Advancing the diagnosis and classification of renal cell carcinomas,BMC Medicine

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Advancing the diagnosis and classification of renal cell carcinomas
BMC Medicine ( IF 9.3 ) Pub Date : 2021-09-06 , DOI: 10.1186/s12916-021-02105-2
Joseph A Rothwell ₁

Affiliation

Abstract

Renal cancer is of concern due to its rising incidence worldwide, with new cases per year expected to climb by as much as 20% by 2030 [1]. Renal neoplasms encompass a variety of malignant and benign tumours with varying prognoses. Accurate classification and diagnosis currently rely on immunohistochemistry to detect known protein markers, in combination with examination of morphological characteristics. Accurate identifications are not always possible, which in turn hinders the delivery of a diagnosis and process to adapted treatment plans. Advanced-stage renal cell carcinomas carry extremely poor prognoses, so improved methods are urgently needed.

High-resolution mass spectrometry (MS) is the cornerstone of proteomics technologies and rapidly evolving tool that promises to drive such methods forward. In MS-based proteomics, large numbers of proteins can be characterised and their absolute amounts measured in solid and liquid biopsies, termed the “total protein approach” (TPA) [2]. Tumours may then be distinguished by the unique concentration ranges of protein biomarkers. With an unparalleled depth of interrogation of tumour proteomes, the technique enables new protein markers of specific neoplasms to be found among the vast number of overexpressed proteins in tumour cells. TPA does not require labelling of the sample or the addition of specific antibodies to the sample, and neither are calibration standards required. While some studies have applied the technique to cell or animal models, only one has been conducted with tissue biopsies from cancer patients [3], whose aim was to elucidate changes in energy metabolism and plasma membrane transport, rather than improve clinical practice and patient outcomes. No previous study has applied TPA to renal neoplasms.

In the new study by Jorge et al. [4], TPA is applied, for the first time, to the diagnosis of renal cell carcinomas. The investigators collected human renal tissue biopsies of malignant clear cell renal cell carcinoma, papillary renal cell carcinoma and chromophobe renal cell carcinoma, as well as the benign renal oncocytoma. Normal adjacent tissue samples were used as a control. It is hypothesised that due to extensive molecular heterogeneity between these tumour types [5], the depth of profiling offered by TPA will allow substantial improvements in classification accuracy over current methods, via the discovery of sensitive and specific biomarker panels.

Proteins were first extracted from 27 tumour biopsies and controls by trypsin digestion and these extracts injected into a liquid chromatography—a mass spectrometry instrument. A total of 1234 proteins were reliably detected in more than one sample type, and these were retained for quantification. Proteomes varied the most between tumour biopsies, while normal adjacent tissue samples were the most homogenous. Statistical analysis identified 205 proteins whose abundances distinguished the different tumour subtypes, of which some were already known as immunohistochemical markers for specific neoplasms. Smaller panels of proteins were then chosen that distinguished each neoplasm from all others and normal tissue. The most discriminant protein for each neoplasm was validated on tissue microarrays in an immunohistochemical analysis of 128 separate renal cell carcinoma and control tissue biopsies. The investigators were able to assess the efficacy of protein markers for each of the malignant renal carcinomas by determining the proportion of cells stained in each tissue type for each protein. PLIN2 was thus proposed as a sensitive and specific marker of clear cell renal cell carcinoma, while beta-tubulin III, diffuse LAMP1 and HK1 were proposed as efficient markers of papillary renal cell carcinoma, chromophobe renal cell carcinoma and renal oncocytoma, respectively.

The findings were further strengthened by their biological plausibility. Of the 81 discriminant proteins for clear cell renal cell carcinoma, 46 had been described previously, of which the authors highlight thymidine phosphorylase and PLIN2 as mediators of cell proliferation and the inhibition of apoptosis. The similarity of renal carcinoma proteomes to each other was consistent with their cellular origins, either cells of the proximal tubules or intercalated cells of the distal nephron and collecting ducts. Moreover, the benign renal oncocytoma proteome was most similar to that of normal adjacent tissue, lending additional confidence in the context of deployment in clinical settings.

