JoVE Journal > Cancer Research
Summary
Abstract
Introduction
Protocol
Results
Discussion
Disclosures
Acknowledgements
Materials
References
Automatic Translation
Please note that all translations are automatically generated. Click here for the English version.
Cancer Research

使用结构DNA改变分析和患者衍生异种移植测试癌症的靶向疗法

Published: July 25, 2020
doi:
10.3791/60646
,
1Center for Individualized Medicine,Mayo Clinic, 2Department of Molecular Pharmacology and Experimental Therapeutics,Mayo Clinic

Summary

在这里，我们提出了一个方案，以测试基于肿瘤的基因组构成选择的靶向疗法的疗效。该协议描述了结构DNA重组的识别和验证，将患者的肿瘤引入小鼠，并测试对相应药物的反应。

Abstract

我们在这里介绍一种综合方法，用于测试靶向疗法的疗效，该方法结合了下一代测序技术、治疗靶点分析和药物反应监测，使用患者衍生异种移植（PDX）。以卵巢肿瘤为例验证了此策略。配合对下一代测序 （MPSeq） 协议用于识别结构变化，然后分析潜在的可定位变化。在免疫功能低下的小鼠中生长的人类肿瘤使用基于基因组分析选择的药物进行治疗。结果表明，在PDX模型中，预测响应与观测响应之间具有良好的相关性。提出的方法可用于测试联合治疗的疗效，并协助复发性癌症患者进行个性化治疗，特别是在标准治疗失败且需要使用标签外药物的情况下。

Introduction

患者衍生的异种移植（PDX），这是从患者肿瘤片段植入免疫缺陷小鼠中产生的，已成为一个强大的临床前模型，以帮助个性化的抗癌护理。PDX模型已成功开发用于各种人类恶性肿瘤。这些包括乳腺癌和卵巢癌，恶性黑色素瘤，结肠直肠癌，胰腺癌，和非小细胞肺癌1，2，3，4，5。2,3,4,51肿瘤组织可以植入正畸或异位。前者被认为更准确，但技术上是困难的，涉及直接移植到肿瘤起源的器官。这些类型的模型被认为是精确模仿原始肿瘤的组织学由于”自然”微环境的肿瘤6，7。,7例如，正畸移植到小鼠卵巢的毛刺导致肿瘤扩散到腹腔和产生腹水，典型的卵巢癌8。同样，将乳腺肿瘤注射到胸腔而不是腹部乳腺中，也影响了PDX的成功率和行为9。然而，正畸模型需要复杂的成像系统来监测肿瘤的生长。实体肿瘤的异位植入通常通过将组织植入小鼠的皮下侧翼进行，这样可以更容易地监测肿瘤的生长，而且成本更低，耗时更耗时。然而，肿瘤生长皮下很少转移不像在正畸植入10的情况下观察到的。

包皮的成功率已证明不同，并在很大程度上取决于肿瘤类型。据报道，含有较高百分比肿瘤细胞的更具侵略性的肿瘤和组织标本的成功率为12，13。,13与这一点一致，从转移位点衍生的肿瘤被证明以50-80%的频率生长，而来自原位点的肿瘤以低至14%的频率12。相比之下，含有坏死细胞和存活性肿瘤细胞较少的组织感染不良。肿瘤生长也可以通过添加地下室膜基质蛋白到组织组合时注射到小鼠14，而不影响原肿瘤的特性。还发现用于植入的组织件的大小和数量会影响植入的成功率。与皮下植入相比，由于肾下胶囊能够维持原有的肿瘤频闪并提供宿主频闪细胞以及15，因此在肾后胶囊中植入肿瘤的接受率更高。

大多数研究使用NOT/SCID免疫缺陷小鼠，缺乏自然杀伤细胞16，并已被证明增加肿瘤移植，生长和转移相比，其他菌株14。然而，需要额外的监测，因为他们可能发展胸腺淋巴瘤早在3-4个月的13岁。在SCID小鼠生长的卵巢肿瘤移植中，B细胞的生长被利托西马成功抑制，防止淋巴瘤的发展，但不会影响卵巢肿瘤的移植17。

