基于胃癌细胞及其肿瘤微环境中关键分子的研究
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摘要
在全球，每年癌症新发病例为1,270万，死亡病例为760万。而胃癌居我国恶性肿瘤发病率的第二位，死亡率的第三位。尽管这些年，早期筛查技术有所提升以及以手术治疗为主的综合防治措施的有了长足的改进，但胃癌根治性切除后5年转移复发率极高，其根本问题在于肿瘤的侵袭转移，因此胃癌侵袭转移机理研究及综合防治措施一直是肿瘤学界研究的重点。近年来，越来越多的研究显示肿瘤侵袭转移是一个多阶段、多因素逐级发展的渐进过程，不仅涉及癌细胞自身，也通过肿瘤微环境发生作用。因此，研究肿瘤侵袭转移必须从上述两方面入手。
传统的肿瘤病理形态由于存在个体差异，判断误差较大，因此迫切需要发展分子病理以及基于分子病理的定量、半定量分析技术来准确分析肿瘤细胞的侵袭转移能力。另外，间质微环境成分众多，功能各异，现有的技术难以实现肿瘤组织中原位多个关键成分的共成像，因此迫切需要发展原位多分子共成像技术。
量子点（Quantum dots, QDs）分子探针成像技术具有独特的表面和尺寸效应，激发光波谱宽、连续，紫外光可以激发任何大小的QDs；发射光波谱窄而对称；光稳定性好；荧光亮度强；荧光时间长，30-100ns；发射波长随QDs大小而改变等。近几年来，量子点探针技术与肿瘤分子标志物的有机结合，在分子成像、细胞成像和体内成像中取得了重大进步，作为一类新型纳米荧光探针，已经广泛用于肿瘤的分子靶向诊断研究。
本课题基于胃癌的临床实践难题，从癌细胞分子病理生物学行为和肿瘤微环境两方面开展相关研究，结合量子点标记分子探针技术在肿瘤分子靶向诊断方面的应用优势，阐释胃癌侵袭转移过程中的癌细胞与微环境协同进化理论，为揭示胃癌侵袭转移机理奠定基础。
第一部分：转化生长因子β1的表达与胃癌预后之间的关系
研究目的：作为一个多功能的细胞因子，转化生长因子β (Transformgrowth factors beta, TGFβ)可通过蛋白激酶受体和SMAD介体途径，在肿瘤生长、侵袭和转移过程中，起着重要的作用。在正常情况下，TGFβ作为一个肿瘤抑制器，抑制细胞增殖、诱导凋亡和调节自噬。随着肿瘤的发展，TGFβ将会促进上皮间质转化和肿瘤细胞的运动、侵袭和转移。
在世界范围内，胃癌是最常见的恶性肿瘤之一，其占癌症死亡的第三位。根据Lauren分级，胃癌可分为弥漫型和肠型两种。与肠型胃癌不同，弥漫型胃癌通常会伴随着间质纤维化，呈现弥漫性浸润生长，并且预后较差。TGFβ1是一个强有力的纤维化刺激因子，可为胃癌的播散创造一个适宜的环境。而且，在胃癌组织中，TGFβ1的高表达与胃癌的不良预后相关，特别是在弥漫型胃癌中。这些研究提示，TGFβ1或许在不同类型胃癌中具有不同作用。更重要的是，由于TGFβ1的分泌机制复杂，且具有双向功能，以前的研究或许不能准确地描述TGFβ1表达和患者预后之间的关系。因此，有必要探讨TGFβ1表达与不同临床病例亚组胃癌患者预后之间的关系。本文主要通过免疫组织化学染色方法，研究不同临床病理亚组胃癌的TGFβ1表达及其临床意义。
研究方法：1.收集胃癌患者病例及相关资料：收集2002年12月至2011年1月期间接受以手术为主的多学科治疗的184例患者。由上海生物芯片有限公司，将184例胃癌肿瘤组织和41例癌旁组织制作成3张组织芯片(450个点，2个点/例)。为了控制TMAs的质量，仅选择可揭示肿瘤典型生物学行为的肿瘤侵袭前缘组织。根据第七版AJCC TNM分期系统和Lauren分类系统进行TNM分期和病理分类。至2012年5月31日，胃癌患者的中位随访时间为57.3月(范围：16.6-100.1)，108例患者发生了胃癌相关性死亡。2. TGFβ1的免疫组织化学染色：使用SP染色方法对组织芯片进行染色。染色过程简述如下：60℃常规烤片2h；二甲苯脱蜡3次，3min/次；梯度酒精如水；0.01mmol/L的磷酸盐缓冲液微波抗原修复20min；冷却至室温后，使用0.03%H2O2孵育10min，去除内源性过氧化氢酶；加入2%的BSA进行封闭30min，降低背景强度；每张组织芯片上加入250μL兔抗人的TGFβ1单克隆抗体，4℃过夜孵育；加入250μL小鼠抗兔的二抗37℃孵育30min；0.2%DAB显色2min；苏木素复染2min；5%的酒精分化5s；梯度酒精脱水。3.结果评估：由两位有经验的病理学专家对免疫组织化学染色结果进行评估。根据以前的报道，评分系统结合细胞染色强度和细胞染色比例，主要评分标准为：细胞染色强度分为无染色（0分）、轻度染色（1分）、中度染色（2分）和强染色（3分）；细胞阳性染色比例<10%为1级，细胞阳性染色比例10%-25%为2级，细胞阳性染色比例25%-50%为3级，细胞阳性比例>50%为4级。最终评分为染色强度评分和细胞阳性率评分的和。和为0-3分定义为低表达，4-6分定义为高表达。4.统计学分析：使用SPSS17.0软件进行统计学分析。用Kaplan-Meier方法和Log-rank检验计算累计生存率，使用Pearson卡方检验进行相关性分析，使用Cox比例风险模型进行单因素和多因素分析。P <0.05被认为具有统计学意义。
研究结果：1.胃癌中TGFβ1的表达：TGFβ1在166(90.2%)例胃癌组织和33(80.5%)例癌旁组织中呈棕褐色表达。82(44.6%)例胃癌组织和28(68.3%)例癌旁组织中TGFβ1呈低表达，102(55.4%)例胃癌组织和13(31.7%)例癌旁组织中呈高表达。胃癌组织中TGFβ1表达要显著高于癌旁组织(χ2=7.554,P=0.006)。在某些患者中，可观察到TGFβ1在肿瘤间质和癌细胞中同时表达的现象。2. TGFβ1表达与临床病理参数之间的关系：老年患者中TGFβ1表达高于年轻患者(P=0.017)，肠型胃癌中TGFβ1表达高于弥漫型胃癌(P=0.015)。在其它临床病理参数之间，TGFβ1的表达差异无统计学意义，包括性别、肿瘤位置、浆膜侵犯、淋巴结转移情况、远处转移情况、复发情况及预后。3.临床病例参数与不同TGFβ1表达水平胃癌患者之间的关系：TGFβ1高表达与胃癌患者的不良预后相关(P=0.040)。Log-rank检验显示，弥漫型胃癌患者预后比肠型胃癌患者的预后差(P=0.018)。为了进一步确定TGFβ1和胃癌总生存之间的关系，我们将临床病例参数分为两个不同的亚组。在年轻患者、女性、弥漫型、低分化和淋巴结转移的胃癌中，TGFβ1高表达与不良预后相关。单因素分析显示，年龄、Lauren分类、病理分级、浆膜侵犯、淋巴结转移和TGFβ1表达水平与胃癌预后相关。多因素分析显示，年龄、病理分级、浆膜侵犯和TGFβ1表达水平是胃癌生存的独立预后影响因子。
研究结论：本文探索了TGFβ1表达和胃癌预后之间的关系。结果表明，TGFβ1主要在胃癌的细胞膜和细胞质中表达，在胃癌组织中其表达水平显著高于癌旁组织。这一结果与以前的报道相似[18,22,26,30]。值得注意的是，在某些患者中，TGFβ1在胃癌细胞核和间质细胞中表达，从肿瘤微环境的角度来考虑，这或许具有重要意义。
尽管研究已经证实TGFβ1高表达与不良预后相关（包括本文），但是以前的研究没有明确TGFβ1表达是否与其它临床病理参数相关。本研究显示，在我们研究的临床病理参数中，TGFβ1表达水平与患者年龄、胃癌类型相关。有趣的是，TGFβ1与上皮间质转化相关，纤维化是弥漫型胃癌的典型特征，但是本研究显示，肠型胃癌中TGFβ1表达高于弥漫型胃癌。因此，需要更多的研究来进一步确认胃癌中TGFβ1的双向功能和预后价值。而且，Cox多因素风险比例模型显示，TGFβ1表达、年龄、病理分级和浆膜侵犯是独立的预后因子。
总之，本研究证实TGFβ1高表达与胃癌患者的不良预后相关，其高表达与年轻患者、女性患者、远端胃癌、低分化胃癌、弥漫型胃癌和淋巴结转移患者的不良预后相关。本文或许对TGFβ1治疗有一些启示，即在上述胃癌亚组中，抗TGFβ1的治疗或许更有效。
第二部分：量子点标记分子探针技术研究胃癌侵袭微环境的多因素分析
研究目的：胃癌是世界上第四大恶性肿瘤，居中国癌症死亡第三位。尽管这些年来，胃癌早期诊断及以手术为主的综合治疗方式有所改进，但胃癌患者的临床结局还是不乐观，其主要的问题在于肿瘤的侵袭转移。为了解决该难题，肿瘤学界在过去几十年里针对癌细胞开展了很多工作。最近肿瘤学界已经达成共识，肿瘤是一种免疫失衡的全身性疾病，不仅仅是单一肿瘤细胞的问题，而是全身细胞共同导致的问题。该理论的根基指引我们研究肿瘤不仅需要关注肿瘤细胞，还需要关注肿瘤微环境。因为，肿瘤微环境在肿瘤细胞与其微环境协同进化过程起了至关重要的作用。因此，我们迫切需要探讨肿瘤细胞及相关微环境的相互作用，以更好地揭示肿瘤侵袭转移的机制。
在胃癌中，肿瘤微环境是一个复杂且动态变化的集体，在肿瘤侵袭过程中不断发生改变，包括肿瘤细胞从肿瘤原位逃逸，渗入血管或淋巴管，在血管中定居，或粘附至血管内皮细胞，渗出血管或淋巴管进入远处器官定居并增殖，伴随肿瘤新生血管生成，形成转移灶。在这个过程中，肿瘤微环境中的众多成分相互作用，共同促进肿瘤侵袭转移。主要包括炎性细胞，如巨噬细胞；免疫细胞，如T和B淋巴细胞；间质细胞如成纤维细胞和不同成熟度的肿瘤新生血管。这些成分在解剖及空间分布上需要满足一定的特征，才能共同促进肿瘤侵袭转移。最近研究表明，肿瘤细胞、巨噬细胞和肿瘤新生血管在空间分布上相互靠近，形成一个特定结构，称为肿瘤转移的微环境。因此，为了同时展现这些关键成分，迫切需要发展原位同时共成像技术，以更好阐释肿瘤侵袭机制。
目前，能同时显示肿瘤组织切片上多种成分的技术很少。量子点，因其独特的尺寸和表面效应，在生物医学领域应用中展现出巨大的潜力。本研究利用量子点标记分子探针多色成像的技术，研究肿瘤细胞与巨噬细胞和肿瘤新生血管的相互作用关系，发展一种基于计算机辅助计算肿瘤侵袭单元的技术。
研究方法：1.收集胃癌患者病例资料及病理标本：通过建立和济医院2008-2012年的胃癌标本数据库，从中选择30例胃癌患者标本作为试验数据集，另外选择一组独立的60例胃癌患者标本作为验证数据集，完善相关的临床病理信息，及病理标本收集。2.基于量子点的肿瘤组织双色成像：上述收集的标本病理切片后，采用量子点标记分子探针技术原位双分子染色，对胃癌组织中的巨噬细胞及肿瘤新生血管进行原位共成像。3.图像采集及肿瘤侵袭单元计算：量子点双分子成像后，在荧光显微镜及多光谱分析软件下采集荧光图像，利用多光谱分析软件对巨噬细胞以及肿瘤新生血管的成像结果进行信号分离，在计算机辅助的图像处理技术辅助下，计算肿瘤侵袭转移单元数目。4.统计分析：所有的统计分析均在SPSS21.0软件中完成。
