BMC Medicine ( IF 7.0 ) Pub Date : 2021-08-18 , DOI: 10.1186/s12916-021-02052-y Lishu He 1
Pancreatic cancer is predicted to become the second leading cause of cancer-related deaths in the USA by 2030 with a 5-year survival rate of only 9% [1, 2]. Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic malignancy and remains a treatment-refractory disease. So far, apart from surgical resection, conventional chemotherapies, radiotherapies, few therapeutic strategies, adjuvant or neoadjuvant, are available for improved PDAC patient benefit. In recent years, more research efforts have been devoted to developing targeted therapeutics via discovery of novel molecular vulnerabilities and molecular subtyping of various diseases. For PDAC, despite many attempts to refine its molecular taxonomy over the years, the current molecular subtyping still does not efficiently inform novel molecular vulnerabilities for the development of targeted therapies. Thus, fine-tuning the resolution of PDAC molecular subtyping has become a pivotal need.
Here, we discuss a manuscript from Hwang et al. which focused on the optimization of a single-nucleus RNA-sequencing (snRNA-seq) technique to understand how preoperative treatment may impact residual tumors. Moreover, we examine how this technique can further contribute to the field by identifying additional molecular vulnerabilities that can be harnessed for informative stratification during PDAC patient clinical management and targeted combinations with neoadjuvant therapies [3].
Single-cell technologies, especially single-cell RNA-seq, have been regularly used in elucidating intertumoral tumor microenvironment (TME) and heterogeneity as well as expanding upon data from traditional bulk RNA profiling of various tumors. However, the use of scRNA-seq is not as prevalent in PDAC as in other cancers due to high intrinsic nuclease content and dense desmoplastic stroma. High-resolution transcriptional networks and any resulting patient stratification from bulk RNA analyses also tend to be obscured because of lower-quality sample collection due to the complicated PDAC TME. Hwang and colleagues propose an optimized snRNA-seq technique for better recovery of PDAC cancer cell profile without compromising the spectrum of cell states. The authors demonstrate, with frozen archival specimens not commonly considered for analysis, the potential of snRNA-seq to capture various cell types with comparable quality to that obtained from gold standard multiplex profiling in situ. This technique also enables processing of banked frozen tissue samples dating back at least 7 years, while bypassing some of the challenges involved in sample preparation for traditional single-cell RNA-seq, such as balancing sample viability and accurate cell type representation. Thus, the use of snRNA-seq provides an exciting avenue to further resolve PDAC molecular subtypes and gain novel insights for precision medicine approaches based on tumor reprogramming profile.
Apart from accurately capturing the malignant and non-malignant compartments of human PDAC tumors, the snRNA-seq analyses, combined with spatially resolved transcriptomics, also revealed a novel, clinically relevant molecular taxonomy with better patient stratification potential than that from the two previously identified consensus molecular subtypes, basal-pancreatic and classical-pancreatic [4]. Under the refined molecular taxonomy of PDAC proposed in the preprint, patients can be further stratified into prognostic risk groups based on malignant cell and cancer-associated fibroblast programs beyond just their conventional, consensus tumor profile.
As a demonstration of the optimized snRNA-seq technique, Hwang et al. studied differential gene expression programs and cell types across untreated and neoadjuvant chemotherapy and radiotherapy (CRT)-treated frozen archival PDAC samples. Following CRT, PDAC tumors experienced an increase in immune filtration along with a shift in cell composition and transcriptional programming from a more classical-like profile to a more basal-like profile. Interestingly, a more immunogenic environment, despite the induced pro-resistance cell state to CRT, may render the tumor more susceptible to immunotherapies. As PDAC treatments evolve into the era of precision medicine and combination therapies, future studies are warranted to explore CRT’s potential sensitization of PDAC with CRT-induced basal-like phenotype to some immunotherapies. It would also be of interest to examine multi-compartment reprogramming following combinatorial treatments of CRT and targeted immunotherapies based on patient stratification.
In summary, Hwang et al. have developed an optimized snRNA-seq technique with frozen archival specimens to further dissect the PDAC transcriptional response to CRT and refine clinically relevant molecular subtypes. The authors identify differential cell state programs in pre- and post-CRT malignant cells and demonstrate a refined molecular taxonomy of PDAC.
It should be noted, however, that the study was limited in that it did not include matched pre-and post-treatment specimens as an additional control or a sample size large enough to exclude the potential impact of the patient’s specific treatment regimen. As the frozen archival samples only included primary tumors, it would also be valuable in future studies to include metastatic tumor samples in analyses. There have also been other studies establishing PDAC patient stratification via transcriptomic signatures with cancer-associated fibroblast programs and microenvironment features taken into account ([5], for example). It would better inform the utility of snRNA-seq technique discussed here in clinical management to compare stratification patterns by those studies to the snRNA-seq-based stratification.