Proteomics analyses have previously been applied to tumour classification using gel-based protein quantification, including for renal cell carcinomas [6], but the study of Jorge et al. makes a convincing case that MS-based TPA will be able to offer superior speed and accuracy. Some barriers will need to be overcome before proteomics, and TPA will be able to offer an alternative to immunohistochemistry in clinical settings. Firstly, MS-based platforms for proteomic analysis remain costly to establish. Secondly, although suitable instruments are becoming more affordable, reference databases of tumour TPA concentrations will be required and are not currently available. In the meantime, TPA will undoubtedly develop as a valuable method of discovering new protein biomarkers for use in existing immunohistochemistry diagnosis. Further on, given the minimal sample preparation required and potential for accurate high-throughput analyses, the authors conclude that it could potentially become a standard diagnostic tool, aiding accurate prognoses and guiding treatment plans for advanced stage renal carcinomas. A limitation of the study of Jorge et al. is the low number of biopsies per neoplasm analysed in the study, given the known inter-tumour and intra-tumour variability in renal neoplasms [7]. Future validations will aim to scale up analyses to larger patient cohorts.

Not applicable.

1.
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Ca-a Cancer Journal for Clinicians. 2021;71(3):209–49. https://doi.org/10.3322/caac.21660.
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Jorge S, Capelo JL, LaFramboise W, Satturwar S, Korentzelos D, Bastacky S, et al. Absolute quantitative proteomics using the total protein approach to identify novel clinical immunohistochemical markers in renal neoplasms. BMC Med. 2021. https://doi.org/10.1186/s12916-021-02071-9.
5.
Clark DJ, Zhang H. Proteomic approaches for characterizing renal cell carcinoma. Clin Proteomics 2020;17:28.
6.
Chinello C, L'Imperio V, Stella M, Smith AJ, Bovo G, Grasso A, et al. The proteomic landscape of renal tumors. Expert Review of Proteomics. 2016;13(12):1103–20. https://doi.org/10.1080/14789450.2016.1248415.
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7.
Gerlinger M, Rowan AJ, Horswell S, Larkin J, Endesfelder D, Gronroos E, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012;366(10):883–92. https://doi.org/10.1056/NEJMoa1113205.
CAS Article PubMed PubMed Central Google Scholar

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Affiliations

Université Paris-Saclay, UVSQ, Inserm, Gustave Roussy, Exposome and Heredity team (CESP U1018), Villejuif, F-94805, France
Joseph A. Rothwell

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JR was the sole author. The author read and approved the final manuscript.

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Correspondence to Joseph A. Rothwell.

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Rothwell, J.A. Advancing the diagnosis and classification of renal cell carcinomas. BMC Med 19, 221 (2021). https://doi.org/10.1186/s12916-021-02105-2

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Received: 23 August 2021
Accepted: 23 August 2021
Published: 06 September 2021
DOI: https://doi.org/10.1186/s12916-021-02105-2

Keywords

Renal neoplasia
Proteomics
Total protein approach
Renal cancer diagnosis

中文翻译：

推进肾细胞癌的诊断和分类

摘要

肾癌因其在全球范围内的发病率不断上升而备受关注，预计到 2030 年每年新增病例将增加 20% [1]。肾肿瘤包括多种具有不同预后的恶性和良性肿瘤。准确的分类和诊断目前依赖于免疫组织化学来检测已知的蛋白质标记物，并结合形态学特征的检查。准确的识别并不总是可能的，这反过来又阻碍了诊断和流程的交付，以适应调整后的治疗计划。晚期肾细胞癌预后极差，因此迫切需要改进的方法。