研究结果：1.肿瘤进展过程中肿瘤侵袭单元呈现两种不同的表现形式：在第一种类型中，肿瘤新生血管为纵向分布。多角形肿瘤新生血管在各个渗出的角中都伴随着巨噬细胞的浸润。另外，还能看到巨噬细胞浸润肿瘤新生血管，一半在血管外，一半在血管内。另一种类型的肿瘤侵袭单元中血管成横向分布，巨噬细胞可以分布在血管周围，靠近血管；也可以穿透血管，还可以在血管内定居。上述两种不同形态的肿瘤侵袭单元，以及肿瘤新生血管与巨噬细胞的动态变化，共同促进肿瘤侵袭转移。2.肿瘤侵袭单元与患者病理特征的关系：不同分化程度的胃癌患者的肿瘤侵袭单元数量不同，并且高分化管状腺癌、低分化管状腺癌及印戒细胞癌三组间的肿瘤侵袭单元具有显著性差异。首先，低分化组的巨噬细胞密度（1544）显著高于高分化组（317）和印戒细胞癌组（1011）。肿瘤新生血管的统计结果与肿瘤巨噬细胞的统计结果相似。低分化组的肿瘤新生血管密度最高（789），显著高于高分化组（474）和印戒细胞癌组（774）。并且，低分化组的肿瘤侵袭单元（373）显著高于低分化组（82）和印戒细胞癌组（177）。因此，这组试验数据集的结果提示肿瘤侵袭单元在三组之间有显著差异。3.肿瘤侵袭单元与临床预后的关系：验证数据集中60例患者的平均年龄为60.7岁，女性患者为41例，男性患者为19例，高分化管状腺癌为20例，低分化管状腺癌为23例，印戒细胞癌患者为17例，肿瘤未浸润至浆膜的为14例，浸润至浆膜及以外的为46例，无淋巴结转移的为18例，有淋巴结转移的为42例，无远处转移的为53例，有远处转移的为7例，肿瘤分期为早期的是21例，晚期的为39例，50例患者行根治性胃大部切除手术，10例患者为根治性胃全切手术，20例患者进行了术后化疗，另外40例未进行术后化疗，60例患者的总体生存期为17.8月。生存分析数据显示：肿瘤巨噬细胞密度和肿瘤侵袭单元是胃癌患者预后的独立预后因子，肿瘤新生血管不是独立的预后因子。肿瘤巨噬细胞数量越多，肿瘤侵袭单元越多，胃癌患者的预后越差。ROC工作曲线表明肿瘤侵袭单元的曲线下面积最大（63.2%），提示相较于巨噬细胞而言，肿瘤侵袭单元的独立预后预测效能更高。4.肿瘤侵袭单元对患者预后影响：为了更进一步证实，我们选择6个案例来做进一步阐述，3例是有相同临床TNM分期和临床治疗方式的低分化管状腺癌，3例是有相同临床TNM分期和临床治疗方式的高分化管状腺癌。深入分析上述几个案例，可以发现具有相同临床TNM分期及临床治疗策略的肿瘤患者，肿瘤侵袭单元数量越多，患者的总体生存期越短。3例低分化管状腺癌中，每200个高倍视野的肿瘤侵袭单元数量分别为7个，20个和58个，其对应的总体生存期分别为20.6月，11.1月和3.3月。另外的3例高分化管状腺癌中，每200个高倍视野的肿瘤侵袭单元数量分别为3个，11个和24个，其对应的总体生存期分别是42.6月，27.6月和14.3月。
研究结论：上述研究结果表明肿瘤细胞和肿瘤微环境的相互作用对胃癌患者的临床结局有显著影响。具体而言，肿瘤细胞和其周围微环境中的新生血管和浸润性巨噬细胞形成在空间分布上的结构，即肿瘤侵袭单元，促进肿瘤侵袭转移。肿瘤组织中肿瘤侵袭单元的数量与患者的预后及肿瘤分化程度呈负相关。另外，肿瘤侵袭单元的预后预测效能比单一的肿瘤微环境成分高。更有趣地是，我们发现肿瘤新生血管密度并不是胃癌患者预后的独立生存因子。这提示我们肿瘤新生血管对肿瘤进展的影响可能是受巨噬细胞调控的，这也同样证实了肿瘤侵袭单元的有效性。
量子点标记分子探针技术及多光谱分析技术对这项研究有突出贡献。量子点是一类具有独特的光学特性的纳米分子，在生物医学领域有广泛的应用前景。相较于传统的荧光染料和蛋白而言，量子点有独特的尺寸和表面效应。量子点具有显著的成像优势，包括组分可调性成像，荧光强度更高，抗漂白能力更强。另外，不同颜色的量子点能在相同波长的紫外光下激发，且光谱重叠窄，这使得其在多分子靶向原位成像技术中展现出突出的优势。正是这一特性，使得量子点很适合用于肿瘤侵袭单元的研究。在本研究中，巨噬细胞和肿瘤新生血管同时标记为绿色和红色。因此，相较于传统的人工合成不同激发光下的成像图片，这种基于量子点标记分子探针技术的成像技术真正实现了原位多分子共成像，可用于更好地研究肿瘤组织多种成分的相互作用。
肿瘤转移和复发是胃癌治疗失败的根本原因，肿瘤细胞侵袭是肿瘤发生远处转移的第一步，也是最重要的一步。目前，还没有合适的技术能预测肿瘤侵袭转移的风险。本研究结合量子点标记分子探针多色成像技术的优势，对肿瘤侵袭单元的成分包括肿瘤细胞、巨噬细胞和肿瘤新生血管进行成像，分析其空间分布关系，发展了计算机辅助的图像处理技术和算法。结果显示肿瘤侵袭单元具有较高的预测效能，再次证实了量子点标记分子探针原位多分子共成像技术在肿瘤多参数整合研究中的优势。
Based on the GLOBOCAN2008, about12.7million cancer cases and7.6million cancer deaths are estimated to have occurred in2008. In China, the gastriccancer (GC) incidence ranked the second in cancer, and third of the cancer mortality.Although, the early cancer screening techniques and the surgery guidedcomprehensive treatment strategies of GC developed rapidly recently, thereoccurrence and invasion after curative resection of GC still high. The root causesare cancer invasion and metastasis. Thus, the future research will focus on cancerinvasion and metastasis and comprehensive treatment strategy. Recently, more andmore researches demonstrated that the cancer invasion and metastasis is amulti-stage and multi-factor involved dynamic process. It not only includes thecancer cells themselves, but also the tumor microenvironment.
Due to the personal judgment errors of traditional pathological morphology, it isurgent to develop the quantitative analysis technique based on the molecularpathology. In addition, tumor microenvironment is a complex community, involvingmany components with different functions. The existed techniques are farsatisfactory to realize the in situ multiplexed imaging of tumor microenvironment.Thus, the in situ multiplexed imaging technique is urgently needed.
Quantum dots (QDs) are engineered nanoparticles with unique surface and sizeeffect so that it may have many advantages over fluorescent dyes, includingcontinuous emission spectrum, size-tunable, enhanced signal brightness andresistance to photobleaching. Recently, combined with the QDs based imagingtechnique, the molecular imaging, cellular imaging and in vivo imaging has progressed quickly. Due to the advantages of QDs in molecular imaging, the novelfluorescent nanoparticles have been widely used in the molecular target imaging.
This study focused on the clinical dilemma of GC, and explored the tumorbiological behavior from the molecular pathology of cancer cells and tumormicroenvironment. It aimed to reveal the co-evolution theory of cancer cells andtumor microenvironment combined with the application advantages of QDs basedmolecular probe, so as to in depth understand the mechanisms of GC invasion andmetastasis.
Two parts are included in this study:Part I: High Expression of Transform Growth Factor Beta1inGastric Cancer Confers Worse Outcome: Results of a Cohort Studyon184Patients
