the αC-helix in exon 20

4.P-loop αC-helix compression(PACC) mutations on the interior surface of the ATP binding pocket or C-terminal end of the αC-helix

The current approach to testing new drugs in EGFR-mutant NSCLC is based on exon number, which indicates where the mutation occurs within a linear portion of the DNA. Grouping mutations by exon has produced mostly heterogeneous results in clinical studies and laboratory models, which the authors note seems to indicate a poor correlation between exon number and drug sensitivity or resistance.

"Within a given exon, mutations vary widely. We organized mutations based on how they impact the EGFR structure and drug binding instead, which allows for testing a drug across a whole group of mutations that are structurally similar at the same time," Heymach said. "We believe this could become the new standard approach for classifying and describing mutations and then pairing them with the right drug."

For this study, the researchers analyzed data from 16,175 patients with EGFR-mutant NSCLC from five different patient databases. Primary and co-occurring mutations were recorded for 11,619 patients. Of those, 67.1% had classical EGFR mutations, 30.8% had atypical EGFR mutations and 2.2% had both.

For both classical and atypical EGFR mutations, the team analyzed the time to treatment failure (TTF), an indication of how quickly a cancer becomes resistant to therapy. The researchers found a shorter TTF and lower overall survival for patients with atypical mutations regardless of treatment type. Patients with classical mutations treated with first- and third-generation TKIs had a longer TTF.

The researchers then created a panel of 76 cell lines with EGFR mutations and screened those cell lines against 18 EGFR inhibitors, which revealed the four distinct subgroups. Comparing the correlation to drug sensitivity by subgroup, versus exons, showed that the structure-based subgroups were more predictive than exon-based groups.

Classical-like mutations were sensitive to all classes of TKIs, particularly third-generation TKIs. Exon 20 loop insertion mutations remained the most heterogeneous subgroup, with certain mutations responding best to second-generation TKIs. T790M-like mutations were sensitive to ALK and PKC inhibitors, with some mutations retaining sensitivity to third-generation TKIs. PACC mutations were most sensitive to second-generation TKIs.

The study also highlights the importance of biomarker testing for all patients with a new diagnosis or recurrence of NSCLC. Current next-generation sequencing methods have the ability to detect the full spectrum of known oncogenic driver EGFR mutations, virtually all of which fall into one of the four structure-based subgroups. The authors note that this is especially important for rare mutations, which are more difficult to study through a traditional clinical trial approach based on individual mutations. Future prospective studies will help refine and inform the subgroup framework.

"This is an important advance for patients because, right now, there is no FDA-approved targeted therapy for the majority of EGFR mutations, leaving clinicians in the dark as to what drug touse for which mutation," Heymach said. "Now, based on the structural group in which the mutation falls, we can better match the best drug for agiven mutation. Going forward, this may also help focus drug development efforts, by testing drugs against an entire group of mutations that are structurally similar, rather than against individual mutations."

参考文献

Structure-based classification predicts drug response in EGFR-mutant NSCLC, Nature (2021). DOI: 10.1038/s41586-021-03898-1

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