最近，NSG（NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ）小鼠，携带编码白细胞间2受体伽马链18的基因的空突变，成为PDX模型的生成常用菌株。据报道，从已建立的PDX模型传至后代小鼠的肿瘤，在3至6代19、20代中保留组织学和分子特性。许多研究表明，PDX模型中的治疗结果模仿了相应的患者2、3、4、21、22、23。,4,21,22,232,非小肺癌和结直肠癌PDX模型对化疗的响应率与同一药物24、25的临床试验相似。在PDX模型中进行的研究，为参加临床试验的患者开发，表明对测试药物的反应类似于在相应患者2，3，43的临床观察。2

结合 PDX 模型对患者肿瘤进行高通量基因组分析，为研究特定基因组变化与治疗反应之间的相关性提供了一个强大的工具。这些已经在几个出版物26，27,描述。例如，在一组携带EGFR扩增的结直肠PDX模型中对EGFR抑制剂cetuximab的治疗反应，在28的患者中对切图西马布的临床反应。

PDX 模型的开发和应用存在一些挑战。其中肿瘤异质性29，30，,30可能损害治疗反应解释的准确性，因为单细胞克隆在PDX中具有较高的增殖能力，可以超过其他31，从而导致异质性的损失。此外，当单个肿瘤活检用于开发PDX时，一些细胞群体可能会错过，不会代表在最后的移植。建议从同一肿瘤的多个样本进行植入，以解决此问题。虽然PDX肿瘤往往包含原始供体肿瘤的所有细胞类型，但这些细胞逐渐被那些穆林起源3的细胞替代。PDX模型中的乳氨酸频闪细胞与人类肿瘤细胞之间的相互作用尚不清楚。然而，频闪细胞被证明可以重述肿瘤微环境33。

尽管有这些限制，PDX 模型仍然是最有价值的转化研究工具之一，以及选择患者疗法的个性化医学。PDX 的主要应用包括生物标志物发现和药物测试。PDX模型还成功地用于研究耐药性机制和确定克服耐药性34，35,的策略。本手稿中描述的方法使研究人员能够识别人类肿瘤中潜在的治疗靶点，并评估体内相应药物的疗效，在携带最初基因组特征的内藏肿瘤的小鼠中。该协议使用卵巢肿瘤，在内包，但适用于任何类型的肿瘤足够侵略性生长在小鼠2，3，12。3,122

Protocol

根据梅奥诊所机构审查委员会（IRB）批准的一项协议，在切除手术时收集了同意卵巢癌患者的新鲜组织。本协议中使用的所有动物程序和治疗都得到梅奥诊所机构动物护理和使用委员会 （IACUC） 的批准，并遵循动物护理指南。 1. 配合对测序和分析 注：新鲜或闪光冷冻组织必须用于配合对（MPseq）测序。石蜡嵌入材料不适合，因为它含有支离破碎的DNA。 …

Representative Results

脱毛手术时从切除卵巢肿瘤中的组织根据 IRB 指导收集，用于 1） 基因组表征和 2） 免疫功能低下小鼠（图1）。配合测序协议36，37用于识别DNA的结构变化，包括损耗、增益和扩增。36,图2显示了一个肿瘤（指定为OC101）中显示基因组变化情况的代表性基因组图谱。发现高品位血清亚型肿瘤的典型，发现多个增?…

Discussion

我们描述了我们在PDX模型中用于进行”临床试验”的方法和方案，这些方法和方案利用基因组分析获得的肿瘤分子特性来确定用于测试的药物的最佳选择。多个测序平台目前用于原发肿瘤的基因组表征，包括全基因组测序、RNAseq 和定制基因面板。对于高品位血清卵巢癌，Mpseq 识别结构变化，DNA重排和拷贝数变化，是特别有用的，因为在这种类型的肿瘤中观察到高度的基因组不稳定。Mpseq 平台的第二?…

Disclosures

The authors have nothing to disclose.