Nevertheless, Hwang and colleagues brought forth an impressive study supporting the increasing value of single-nucleus sequencing technologies to resolve cell state dynamics of complicated, treatment-refractory malignancies, such as PDAC, and better stratify patients for targeted therapy based on their molecular subtypes.
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Collisson EA, Sadanandam A, Olson P, Gibb WJ, Truitt M, Gu S, et al. Subtypes of pancreatic ductal adenocarcinoma and their differing responses to therapy. Nat Med. 2011;17(4):500–3. https://doi.org/10.1038/nm.2344.
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The author would like to acknowledge Dr. Gwen Lomberk for her insightful and helpful comments during the preparation of this commentary.
Preprint
This MedView was written to highlight the following preprint – Single-nucleus and spatial transcriptomics of archival pancreatic cancer reveals multi-compartment reprogramming after neoadjuvant treatment (https://www.biorxiv.org/content/10.1101/2020.08.25.267336v1) [3].
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Department of Pharmacology & Toxicology, Medical College of Wisconsin, Milwaukee, WI, USA
Lishu He
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LH prepared the manuscript. The author read and approved the final manuscript.
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Lishu He is a PhD candidate at the Medical College of Wisconsin. Her research focuses on epigenomic-based pharmacology and small molecule therapeutics in pancreatic cancer.
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Correspondence to Lishu He.
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He, L. Refining pancreatic ductal adenocarcinoma molecular subtype and precision therapeutics with single-nucleus RNA-seq. BMC Med 19, 182 (2021). https://doi.org/10.1186/s12916-021-02052-y
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DOI: https://doi.org/10.1186/s12916-021-02052-y
中文翻译：
用单核 RNA-seq 精炼胰腺导管腺癌分子亚型和精准治疗
预计到 2030 年，胰腺癌将成为美国癌症相关死亡的第二大原因，其 5 年生存率仅为 9% [1, 2]。胰腺导管腺癌 (PDAC) 是最常见的胰腺恶性肿瘤，并且仍然是一种难治性疾病。到目前为止，除了手术切除外，常规化疗、放疗、辅助或新辅助治疗策略很少，可用于改善 PDAC 患者的益处。近年来，更多的研究工作致力于通过发现新的分子脆弱性和各种疾病的分子亚型来开发靶向疗法。对于 PDAC，尽管多年来多次尝试改进其分子分类，当前的分子亚型仍然不能有效地为靶向治疗的开发提供新的分子脆弱性。因此，微调 PDAC 分子亚型的分辨率已成为关键需求。
在这里，我们讨论了 Hwang 等人的手稿。它专注于优化单核 RNA 测序 (snRNA-seq) 技术，以了解术前治疗如何影响残留肿瘤。此外，我们研究了该技术如何通过识别可用于 PDAC 患者临床管理和靶向组合与新辅助治疗的信息分层的其他分子漏洞来进一步对该领域做出贡献 [3]。
单细胞技术，尤其是单细胞 RNA-seq，经常用于阐明肿瘤间肿瘤微环境 (TME) 和异质性，以及扩展来自各种肿瘤的传统批量 RNA 分析的数据。然而，由于高内在核酸酶含量和致密的促纤维增生基质，scRNA-seq 在 PDAC 中的使用不像在其他癌症中那样普遍。由于复杂的 PDAC TME 导致样本收集质量较低，高分辨率转录网络和任何由此产生的大量 RNA 分析的患者分层也往往被掩盖。Hwang 及其同事提出了一种优化的 snRNA-seq 技术，可以在不影响细胞状态谱的情况下更好地恢复 PDAC 癌细胞谱。作者证明，冷冻档案标本通常不考虑用于分析，就地。该技术还可以处理至少可追溯到 7 年前的银行冷冻组织样本，同时绕过了传统单细胞 RNA-seq 样本制备所涉及的一些挑战，例如平衡样本活力和准确的细胞类型表示。因此，snRNA-seq 的使用为进一步解析 PDAC 分子亚型提供了一条令人兴奋的途径，并为基于肿瘤重编程谱的精准医学方法获得新见解。