Objective: As a multifunctional cytokine, transform growth factors beta (TGFβ)regulates many biological processes by protein kinase receptors and drosophilamothers against decapentaplegic protein (SMAD) mediators, and plays importantroles in tumor growth, invasion and metastasis. Briefly, in healthy system, TGFβ actsas a tumor suppressor to inhibit cell proliferation, induce apoptosis and regulateautophagy. With tumor development, TGFβ will promote epithelial-mesenchymaltransition (EMT), tumor cell motility, invasion and metastasis.
GC is one of the most common carcinomas and the third cause of cancer deathin the world. GC is divided into two types based on Lauren classification: diffusetype and intestinal type. Different with intestinal type GC, diffuse type GC ischaracterized by a diffusely infiltrating growth accompanied by stromal fibrosis anda poor prognosis. TGFβ1just is a powerful fibrotic stimulating factor, and contribute to create a favorable environment for the dissemination of GC. Compared withnormal gastric tissues, TGFβ1is significantly up-regulated in GC. Furthermore,TGFβ1over-expression of in GC tissues is correlated with GC prognosis, especiallyin diffused type GC. These studies may imply TGFβ1has different roles in differenttype GC. What is more, because of the complex secretion mechanism andbidirectional function of TGFβ1, previous studies may not accurately describe therelationship between TGFβ1expression and patients prognosis. Therefore, it isdesirable to explore the relationship between TGFβ1expression and prognosis ofpatients with GC in different clinicopathological subgroups. This study was mainlyto investigate TGFβ1expression and its clinical significance in differentclinicopathological subgroups of GC patients by immunohistochemistry method.
Methods:1. Patients: A total of184GC patients receiving surgery-basedmulti-disciplinary treatment from December2002to February2011were collected.The patients’ paraffin specimens were constructed into three tissues microarrays(TMAs) which contained184tumor tissues and41peritumoral tissues (450cores,two cores for each tissues), developed by Shanghai Biochip Company Ltd.(Shanghai, China). In order to control the quality of TMAs, only tumor tissues at thecancer invasion front were selected, so that the typical biological behaviors of cancercould be revealed. TNM stage and pathologic classification were determinedaccording to the7th edition AJCC TNM system and Lauren classification,respectively. Up to May31,2012, the median follow-up of those GC patients was57.3(range:16.6-100.1) mo, with108(58.7%) GC-specific deaths occurred.2.Immunohistochemistry of TGFβ1: SP immunohistochemistry method was used tostain the TMAs. Briefly, after de-waxing, the TMAs were performed microwaveantigen retrieval for20min at moderate baking temperature in0.01mmol/L (pH=6.0)citrate buffer solution. After cooling at room temperature, TMAs were treated with 0.03%hydrogen peroxide methanol for10min to inactivate endogenous peroxidase.Then2%BSA was used to block TMAs to decrease background intensity. Everychip treated overnight at4℃with250μl rabbit anti-human TGFβ1monoclonalantibodyand then incubated with the corresponding secondary antibody (1:250dilution) for30min at37℃. Then treated with0.2%diaminobenzidine solution for2min to coloration. The TMAs were counterstained with hematoxylin anddifferentiated by hydrochloric acid alcohol.3. Result evaluation: The results ofimmunohistochemistry were analyzed by two experienced pathologists with over10years of experience in clinical tumor pathology. The scoring system combined cellsstaining intensity with cells positive rate as reported previously, and briefed in thefollowing: Cells staining intensity was defined as no stain, slight stain, medium stainand strong stain, and correspondingly scored as0,1,2and3, respectively. Cellspositive rate was defined as grade1with positive TGFβ1immunostaining in <10%of tumor cells stained positive, grade2with10%to25%of tumor cells stainedpositive, grade3with25%to50%of tumor cells stained positive, and grade4with>50%of tumor cells stained positive, and correspondingly scored as0,1,2and3,respectively. The final score was the sum of cells staining intensity score and cellspositive rate score. The sum score0-3was defined as low expression and4-6as highexpression.4. Statistical analysis: Statistical analysis was performed with SPSS17.0software. Cumulative survival was calculated by the Kaplan-Meier method andanalyzed by the Log-rank test. Correlation test was calculated by Pearson chi-square.Univariate and multivariate survival analysis were performed with the Coxproportional hazards method. P＜0.05was judged to be significant.