高分辨率质谱 (MS) 是蛋白质组学技术和快速发展的工具的基石，有望推动此类方法向前发展。在基于 MS 的蛋白质组学中，可以表征大量蛋白质，并在固体和液体活检中测量它们的绝对数量，称为“总蛋白质方法”(TPA) [2]。然后可以通过蛋白质生物标志物的独特浓度范围来区分肿瘤。凭借无与伦比的肿瘤蛋白质组研究深度，该技术能够在肿瘤细胞中大量过表达的蛋白质中发现特定肿瘤的新蛋白质标记物。TPA 不需要标记样品或向样品中添加特异性抗体，也不需要校准标准。虽然一些研究已将该技术应用于细胞或动物模型，只有一项对癌症患者的组织活检进行了 [3]，其目的是阐明能量代谢和质膜转运的变化，而不是改善临床实践和患者预后。之前没有研究将 TPA 应用于肾肿瘤。

在 Jorge 等人的新研究中。[4]，TPA首次应用于肾细胞癌的诊断。研究人员收集了恶性透明细胞肾细胞癌、乳头状肾细胞癌和嫌色肾细胞癌以及良性肾嗜酸细胞瘤的人肾组织活检。正常邻近组织样本用作对照。据推测，由于这些肿瘤类型之间存在广泛的分子异质性 [5]，TPA 提供的分析深度将允许通过发现敏感和特定的生物标志物面板，显着提高当前方法的分类准确性。

首先通过胰蛋白酶消化从 27 个肿瘤活检组织和对照中提取蛋白质，然后将这些提取物注入液相色谱仪——一种质谱仪。在不止一种样品类型中可靠地检测到总共 1234 种蛋白质，并保留这些蛋白质用于定量。蛋白质组在肿瘤活检之间的差异最大，而正常的相邻组织样本是最同质的。统计分析确定了 205 种蛋白质，其丰度区分了不同的肿瘤亚型，其中一些已经被称为特定肿瘤的免疫组织化学标志物。然后选择较小的蛋白质组，将每种肿瘤与所有其他肿瘤和正常组织区分开来。在对 128 个单独的肾细胞癌和对照组织活检的免疫组织化学分析中，在组织微阵列上验证了对每种肿瘤最具辨别力的蛋白质。研究人员能够通过确定每种蛋白质在每种组织类型中染色的细胞比例来评估蛋白质标记物对每种恶性肾癌的功效。因此，PLIN2 被提议作为透明细胞肾细胞癌的敏感和特异性标志物，而β-微管蛋白 III、弥漫性 LAMP1 和 HK1 被提议分别作为乳头状肾细胞癌、嫌色肾细胞癌和肾嗜酸细胞瘤的有效标志物。研究人员能够通过确定每种蛋白质在每种组织类型中染色的细胞比例来评估蛋白质标记物对每种恶性肾癌的功效。因此，PLIN2 被提议作为透明细胞肾细胞癌的敏感和特异性标志物，而β-微管蛋白 III、弥漫性 LAMP1 和 HK1 被提议分别作为乳头状肾细胞癌、嫌色肾细胞癌和肾嗜酸细胞瘤的有效标志物。研究人员能够通过确定每种蛋白质在每种组织类型中染色的细胞比例来评估蛋白质标记物对每种恶性肾癌的功效。因此，PLIN2 被提议作为透明细胞肾细胞癌的敏感和特异性标志物，而β-微管蛋白 III、弥漫性 LAMP1 和 HK1 被提议分别作为乳头状肾细胞癌、嫌色肾细胞癌和肾嗜酸细胞瘤的有效标志物。

他们的生物学合理性进一步加强了这些发现。在用于透明细胞肾细胞癌的 81 种鉴别蛋白中，46 种先前已被描述，其中作者强调胸苷磷酸化酶和 PLIN2 作为细胞增殖和细胞凋亡抑制的介质。肾癌蛋白质组彼此的相似性与其细胞起源一致，无论是近端肾小管的细胞还是远端肾单位和集合管的插入细胞。此外，良性肾嗜酸细胞瘤蛋白质组与正常邻近组织的蛋白质组最相似，这为临床环境中的部署提供了额外的信心。