Acknowledgements

我们感谢梅奥临床个人医学中心（CIM）的成员林阳博士和Faye R. Harris，MS，帮助进行实验。这项工作得到了尼尔·埃克尔斯先生和夫人向梅奥临床个性化医学中心（CIM）赠送的礼物的支持。

Materials

3M Vetbond 3M, Co. 1469SB
anti-AKT antibody Cell Signaling Technologies, Inc. 9272
Anti-GAPDH antibody(G-9) Santa Cruz Biotech. Inc. sc-365062
Anti-MAPK antibody Cell Signaling Technologies, Inc. 9926
Anti-phospho-AKT antibody Cell Signaling Technologies, Inc. 9271
Anti-mTOR antibody Cell Signaling Technologies, Inc. 2972
Anti-Phospho-mTOR antibody Cell Signaling Technologies, Inc. 2971
Anti-Phospho-S6 antibody Cell Signaling Technologies, Inc. 4858
Anti-Rictor antibody Cell Signaling Technologies, Inc. 2114
Anti-S6 antibody Cell Signaling Technologies, Inc. 2217
Captisol ChemScene, Inc. cs-0731
Carboplatin NOVAPLUS, Inc. 61703-360-18
DMEM Mediatech, Inc. 10-013-CV
Easy-A Hi-Fi PCR Cloning Enzyme Agilent, Inc. 600404-51
Lubricant Cardinal Healthcare 82-280
Matrigel Corning, Inc. 356234
McCoy's media Mediatech, Inc. 10-050-CV
MK-2206 ApexBio, Inc. A3010
MK-8669 ARIAD Pharmaceuticals, Inc. AP23573
Nair Sensitive Skin Church & Dwight Co. Nair Hair Remover Shower Power Sensitive
NOD/SCID mice Charles River, Inc. NOD.CB17-Prkdcscid/NCrCrl
Paclitaxel NOVAPLUS, Inc. 55390-304-05
PEG400 Millipore Sigma, Inc. 88440-250ML-F
Perjeta Genetech, Co. Pertuzumab
Rituximab Genetech, Co. Rituxan
RPMI1640 Mediatech, Inc. 10-040-CV
SCID mice Harlan Laboratories, Inc. C.B.-17/IcrHsd-PrkdcscidLystbg
SLAx 13-6MHz linear transducer FUJIFILM SonoSite, Inc HFL38xp
SonoSite S-series Ultrasound machine FUJIFILM SonoSite, Inc SonoSite SII
Tween 80 Millipore Sigma, Inc. P4780-100ML
DOWNLOAD MATERIALS LIST