Results:1. TGFβ1expression in GC: TGFβ1expressed in166(90.2%) GCtumors and33(80.5%) peritumoral tissues, as brownish fine granules in thecytoplasm and membrane of GC cells. Based on the above evaluation criteria,82 (44.6%) GC tissues and28(68.3%) peritumoral tissues had low expression, and102(55.4%) GC tissues and13(31.7%) peritumoral tissues had high expression. Theexpression of TGFβ1in GC tissues were significantly higher than in peritumoraltissues (χ2=7.554, P=0.006). In some patients, expression of TGFβ1in tumor stromaand cancer cell was also observed.2. The relationship between TGFβ1expressionand clinicopathological parameters: TGFβ1expression was higher in the old thanin the young (P=0.017) and higher in intestinal type GC than in diffuse type GC(P=0.015). There were no statistically significant differences in TGFβ1among otherclinicopathological parameters.3. The relationship between clinicopathologicalparameters and OS of GC patients stratified by TGFβ1expression: Highexpression of TGFβ1was related to worse OS of GC patients. Log-rank test showedthat diffuse type GC patients had a poor survival than intestinal type GC patients(P=0.018). To further evaluate the relationship between TGFβ1expression and OS,the clinicopathological parameters were divided into different subgroups. HighTGFβ1expression had a worse survival in young people, female, diffuse type GC,poor differentiation, and lymph nodes metastasis. Univariate analysis showed thatage, Lauren classification, pathological grading, serosal invasion, lymph nodesmetastasis and TGFβ1expression were risk factors. Multivariate Cox proportionalhazards analysis showed that age, pathological grading, serosal invasion and TGFβ1expression were independent risk factors.
Conclusion: This study explored the relationship between TGFβ1expressionand GC prognosis. The results showed TGFβ1mainly expressed in the GCcytoplasm and cytomembrane, with significantly higher expression in GC tissuesthan peritumoral tissues. This finding was similar to previous reports [18,22,26,30].What deserves more attention was the finding that some positive staining was alsofound in GC nucleus and stromal cells in some patient. This may had special significance from the perspectives of tumor microenvironment.
Although it has been demonstrated that TGFβ1high expression was related topoor prognosis (including our study), previous studies have not defined whetherTGFβ1expression was related to other clinicopathological parameters. Our studyonly showed TGFβ1expression was related to patients’ age and GC type among theclinicopathological parameters investigated. Interestingly, TGFβ1is involved withEMT, and fibrosis is the typical feature of diffuse type GC, but this study showedTGFβ1expression was higher in intestinal type GC than in diffuse type GC.Therefore, more studies are needed to further elucidate the bidirectional functionsand prognosis value of TGFβ1in GC. Furthermore, Cox’s proportional hazard modelshows that TGFβ1expression, age, clinicopathologicalical grading and serosalinvasion were independent risk factors.
In conclusion, this study has demonstrated that TGFβ1high expression wasrelated to poor prognosis of GC patients, who tend to be young, female, distantgastric tumor with poor differentiation, diffuse type, and lymph nodes metastasis.The present study perhaps has some enlightenment for TGFβ1treatment, namely inthe above subgroups GC patients, the treatment aim at TGFβ1would be moreeffective.
Part II: Tumor invasion unit in gastric cancer revealed byQDs-based in situ molecular imaging and multispectral analysis
Objective: Gastric cancer (GC) is the fourth most common cancer worldwide,and the third leading cancer cause in China. Despite recent progresses in the earlydiagnosis and the surgery-centered multidisciplinary treatments for GC, the overallclinical outcome of such patients is still far from satisfactory, mainly due to the post-treatment occurrence and metastasis, via blood circulation, lymphatic channelsor direct cancer cells invasion and seeding. To tackle this problem, many effortsfocusing on cancer cells have been made. And eventually, the oncology communityhas come to the understanding that cancer is a disease of imbalance, i.e., not merelya disease of rogue cells but the body’s mismanagement of those cells, thefundamental importance of such theoretical changes is that we have to pay particularattention to tumor microenvironment, in addition to cancer cells, because tumormicroenvironment plays an important role via the co-evolution of tumor cells andstroma. Thus, it is urgent ro explore the co-evolution of tumor cells and stroma.
In GC, tumor microenvironment is a complex and dynamic community, whichis undergoing constant evolutions during cancer invasion, involving tumor cellsescape from primary sites into vasculature (blood circulation and lymphatic channel),reside and adhere to endothelial cells, penetrate from vasculature into other organsand reside in them, accompanied with tumor neo-vessels growth. Many importantcomponents in tumor microenvironment work together to contribute to cancerinvasion. Major components in tumor microenvironment are inflammatory cells suchas macrophages, immune cells such as T and B lymphocytes, stromal cells such asfibroblasts, and neo-blood vessels of various stages of maturity. These players mustbe in an appropriate anatomic proximity and spatial vicinity with the tumor cells inorder to facilitate cancer invasion. And indeed, recent studies have shown that tumorcells, macrophages and tumor neo-vessels in close vicinity with one another form aunique structure called tumor microenvironment of metastasis (TMEM), or in moreeasily understandable terms, called ‘tumor invasion unit’. Therefore, thesimultaneous recognition and analysis of all the components in the tumor invasionunit is very important to understanding the new perspective of cancer invasion.
There are few techniques that can simultaneously image multiple components in complex tumor microenvironment of the same tissue section. Thus, it is urgent todevelop a more holistic method to image the complex interactions of stromalcomponents in situ. Quantum dots, with its unique size and surface effects, haveshown great potential in biomedical application, especially in multiplexed imagingin situ. In this study, taking the advantages of established QDs-based multiplexedimaging in situ, we analyzed on the interactions between macrophages, tumorneo-vessels and cancer cells, and developed a computer-based algorithm of tumorinvasion units.
Methods:1. Patients and specimens: Tissue sections (4m thickness) of90human GC cases were selected from the central database on GC established at ourcancer center, including30with detailed pathological information, and60withcomplete clinic-pathological and survival information available on the patients.Tumor tissues from the first set of30patients were used for a trial study, to explorethe correlation of tumor invasion unit with classical pathological features, in order totest if there was any relationship between tumor invasion unit unfavorablepathological features. Tumor tissues from the second set of60patients with detailedsurvival information were used for validation study, in order to further verify iftumor invasion unit could predict the overall survival (OS).2. QDs-based doublemolecular imaging: The QDs-based in situ molecular imaging procedures wereperformed with the following major steps: tissue slides de-paraffinizing→antigenretrieval→blocking→primary antibody for macrophages and CD105→washingand blocking→staining with QDs-525, QDs-585or QDs-655→washing→detectionand acquisition.3. Image capture and analysis: The QDs stained images werecaptured by Olympus DP72cameraunder CRi Nuance multispectral imaging system.The QDs-525, QDs-585, QDs-655were excited by UV light (330-385nm). Aspectral cube for each image, which contains the complete spectral information at10 nm wavelength intervals from420-720nm, was collected by the CRi Nuancemultispectral imaging system. And all the cubes were captured under the samecondition at×200magnification with the same settings for each image, so as toavoid the selection bias. The QDs fluorescence signal unmixing was processed bythe software package within the Nuance system. Then, after obtaining the images ofsignal unmixing, the macrophages were counted on each image and the totalmacrophages was documented as the counts for the patient for further analysis. Thesame calculation method was used for tumor neo-vessels counting. After processingQDs images, the images with double signals of both macrophages and tumorneo-vessels at the tumor nest area were acquired. As the tumor invasion unitconsisted of cancer cells, macrophages and tumor neo-vessels, a circle with thediameter of60m centered on macrophages was chosen, approximately three celldiameters across. With the computer-based algorithm, if there was any red signal inthis circle, it was counted as1, otherwise; it was counted as0. Then the total countsof five images for each patient was output as the result of tumor invasion units.4.Statistical analysis: Statistical analyses were performed with SPSS software version21.0. For the comparison of individual variables, Fisher’s exact test, t test andMann-Whitney Test were conducted as appropriate. The Kaplan-Meier survivalcurves were plotted to analyze the OS by different study parameters, with log ranktest to define the statistical differences between the subgroups. Two sided P <0.05was judged as statistically significant.