蛋白质组学分析先前已应用于使用基于凝胶的蛋白质定量的肿瘤分类，包括肾细胞癌 [6]，但 Jorge 等人的研究。令人信服的理由是，基于 MS 的 TPA 将能够提供卓越的速度和准确性。在蛋白质组学之前需要克服一些障碍，TPA 将能够在临床环境中提供免疫组织化学的替代方案。首先，用于蛋白质组学分析的基于 MS 的平台的建立成本仍然很高。其次，虽然合适的仪器变得越来越便宜，但仍需要肿瘤 TPA 浓度的参考数据库，目前尚不可用。同时，TPA 无疑将发展成为发现用于现有免疫组织化学诊断的新蛋白质生物标志物的一种有价值的方法。进一步，鉴于所需的样品制备最少，并且有可能进行准确的高通量分析，作者得出结论，它有可能成为标准的诊断工具，有助于准确预测并指导晚期肾癌的治疗计划。Jorge 等人研究的局限性。考虑到肾肿瘤中已知的肿瘤间和肿瘤内变异性，研究中分析的每个肿瘤的活检数量较少 [7]。未来的验证将旨在将分析扩大到更大的患者队列。考虑到肾肿瘤中已知的肿瘤间和肿瘤内变异性，研究中分析的每个肿瘤的活检数量较少 [7]。未来的验证将旨在将分析扩大到更大的患者队列。考虑到肾肿瘤中已知的肿瘤间和肿瘤内变异性，研究中分析的每个肿瘤的活检数量较少 [7]。未来的验证将旨在将分析扩大到更大的患者队列。

不适用。

1.
Sung H、Ferlay J、Siegel RL、Laversanne M、Soerjomataram I、Jemal A 等。2020 年全球癌症统计数据：GLOBOCAN 对 185 个国家/地区 36 种癌症的全球发病率和死亡率的估计。Ca-临床医生的癌症杂志。2021;71(3):209-49。https://doi.org/10.3322/caac.21660。
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Wisniewski JR、Hein MY、Cox J、Mann M. 用于蛋白质拷贝数和浓度估计的“蛋白质组标尺”，无需掺入标准。Mol 细胞蛋白质组学。2014；13(12)：3497-506。https://doi.org/10.1074/mcp.M113.037309。
CAS 文章 PubMed PubMed Central Google Scholar
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Wisniewski JR、Dus-Szachniewicz K、Ostasiewicz P、Ziolkowski P、Rakus D、Mann M。结直肠粘膜、腺瘤和癌症的绝对蛋白质组分析揭示了脂肪酸代谢和质膜转运蛋白的剧烈变化。J蛋白质组研究。2015;14(9):4005–18。https://doi.org/10.1021/acs.jproteome.5b00523。
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Jorge S、Capelo JL、LaFramboise W、Satturwar S、Korentzelos D、Bastacky S 等。使用总蛋白方法的绝对定量蛋白质组学在肾肿瘤中鉴定新的临床免疫组织化学标志物。BMC 医学。2021. https://doi.org/10.1186/s12916-021-02071-9。
5.
Clark DJ、Zhang H. 表征肾细胞癌的蛋白质组学方法。临床蛋白质组学 2020；17:28。
6.
Chinello C、L'Imperio V、Stella M、Smith AJ、Bovo G、Grasso A 等。肾肿瘤的蛋白质组学景观。蛋白质组学专家评论。2016;13(12):1103-20。https://doi.org/10.1080/14789450.2016.1248415。
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Gerlinger M、Rowan AJ、Horswell S、Larkin J、Endesfelder D、Gronroos E 等。多区域测序揭示的肿瘤内异质性和分支进化。N Engl J Med。2012；366（10）：883-92。https://doi.org/10.1056/NEJMoa1113205。
CAS 文章 PubMed PubMed Central Google Scholar

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Université Paris-Saclay, UVSQ, Inserm, Gustave Roussy, Exposome and Heredity team (CESP U1018), Villejuif, F-94805, France
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