References

  1. Tentler, J. J., et al. Patient-derived tumour xenografts as models for oncology drug development. Nature Reviews Clinical Oncology. 9, 338-350 (2012).
  2. Marangoni, E., et al. A new model of patient tumor-derived breast cancer xenografts for preclinical assays. Clinical Cancer Research. 13, 3989-3998 (2007).
  3. Zhang, X., et al. A renewable tissue resource of phenotypically stable, biologically and ethnically diverse, patient-derived human breast cancer xenograft models. Cancer Research. 73, 4885-4897 (2013).
  4. Hidalgo, M., et al. Patient-derived xenograft models: an emerging platform for translational cancer research. Cancer Discovery. 4, 998-1013 (2014).
  5. Weroha, S. J., et al. Tumorgrafts as in vivo surrogates for women with ovarian cancer. Clinical Cancer Research. 20, 1288-1297 (2014).
  6. Rubio-Viqueira, B., et al. Optimizing the development of targeted agents in pancreatic cancer: tumor fine-needle aspiration biopsy as a platform for novel prospective ex vivo drug sensitivity assays. Molecular Cancer Therapeutics. 6, 1079-1088 (2007).
  7. Rubio-Viqueira, B., Hidalgo, M. Direct in vivo xenograft tumor model for predicting chemotherapeutic drug response in cancer patients. Clinical Pharmacology and Therapeutics. 85, 217-221 (2009).
  8. Ricci, F., et al. Patient-derived ovarian tumor xenografts recapitulate human clinicopathology and genetic alterations. Cancer Research. 74, 6980-6990 (2014).
  9. Fleming, J. M., et al. Local regulation of human breast xenograft models. Journal of Cellular Physiology. 224, 795-806 (2010).
  10. Hoffman, R. M. Patient-derived orthotopic xenografts: better mimic of metastasis than subcutaneous xenografts. Nature Reviews Cancer. 15, 451-452 (2015).
  11. Jung, J., Seol, H. S., Chang, S. The Generation and Application of Patient-Derived Xenograft Model for Cancer Research. Cancer Research and Treatment. 50, 1-10 (2018).
  12. Sivanand, S., et al. A validated tumorgraft model reveals activity of dovitinib against renal cell carcinoma. Science Translational Medicine. 4, 137-152 (2012).
  13. Pavía-Jiménez, A., Tcheuyap, V. T., Brugarolas, J. Establishing a human renal cell carcinoma tumorgraft platform for preclinical drug testing. Nature Protocols. 9, 1848-1859 (2014).
  14. Fridman, R., Benton, G., Aranoutova, I., Kleinman, H. K., Bonfil, R. D. Increased initiation and growth of tumor cell lines, cancer stem cells and biopsy material in mice using basement membrane matrix protein (Cultrex or Matrigel) co-injection. Nature Protocols. 7, 1138-1144 (2012).
  15. Cutz, J. C., et al. Establishment in severe combined immunodeficiency mice of subrenal capsule xenografts and transplantable tumor lines from a variety of primary human lung cancers: potential models for studying tumor progression-related changes. Clinical Cancer Research. 12, 4043-4054 (2006).
  16. Siolas, D., Hannon, G. J. Patient-derived tumor xenografts: transforming clinical samples into mouse models. Cancer Research. 73, 5315-5319 (2013).
  17. Butler, K. A., et al. Prevention of Human Lymphoproliferative Tumor Formation in Ovarian Cancer Patient-Derived Xenografts. Neoplasia. 19, 628-636 (2017).
  18. Cao, X., et al. Defective lymphoid development in mice lacking expression of the common cytokine receptor gamma chain. Immunity. 2, 223-238 (1995).
  19. Dobbin, Z. C., et al. Using heterogeneity of the patient-derived xenograft model to identify the chemoresistant population in ovarian cancer. Oncotarget. 5, 8750-8764 (2014).
  20. Choi, Y. Y., et al. Establishment and characterisation of patient-derived xenografts as paraclinical models for gastric cancer. Scientific Reports. 6, 22172 (2016).
  21. Malaney, P., Nicosia, S. V., Davé, V. One mouse, one patient paradigm: New avatars of personalized cancer therapy. Cancer Letters. 344, 1-12 (2014).
  22. Rosfjord, E., Lucas, J., Li, G., Gerber, H. P. Advances in patient-derived tumor xenografts: from target identification to predicting clinical response rates in oncology. Biochemical Pharmacology. 91, 135-143 (2014).
  23. Braekeveldt, N., Bexell, D. Patient-derived xenografts as preclinical neuroblastoma models. Cell and Tissue Research. 372, 233-243 (2018).
  24. ‘Perez-Soler, R., et al. Response and determinants of sensitivity to paclitaxel in human non-small cell lung cancer tumors heterotransplanted in nude mice. Clinical Cancer Research. 6, 4932-4938 (2000).