Results:1. Two types of tumor invasion unit with dynamic changes: Basedon the spreading and layout patterns of neo-vessels and the macrophages infiltrationsteps, two forms of tumor invasion units in constant dynamic changes could berecognized. In form one, the tumor neo-vessel was seen in longitudinal section. Withthe special longitudinal spreading and irregular morphology, the tumor neo-vessels presented to be multi-angled accompanied with macrophages infiltration at eachangle. Macrophages undergoing intravasation could also be observed, half in andhalf out of the blood vessel. In form two the tumor neo-vessel is seen in cross section,with macrophages lodging the vessel with membrane processes, crossing the vesselwall, and in close vicinity to the vessel, thus revealing the dynamic interactionsbetween macrophages and neo-vessels to facilitate tumor invasion.2. Tumorinvasion unit was correlated with worse clinico-pathological features in30GCcases in the trial study: First, we selected a trial set of30GC patients with the threemost common pathological types, including well differentiated, poorly differentiatedand signet-ring cell carcinoma. These patients were classified into3groups bydifferent pathological types,10cases in each group. According to the quantitativeanalysis, the mean macrophages density was higher in the poorly differentiatedgroup (1544) than the well differentiated group (317) and the signet-ring cell group(1011), with statistical significance in three groups. The results of tumor neo-vesselsanalyses were the same as the macrophages. The poorly differentiated group had thehighest count but the well differentiated group had the lowest counts, with statisticalsignificances among three groups (P <0.001, for between-group comparisons). Andalso, the number of tumor invasion units was much higher in the poorlydifferentiated group (373) than the well differentiated group (82) or the signet-ringcell group (177), with statistical significance in three groups (P=0.000, for allbetween-group comparisons). Thus, in this trial set, compared with the analysisresults of macrophages and tumor neo-vessels, tumor invasion units had the similareffects. However, the P value of tumor invasion unit was the smallest, suggesting itsstronger correlation with worst histological types.3. The impact of tumor invasionunit on clinical outcome: Taking the median value of macrophages, neo-vessels andtumor invasion units as the cut-off value, these parameters were divided into high density groups if the individual value was above the cut-off value, and low densitygroups if the individual value was below the cut-off value. Their OS curves bymacrophages density, neo-vessels density and tumor invasion units showed that bothmacrophages and tumor invasion units were independent factors to impact onsurvival, while tumor neo-vessel density itself was not an independent factor forsurvival. ROC analysis also demonstrated that tumor invasion unit had the largestarea under the curve (63.2%), suggesting that tumor invasion unit had biggerpredicting power than macrophages for OS prediction.4. Typical examples ofimpacts of tumor invasion unit on clinical prognosis: To further validate thisobservation, we selected6cases for detailed analysis,3cases of poorlydifferentiated adenocarcinoma with identical TNM stages and clinical treatments,and another3cases of highly differentiated adenocarcinoma with identical TNMstages and clinical treatments. From these cases, it could be observed more clearlythat the higher the number of tumor invasion units, the shorter the OS, regardless ofthe tumor differentiation and histological types. In the3cases of poorlydifferentiated adenocarcinoma, the tumor invasion units were7,20and58per×200magnification field, respectively; and the corresponding OS were20.6,11.1and3.3months, respectively. In another3cases of well differentiated adenocarcinoma, thetumor invasion units were3,11, and24per×200magnification field, respectively;and the corresponding OS were42.6,27.6, and14.3months, respectively.
Conclusion: This study demonstrated that the complex and constantinteractions between tumor cells and their microenvironment could have asignificant impact on the clinical outcomes of GC patients. Specifically, tumor cells,new blood vessels due to tumor angiogenesis and infiltrating macrophages in thetumor microenvironment could form a spatially very close entity called tumorinvasion unit that facilitates tumor invasion. The number of tumor invasion units in the tumor tissue is negatively correlated with poor tumor differentiation and worsesurvival. In addition, the tumor invasion unit has bigger predictive power of OS thansingle component counts of macrophages and tumor neo-vessels, respectively.Interestingly, we have found that the tumor neo-vessels density was not anindependent prognostic factor for OS. This may suggest that the effects of tumorneo-vessels on cancer invasion can be regulated by tumor macrophages infiltrationwith synergistic effect, even at early stages of cancer development. This could atleast account for the emerging concept of the tumor invasion unit. QDs-basedmolecular imaging and multispectral analysis could make a unique contribution inthis regard, as the current study demonstrated. QDs are engineered nanoparticleswith unique optical properties suitable for biomedical application. Compared withorganic dyes and fluorescent proteins, due to its unique size and surface feature, QDshave many advantages such as composition-tunable light emission, enhancedfluorescence brightness, strong resistance to photobleaching and chemicaldegradation. In addition, different colors of QDs can be simultaneously excited by asingle light source, with minimal spectral overlapping, which provides significantadvantages for multiplexed detection of target. This property is very suitable forinvestigating the complex interactions between tumor cells and their surroundingmicroenvironment at the architectural level. In this study, the infiltratingmacrophages and tumor angiogenesis were simultaneously labeled green and red,respectively. The blue auto-fluorescence also showed clear tumor backgroundstructure. Therefore, instead of producing artificial overlay images by conventionalimaging techniques, this QDs based molecular imaging technique could provideauthentic multicolor images to better reveal the complex interactions of differentcomponents in the tumor tissue.
Cancer recurrence and metastasis is the root cause of treatment failure in GC, and tumor cells escape is the first and most important step towards distant metastasis.There are currently no reliable methodologies to predict the risk for metastaticdisease. Taking the advantages of QDs-based multiplexed molecular imagingtechnology, this study has simultaneously revealed the spatial distribution of tumorinvasion unit, consisting of tumor cells, macrophages and tumor neo-vessels anddeveloped a computer-based algorithm for tumor invasion unit analysis at molecularlevel. Both the novel approach and the results in this study suggested the greatpredictive power of tumor invasion unit for OS. A next step will be to validate thesefindings in a larger, independent and standardized GC database with known clinicaloutcome. If the results can be substantiated, the tumor invasion unit could be apowerful tool addition to the current approach for guiding personalized therapy andassessing prognosis, so as to better prevent over-treatment and under-treatment ofcancer patient.