除了准确捕获人类 PDAC 肿瘤的恶性和非恶性区室外，snRNA-seq 分析与空间分辨转录组学相结合，还揭示了一种新的、临床相关的分子分类法，其患者分层潜力比之前确定的两个共识更好分子亚型，基底胰腺和经典胰腺[4]。根据预印本中提出的 PDAC 的精细分子分类法，可以根据恶性细胞和癌症相关的成纤维细胞程序，将患者进一步分为预后风险组，而不仅仅是传统的、共识的肿瘤特征。
作为优化的 snRNA-seq 技术的演示，Hwang 等人。研究了未经处理和新辅助化疗和放疗 (CRT) 处理的冷冻存档 PDAC 样本的差异基因表达程序和细胞类型。在 CRT 之后，PDAC 肿瘤经历了免疫过滤的增加以及细胞组成和转录程序的转变，从更经典的样貌转变为更基础的样貌。有趣的是，尽管诱导了对 CRT 的亲抗性细胞状态，但更具免疫原性的环境可能会使肿瘤更容易受到免疫疗法的影响。随着 PDAC 治疗进入精准医学和联合治疗时代，未来的研究有必要探索 CRT 对具有 CRT 诱导的基底样表型的 PDAC 对某些免疫疗法的潜在敏感性。
总之，黄等人。已经开发出一种优化的 snRNA-seq 技术，使用冷冻档案标本进一步剖析 PDAC 对 CRT 的转录反应，并完善临床相关的分子亚型。作者确定了 CRT 前后恶性细胞的不同细胞状态程序，并展示了 PDAC 的精细分子分类。
然而，应该指出的是，该研究的局限性在于它没有包括匹配的治疗前和治疗后样本作为额外的对照或样本量大到足以排除患者特定治疗方案的潜在影响。由于冷冻档案样本仅包括原发性肿瘤，因此在未来的研究中将转移性肿瘤样本包括在分析中也很有价值。还有其他研究通过转录组特征建立 PDAC 患者分层，并考虑了癌症相关的成纤维细胞程序和微环境特征（例如 [5]）。将这些研究的分层模式与基于 snRNA-seq 的分层进行比较，将更好地告知此处讨论的 snRNA-seq 技术在临床管理中的效用。
尽管如此，Hwang 及其同事提出了一项令人印象深刻的研究，支持单核测序技术在解决复杂、难治性恶性肿瘤（如 PDAC）的细胞状态动态方面的价值不断增加，并根据分子亚型更好地对患者进行靶向治疗分层。
- 1.
Rahib L、Smith BD、Aizenberg R、Rosenzweig AB、Fleshman JM、Matrisian LM。预测到 2030 年的癌症发病率和死亡人数：美国甲状腺癌、肝癌和胰腺癌的意外负担。癌症研究。2014;74(11):2913-21。https://doi.org/10.1158/0008-5472.CAN-14-0155。
CAS 文章 PubMed Google Scholar
- 2.
Siegel RL、Miller KD、Jemal A. 癌症统计数据，2020 年。CA Cancer J Clin。2020;70(1):7–30。https://doi.org/10.3322/caac.21590。
文章 谷歌学术
- 3.
Hwang WL、Jagadeesh KA、Guo JA、Hoffman HI、Yadollahpour P、Mohan R 等。存档胰腺癌的单核和空间转录组学揭示了新辅助治疗后的多室重编程。生物Rxiv。2020. https://doi.org/10.1101/2020.08.25.267336。
- 4.
Collisson EA、Sadanandam A、Olson P、Gibb WJ、Truitt M、Gu S 等。胰腺导管腺癌的亚型及其对治疗的不同反应。纳特医学。2011;17(4):500–3。https://doi.org/10.1038/nm.2344。
CAS 文章 PubMed PubMed Central Google Scholar
- 5.
Puleo F、Nicolle R、Blum Y 等。基于肿瘤和微环境特征的胰腺导管腺癌分层。胃肠病学。2018;155(6):1999–2013.e3。https://doi.org/10.1053/j.gastro.2018.08.033。
文章 PubMed Google Scholar
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作者要感谢 Gwen Lomberk 博士在准备本评论期间提供的富有洞察力和有益的评论。
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编写此 MedView 是为了突出以下预印本——存档胰腺癌的单核和空间转录组学揭示了新辅助治疗后的多室重编程（https://www.biorxiv.org/content/10.1101/2020.08.25.267336v1）[3] ]。
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美国威斯康星州密尔沃基威斯康星医学院药理学和毒理学系
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LH 准备了手稿。作者阅读并批准了最终手稿。
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何丽树是威斯康星医学院的博士研究生。她的研究重点是基于表观基因组的药理学和胰腺癌的小分子疗法。
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He, L. 使用单核 RNA-seq 精炼胰腺导管腺癌分子亚型和精准治疗。BMC Med 19, 182 (2021)。https://doi.org/10.1186/s12916-021-02052-y
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