  25. Fichtner, I., et al. Anticancer drug response and expression of molecular markers in early-passage xenotransplanted colon carcinomas. European Journal of Cancer. 40, 298-307 (2004).
  26. Gao, H., et al. High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response. Nature Medicine. 21, 1318-1325 (2015).
  27. Izumchenko, E., et al. Patient-derived xenografts effectively capture responses to oncology therapy in a heterogeneous cohort of patients with solid tumors. Annals of Oncology. 28, 2595-2605 (2017).
  28. Bertotti, A., et al. A molecularly annotated platform of patient-derived xenografts (“xenopatients”) identifies HER2 as an effective therapeutic target in cetuximab-resistant colorectal cancer. Cancer Discovery. 1, 508-523 (2011).
  29. Mengelbier, L. H., et al. Intratumoral genome diversity parallels progression and predicts outcome in pediatric cancer. Nature Communications. 27, 6125 (2015).
  30. McGranahan, N., Swanton, C. Clonal Heterogeneity and Tumor Evolution: Past, Present, and the Future. Cell. 168, 613-628 (2017).
  31. Marusyk, A., et al. Non-cell-autonomous driving of tumour growth supports sub-clonal heterogeneity. Nature. 514, 54-58 (2014).
  32. Braekeveldt, N., et al. Neuroblastoma patient-derived orthotopic xenografts reflect the microenvironmental hallmarks of aggressive patient tumours. Cancer Letters. 375, 384-389 (2016).
  33. DeRose, Y. S., et al. Tumor grafts derived from women with breast cancer authentically reflect tumor pathology, growth, metastasis and disease outcomes. Nature Medicine. 17, 1514-1520 (2011).
  34. Das Thakur, M., et al. Modelling vemurafenib resistance in melanoma reveals a strategy to forestall drug resistance. Nature. 494, 251-255 (2013).
  35. Girotti, M. R., et al. Application of Sequencing, Liquid Biopsies, and Patient-Derived Xenografts for Personalized Medicine in Melanoma. Cancer Discovery. 6, 286-299 (2016).
  36. Murphy, S. J., et al. Mate pair sequencing of whole-genome-amplified DNA following laser capture microdissection of prostate cancer. DNA Research. 19, 395-406 (2012).
  37. Smadbeck, J. B., et al. Copy number variant analysis using genome-wide mate-pair sequencing. Genes Chromosomes and Cancer. 57, 459-470 (2018).
  38. Kovtun, I. V., et al. Lineage relationship of Gleason patterns in Gleason score 7 prostate cancer. Cancer Research. 73, 3275-3284 (2013).
  39. Paster, E. V., Villines, K. A., Hickman, D. L. Endpoints for mouse abdominal tumor models: refinement of current criteria. Comparative Medicine. 59, 234-241 (2009).
  40. Schneider, C. A., Rasband, W. S., Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nature Methods. 9, 671-675 (2012).
  41. Cheng, Y., et al. MK-2206, a novel allosteric inhibitor of Akt, synergizes with gefitinib against malignant glioma via modulating both autophagy and apoptosis. Molecular Cancer Therapeutics. 11, 154-164 (2012).
  42. Rivera, V. M., et al. Ridaforolimus (AP23573; MK-8669), a potent mTOR inhibitor, has broad antitumor activity and can be optimally administered using intermittent dosing regimens. Molecular Cancer Therapeutics. 10, 1059-1071 (2011).
  43. Heitjan, D. F., Manni, A., Santen, R. J. Statistical analysis of in vivo tumor growth experiments. Cancer Research. 53, 6042-6050 (1993).
  44. Vargas, R., et al. Case study: patient-derived clear cell adenocarcinoma xenograft model longitudinally predicts treatment response. NPJ Precision Oncology. 2, 14 (2018).
  45. Harris, F. R., et al. Targeting HER2 in patient-derived xenograft ovarian cancer models sensitizes tumors to chemotherapy. Molecular Oncology. 13, 132-152 (2019).
  46. Fidler, I. J., et al. Modulation of tumor cell response to chemotherapy by the organ environment. Cancer and Metastasis Reviews. 13, 209-222 (1994).

Tags

CancerTargeted TherapyStructural DNA AlterationPatient-derived XenograftsWhole Genome SequencingGenomic AlterationDruggable ChangesIn Vivo TreatmentTreatment EfficacyAlgorithmMicrosequencingGene SymbolsPathway AnalysisFisher’s Exact TestDruggable GenesGene Cards

Play Video
PDF
DOI
DOWNLOAD MATERIALS LIST
Cite This Article
Zhang, P., Kovtun, I. V. Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts. J. Vis. Exp. (161), e60646, doi:10.3791/60646 (2020).
Download Citation
Reprints and Permissions

View Video

✖

To prove you're not a robot, please enter the text in the image below

CAPTCHA Image
Play CAPTCHA Audio
Refresh Image