传统的肿瘤病理形态由于存在个体差异，判断误差较大，因此迫切需要发展分子病理以及基于分子病理的定量、半定量分析技术来准确分析肿瘤细胞的侵袭转移能力。另外，间质微环境成分众多，功能各异，现有的技术难以实现肿瘤组织中原位多个关键成分的共成像，因此迫切需要发展原位多分子共成像技术。
量子点（Quantum dots, QDs）分子探针成像技术具有独特的表面和尺寸效应，激发光波谱宽、连续，紫外光可以激发任何大小的QDs；发射光波谱窄而对称；光稳定性好；荧光亮度强；荧光时间长，30-100ns；发射波长随QDs大小而改变等。近几年来，量子点探针技术与肿瘤分子标志物的有机结合，在分子成像、细胞成像和体内成像中取得了重大进步，作为一类新型纳米荧光探针，已经广泛用于肿瘤的分子靶向诊断研究。
本课题基于胃癌的临床实践难题，从癌细胞分子病理生物学行为和肿瘤微环境两方面开展相关研究，结合量子点标记分子探针技术在肿瘤分子靶向诊断方面的应用优势，阐释胃癌侵袭转移过程中的癌细胞与微环境协同进化理论，为揭示胃癌侵袭转移机理奠定基础。
第一部分：转化生长因子β1的表达与胃癌预后之间的关系
研究目的：作为一个多功能的细胞因子，转化生长因子β (Transformgrowth factors beta, TGFβ)可通过蛋白激酶受体和SMAD介体途径，在肿瘤生长、侵袭和转移过程中，起着重要的作用。在正常情况下，TGFβ作为一个肿瘤抑制器，抑制细胞增殖、诱导凋亡和调节自噬。随着肿瘤的发展，TGFβ将会促进上皮间质转化和肿瘤细胞的运动、侵袭和转移。
在世界范围内，胃癌是最常见的恶性肿瘤之一，其占癌症死亡的第三位。根据Lauren分级，胃癌可分为弥漫型和肠型两种。与肠型胃癌不同，弥漫型胃癌通常会伴随着间质纤维化，呈现弥漫性浸润生长，并且预后较差。TGFβ1是一个强有力的纤维化刺激因子，可为胃癌的播散创造一个适宜的环境。而且，在胃癌组织中，TGFβ1的高表达与胃癌的不良预后相关，特别是在弥漫型胃癌中。这些研究提示，TGFβ1或许在不同类型胃癌中具有不同作用。更重要的是，由于TGFβ1的分泌机制复杂，且具有双向功能，以前的研究或许不能准确地描述TGFβ1表达和患者预后之间的关系。因此，有必要探讨TGFβ1表达与不同临床病例亚组胃癌患者预后之间的关系。本文主要通过免疫组织化学染色方法，研究不同临床病理亚组胃癌的TGFβ1表达及其临床意义。
研究方法：1.收集胃癌患者病例及相关资料：收集2002年12月至2011年1月期间接受以手术为主的多学科治疗的184例患者。由上海生物芯片有限公司，将184例胃癌肿瘤组织和41例癌旁组织制作成3张组织芯片(450个点，2个点/例)。为了控制TMAs的质量，仅选择可揭示肿瘤典型生物学行为的肿瘤侵袭前缘组织。根据第七版AJCC TNM分期系统和Lauren分类系统进行TNM分期和病理分类。至2012年5月31日，胃癌患者的中位随访时间为57.3月(范围：16.6-100.1)，108例患者发生了胃癌相关性死亡。2. TGFβ1的免疫组织化学染色：使用SP染色方法对组织芯片进行染色。染色过程简述如下：60℃常规烤片2h；二甲苯脱蜡3次，3min/次；梯度酒精如水；0.01mmol/L的磷酸盐缓冲液微波抗原修复20min；冷却至室温后，使用0.03%H2O2孵育10min，去除内源性过氧化氢酶；加入2%的BSA进行封闭30min，降低背景强度；每张组织芯片上加入250μL兔抗人的TGFβ1单克隆抗体，4℃过夜孵育；加入250μL小鼠抗兔的二抗37℃孵育30min；0.2%DAB显色2min；苏木素复染2min；5%的酒精分化5s；梯度酒精脱水。3.结果评估：由两位有经验的病理学专家对免疫组织化学染色结果进行评估。根据以前的报道，评分系统结合细胞染色强度和细胞染色比例，主要评分标准为：细胞染色强度分为无染色（0分）、轻度染色（1分）、中度染色（2分）和强染色（3分）；细胞阳性染色比例<10%为1级，细胞阳性染色比例10%-25%为2级，细胞阳性染色比例25%-50%为3级，细胞阳性比例>50%为4级。最终评分为染色强度评分和细胞阳性率评分的和。和为0-3分定义为低表达，4-6分定义为高表达。4.统计学分析：使用SPSS17.0软件进行统计学分析。用Kaplan-Meier方法和Log-rank检验计算累计生存率，使用Pearson卡方检验进行相关性分析，使用Cox比例风险模型进行单因素和多因素分析。P <0.05被认为具有统计学意义。
研究结果：1.胃癌中TGFβ1的表达：TGFβ1在166(90.2%)例胃癌组织和33(80.5%)例癌旁组织中呈棕褐色表达。82(44.6%)例胃癌组织和28(68.3%)例癌旁组织中TGFβ1呈低表达，102(55.4%)例胃癌组织和13(31.7%)例癌旁组织中呈高表达。胃癌组织中TGFβ1表达要显著高于癌旁组织(χ2=7.554,P=0.006)。在某些患者中，可观察到TGFβ1在肿瘤间质和癌细胞中同时表达的现象。2. TGFβ1表达与临床病理参数之间的关系：老年患者中TGFβ1表达高于年轻患者(P=0.017)，肠型胃癌中TGFβ1表达高于弥漫型胃癌(P=0.015)。在其它临床病理参数之间，TGFβ1的表达差异无统计学意义，包括性别、肿瘤位置、浆膜侵犯、淋巴结转移情况、远处转移情况、复发情况及预后。3.临床病例参数与不同TGFβ1表达水平胃癌患者之间的关系：TGFβ1高表达与胃癌患者的不良预后相关(P=0.040)。Log-rank检验显示，弥漫型胃癌患者预后比肠型胃癌患者的预后差(P=0.018)。为了进一步确定TGFβ1和胃癌总生存之间的关系，我们将临床病例参数分为两个不同的亚组。在年轻患者、女性、弥漫型、低分化和淋巴结转移的胃癌中，TGFβ1高表达与不良预后相关。单因素分析显示，年龄、Lauren分类、病理分级、浆膜侵犯、淋巴结转移和TGFβ1表达水平与胃癌预后相关。多因素分析显示，年龄、病理分级、浆膜侵犯和TGFβ1表达水平是胃癌生存的独立预后影响因子。
研究结论：本文探索了TGFβ1表达和胃癌预后之间的关系。结果表明，TGFβ1主要在胃癌的细胞膜和细胞质中表达，在胃癌组织中其表达水平显著高于癌旁组织。这一结果与以前的报道相似[18,22,26,30]。值得注意的是，在某些患者中，TGFβ1在胃癌细胞核和间质细胞中表达，从肿瘤微环境的角度来考虑，这或许具有重要意义。
尽管研究已经证实TGFβ1高表达与不良预后相关（包括本文），但是以前的研究没有明确TGFβ1表达是否与其它临床病理参数相关。本研究显示，在我们研究的临床病理参数中，TGFβ1表达水平与患者年龄、胃癌类型相关。有趣的是，TGFβ1与上皮间质转化相关，纤维化是弥漫型胃癌的典型特征，但是本研究显示，肠型胃癌中TGFβ1表达高于弥漫型胃癌。因此，需要更多的研究来进一步确认胃癌中TGFβ1的双向功能和预后价值。而且，Cox多因素风险比例模型显示，TGFβ1表达、年龄、病理分级和浆膜侵犯是独立的预后因子。
总之，本研究证实TGFβ1高表达与胃癌患者的不良预后相关，其高表达与年轻患者、女性患者、远端胃癌、低分化胃癌、弥漫型胃癌和淋巴结转移患者的不良预后相关。本文或许对TGFβ1治疗有一些启示，即在上述胃癌亚组中，抗TGFβ1的治疗或许更有效。
第二部分：量子点标记分子探针技术研究胃癌侵袭微环境的多因素分析
研究目的：胃癌是世界上第四大恶性肿瘤，居中国癌症死亡第三位。尽管这些年来，胃癌早期诊断及以手术为主的综合治疗方式有所改进，但胃癌患者的临床结局还是不乐观，其主要的问题在于肿瘤的侵袭转移。为了解决该难题，肿瘤学界在过去几十年里针对癌细胞开展了很多工作。最近肿瘤学界已经达成共识，肿瘤是一种免疫失衡的全身性疾病，不仅仅是单一肿瘤细胞的问题，而是全身细胞共同导致的问题。该理论的根基指引我们研究肿瘤不仅需要关注肿瘤细胞，还需要关注肿瘤微环境。因为，肿瘤微环境在肿瘤细胞与其微环境协同进化过程起了至关重要的作用。因此，我们迫切需要探讨肿瘤细胞及相关微环境的相互作用，以更好地揭示肿瘤侵袭转移的机制。
在胃癌中，肿瘤微环境是一个复杂且动态变化的集体，在肿瘤侵袭过程中不断发生改变，包括肿瘤细胞从肿瘤原位逃逸，渗入血管或淋巴管，在血管中定居，或粘附至血管内皮细胞，渗出血管或淋巴管进入远处器官定居并增殖，伴随肿瘤新生血管生成，形成转移灶。在这个过程中，肿瘤微环境中的众多成分相互作用，共同促进肿瘤侵袭转移。主要包括炎性细胞，如巨噬细胞；免疫细胞，如T和B淋巴细胞；间质细胞如成纤维细胞和不同成熟度的肿瘤新生血管。这些成分在解剖及空间分布上需要满足一定的特征，才能共同促进肿瘤侵袭转移。最近研究表明，肿瘤细胞、巨噬细胞和肿瘤新生血管在空间分布上相互靠近，形成一个特定结构，称为肿瘤转移的微环境。因此，为了同时展现这些关键成分，迫切需要发展原位同时共成像技术，以更好阐释肿瘤侵袭机制。
目前，能同时显示肿瘤组织切片上多种成分的技术很少。量子点，因其独特的尺寸和表面效应，在生物医学领域应用中展现出巨大的潜力。本研究利用量子点标记分子探针多色成像的技术，研究肿瘤细胞与巨噬细胞和肿瘤新生血管的相互作用关系，发展一种基于计算机辅助计算肿瘤侵袭单元的技术。
研究方法：1.收集胃癌患者病例资料及病理标本：通过建立和济医院2008-2012年的胃癌标本数据库，从中选择30例胃癌患者标本作为试验数据集，另外选择一组独立的60例胃癌患者标本作为验证数据集，完善相关的临床病理信息，及病理标本收集。2.基于量子点的肿瘤组织双色成像：上述收集的标本病理切片后，采用量子点标记分子探针技术原位双分子染色，对胃癌组织中的巨噬细胞及肿瘤新生血管进行原位共成像。3.图像采集及肿瘤侵袭单元计算：量子点双分子成像后，在荧光显微镜及多光谱分析软件下采集荧光图像，利用多光谱分析软件对巨噬细胞以及肿瘤新生血管的成像结果进行信号分离，在计算机辅助的图像处理技术辅助下，计算肿瘤侵袭转移单元数目。4.统计分析：所有的统计分析均在SPSS21.0软件中完成。
研究结果：1.肿瘤进展过程中肿瘤侵袭单元呈现两种不同的表现形式：在第一种类型中，肿瘤新生血管为纵向分布。多角形肿瘤新生血管在各个渗出的角中都伴随着巨噬细胞的浸润。另外，还能看到巨噬细胞浸润肿瘤新生血管，一半在血管外，一半在血管内。另一种类型的肿瘤侵袭单元中血管成横向分布，巨噬细胞可以分布在血管周围，靠近血管；也可以穿透血管，还可以在血管内定居。上述两种不同形态的肿瘤侵袭单元，以及肿瘤新生血管与巨噬细胞的动态变化，共同促进肿瘤侵袭转移。2.肿瘤侵袭单元与患者病理特征的关系：不同分化程度的胃癌患者的肿瘤侵袭单元数量不同，并且高分化管状腺癌、低分化管状腺癌及印戒细胞癌三组间的肿瘤侵袭单元具有显著性差异。首先，低分化组的巨噬细胞密度（1544）显著高于高分化组（317）和印戒细胞癌组（1011）。肿瘤新生血管的统计结果与肿瘤巨噬细胞的统计结果相似。低分化组的肿瘤新生血管密度最高（789），显著高于高分化组（474）和印戒细胞癌组（774）。并且，低分化组的肿瘤侵袭单元（373）显著高于低分化组（82）和印戒细胞癌组（177）。因此，这组试验数据集的结果提示肿瘤侵袭单元在三组之间有显著差异。3.肿瘤侵袭单元与临床预后的关系：验证数据集中60例患者的平均年龄为60.7岁，女性患者为41例，男性患者为19例，高分化管状腺癌为20例，低分化管状腺癌为23例，印戒细胞癌患者为17例，肿瘤未浸润至浆膜的为14例，浸润至浆膜及以外的为46例，无淋巴结转移的为18例，有淋巴结转移的为42例，无远处转移的为53例，有远处转移的为7例，肿瘤分期为早期的是21例，晚期的为39例，50例患者行根治性胃大部切除手术，10例患者为根治性胃全切手术，20例患者进行了术后化疗，另外40例未进行术后化疗，60例患者的总体生存期为17.8月。生存分析数据显示：肿瘤巨噬细胞密度和肿瘤侵袭单元是胃癌患者预后的独立预后因子，肿瘤新生血管不是独立的预后因子。肿瘤巨噬细胞数量越多，肿瘤侵袭单元越多，胃癌患者的预后越差。ROC工作曲线表明肿瘤侵袭单元的曲线下面积最大（63.2%），提示相较于巨噬细胞而言，肿瘤侵袭单元的独立预后预测效能更高。4.肿瘤侵袭单元对患者预后影响：为了更进一步证实，我们选择6个案例来做进一步阐述，3例是有相同临床TNM分期和临床治疗方式的低分化管状腺癌，3例是有相同临床TNM分期和临床治疗方式的高分化管状腺癌。深入分析上述几个案例，可以发现具有相同临床TNM分期及临床治疗策略的肿瘤患者，肿瘤侵袭单元数量越多，患者的总体生存期越短。3例低分化管状腺癌中，每200个高倍视野的肿瘤侵袭单元数量分别为7个，20个和58个，其对应的总体生存期分别为20.6月，11.1月和3.3月。另外的3例高分化管状腺癌中，每200个高倍视野的肿瘤侵袭单元数量分别为3个，11个和24个，其对应的总体生存期分别是42.6月，27.6月和14.3月。
研究结论：上述研究结果表明肿瘤细胞和肿瘤微环境的相互作用对胃癌患者的临床结局有显著影响。具体而言，肿瘤细胞和其周围微环境中的新生血管和浸润性巨噬细胞形成在空间分布上的结构，即肿瘤侵袭单元，促进肿瘤侵袭转移。肿瘤组织中肿瘤侵袭单元的数量与患者的预后及肿瘤分化程度呈负相关。另外，肿瘤侵袭单元的预后预测效能比单一的肿瘤微环境成分高。更有趣地是，我们发现肿瘤新生血管密度并不是胃癌患者预后的独立生存因子。这提示我们肿瘤新生血管对肿瘤进展的影响可能是受巨噬细胞调控的，这也同样证实了肿瘤侵袭单元的有效性。
量子点标记分子探针技术及多光谱分析技术对这项研究有突出贡献。量子点是一类具有独特的光学特性的纳米分子，在生物医学领域有广泛的应用前景。相较于传统的荧光染料和蛋白而言，量子点有独特的尺寸和表面效应。量子点具有显著的成像优势，包括组分可调性成像，荧光强度更高，抗漂白能力更强。另外，不同颜色的量子点能在相同波长的紫外光下激发，且光谱重叠窄，这使得其在多分子靶向原位成像技术中展现出突出的优势。正是这一特性，使得量子点很适合用于肿瘤侵袭单元的研究。在本研究中，巨噬细胞和肿瘤新生血管同时标记为绿色和红色。因此，相较于传统的人工合成不同激发光下的成像图片，这种基于量子点标记分子探针技术的成像技术真正实现了原位多分子共成像，可用于更好地研究肿瘤组织多种成分的相互作用。
肿瘤转移和复发是胃癌治疗失败的根本原因，肿瘤细胞侵袭是肿瘤发生远处转移的第一步，也是最重要的一步。目前，还没有合适的技术能预测肿瘤侵袭转移的风险。本研究结合量子点标记分子探针多色成像技术的优势，对肿瘤侵袭单元的成分包括肿瘤细胞、巨噬细胞和肿瘤新生血管进行成像，分析其空间分布关系，发展了计算机辅助的图像处理技术和算法。结果显示肿瘤侵袭单元具有较高的预测效能，再次证实了量子点标记分子探针原位多分子共成像技术在肿瘤多参数整合研究中的优势。
Based on the GLOBOCAN2008, about12.7million cancer cases and7.6million cancer deaths are estimated to have occurred in2008. In China, the gastriccancer (GC) incidence ranked the second in cancer, and third of the cancer mortality.Although, the early cancer screening techniques and the surgery guidedcomprehensive treatment strategies of GC developed rapidly recently, thereoccurrence and invasion after curative resection of GC still high. The root causesare cancer invasion and metastasis. Thus, the future research will focus on cancerinvasion and metastasis and comprehensive treatment strategy. Recently, more andmore researches demonstrated that the cancer invasion and metastasis is amulti-stage and multi-factor involved dynamic process. It not only includes thecancer cells themselves, but also the tumor microenvironment.
Due to the personal judgment errors of traditional pathological morphology, it isurgent to develop the quantitative analysis technique based on the molecularpathology. In addition, tumor microenvironment is a complex community, involvingmany components with different functions. The existed techniques are farsatisfactory to realize the in situ multiplexed imaging of tumor microenvironment.Thus, the in situ multiplexed imaging technique is urgently needed.
Quantum dots (QDs) are engineered nanoparticles with unique surface and sizeeffect so that it may have many advantages over fluorescent dyes, includingcontinuous emission spectrum, size-tunable, enhanced signal brightness andresistance to photobleaching. Recently, combined with the QDs based imagingtechnique, the molecular imaging, cellular imaging and in vivo imaging has progressed quickly. Due to the advantages of QDs in molecular imaging, the novelfluorescent nanoparticles have been widely used in the molecular target imaging.
This study focused on the clinical dilemma of GC, and explored the tumorbiological behavior from the molecular pathology of cancer cells and tumormicroenvironment. It aimed to reveal the co-evolution theory of cancer cells andtumor microenvironment combined with the application advantages of QDs basedmolecular probe, so as to in depth understand the mechanisms of GC invasion andmetastasis.
Two parts are included in this study:Part I: High Expression of Transform Growth Factor Beta1inGastric Cancer Confers Worse Outcome: Results of a Cohort Studyon184Patients
Objective: As a multifunctional cytokine, transform growth factors beta (TGFβ)regulates many biological processes by protein kinase receptors and drosophilamothers against decapentaplegic protein (SMAD) mediators, and plays importantroles in tumor growth, invasion and metastasis. Briefly, in healthy system, TGFβ actsas a tumor suppressor to inhibit cell proliferation, induce apoptosis and regulateautophagy. With tumor development, TGFβ will promote epithelial-mesenchymaltransition (EMT), tumor cell motility, invasion and metastasis.
GC is one of the most common carcinomas and the third cause of cancer deathin the world. GC is divided into two types based on Lauren classification: diffusetype and intestinal type. Different with intestinal type GC, diffuse type GC ischaracterized by a diffusely infiltrating growth accompanied by stromal fibrosis anda poor prognosis. TGFβ1just is a powerful fibrotic stimulating factor, and contribute to create a favorable environment for the dissemination of GC. Compared withnormal gastric tissues, TGFβ1is significantly up-regulated in GC. Furthermore,TGFβ1over-expression of in GC tissues is correlated with GC prognosis, especiallyin diffused type GC. These studies may imply TGFβ1has different roles in differenttype GC. What is more, because of the complex secretion mechanism andbidirectional function of TGFβ1, previous studies may not accurately describe therelationship between TGFβ1expression and patients prognosis. Therefore, it isdesirable to explore the relationship between TGFβ1expression and prognosis ofpatients with GC in different clinicopathological subgroups. This study was mainlyto investigate TGFβ1expression and its clinical significance in differentclinicopathological subgroups of GC patients by immunohistochemistry method.
Methods:1. Patients: A total of184GC patients receiving surgery-basedmulti-disciplinary treatment from December2002to February2011were collected.The patients’ paraffin specimens were constructed into three tissues microarrays(TMAs) which contained184tumor tissues and41peritumoral tissues (450cores,two cores for each tissues), developed by Shanghai Biochip Company Ltd.(Shanghai, China). In order to control the quality of TMAs, only tumor tissues at thecancer invasion front were selected, so that the typical biological behaviors of cancercould be revealed. TNM stage and pathologic classification were determinedaccording to the7th edition AJCC TNM system and Lauren classification,respectively. Up to May31,2012, the median follow-up of those GC patients was57.3(range:16.6-100.1) mo, with108(58.7%) GC-specific deaths occurred.2.Immunohistochemistry of TGFβ1: SP immunohistochemistry method was used tostain the TMAs. Briefly, after de-waxing, the TMAs were performed microwaveantigen retrieval for20min at moderate baking temperature in0.01mmol/L (pH=6.0)citrate buffer solution. After cooling at room temperature, TMAs were treated with 0.03%hydrogen peroxide methanol for10min to inactivate endogenous peroxidase.Then2%BSA was used to block TMAs to decrease background intensity. Everychip treated overnight at4℃with250μl rabbit anti-human TGFβ1monoclonalantibodyand then incubated with the corresponding secondary antibody (1:250dilution) for30min at37℃. Then treated with0.2%diaminobenzidine solution for2min to coloration. The TMAs were counterstained with hematoxylin anddifferentiated by hydrochloric acid alcohol.3. Result evaluation: The results ofimmunohistochemistry were analyzed by two experienced pathologists with over10years of experience in clinical tumor pathology. The scoring system combined cellsstaining intensity with cells positive rate as reported previously, and briefed in thefollowing: Cells staining intensity was defined as no stain, slight stain, medium stainand strong stain, and correspondingly scored as0,1,2and3, respectively. Cellspositive rate was defined as grade1with positive TGFβ1immunostaining in <10%of tumor cells stained positive, grade2with10%to25%of tumor cells stainedpositive, grade3with25%to50%of tumor cells stained positive, and grade4with>50%of tumor cells stained positive, and correspondingly scored as0,1,2and3,respectively. The final score was the sum of cells staining intensity score and cellspositive rate score. The sum score0-3was defined as low expression and4-6as highexpression.4. Statistical analysis: Statistical analysis was performed with SPSS17.0software. Cumulative survival was calculated by the Kaplan-Meier method andanalyzed by the Log-rank test. Correlation test was calculated by Pearson chi-square.Univariate and multivariate survival analysis were performed with the Coxproportional hazards method. P＜0.05was judged to be significant.
Results:1. TGFβ1expression in GC: TGFβ1expressed in166(90.2%) GCtumors and33(80.5%) peritumoral tissues, as brownish fine granules in thecytoplasm and membrane of GC cells. Based on the above evaluation criteria,82 (44.6%) GC tissues and28(68.3%) peritumoral tissues had low expression, and102(55.4%) GC tissues and13(31.7%) peritumoral tissues had high expression. Theexpression of TGFβ1in GC tissues were significantly higher than in peritumoraltissues (χ2=7.554, P=0.006). In some patients, expression of TGFβ1in tumor stromaand cancer cell was also observed.2. The relationship between TGFβ1expressionand clinicopathological parameters: TGFβ1expression was higher in the old thanin the young (P=0.017) and higher in intestinal type GC than in diffuse type GC(P=0.015). There were no statistically significant differences in TGFβ1among otherclinicopathological parameters.3. The relationship between clinicopathologicalparameters and OS of GC patients stratified by TGFβ1expression: Highexpression of TGFβ1was related to worse OS of GC patients. Log-rank test showedthat diffuse type GC patients had a poor survival than intestinal type GC patients(P=0.018). To further evaluate the relationship between TGFβ1expression and OS,the clinicopathological parameters were divided into different subgroups. HighTGFβ1expression had a worse survival in young people, female, diffuse type GC,poor differentiation, and lymph nodes metastasis. Univariate analysis showed thatage, Lauren classification, pathological grading, serosal invasion, lymph nodesmetastasis and TGFβ1expression were risk factors. Multivariate Cox proportionalhazards analysis showed that age, pathological grading, serosal invasion and TGFβ1expression were independent risk factors.
Conclusion: This study explored the relationship between TGFβ1expressionand GC prognosis. The results showed TGFβ1mainly expressed in the GCcytoplasm and cytomembrane, with significantly higher expression in GC tissuesthan peritumoral tissues. This finding was similar to previous reports [18,22,26,30].What deserves more attention was the finding that some positive staining was alsofound in GC nucleus and stromal cells in some patient. This may had special significance from the perspectives of tumor microenvironment.
Although it has been demonstrated that TGFβ1high expression was related topoor prognosis (including our study), previous studies have not defined whetherTGFβ1expression was related to other clinicopathological parameters. Our studyonly showed TGFβ1expression was related to patients’ age and GC type among theclinicopathological parameters investigated. Interestingly, TGFβ1is involved withEMT, and fibrosis is the typical feature of diffuse type GC, but this study showedTGFβ1expression was higher in intestinal type GC than in diffuse type GC.Therefore, more studies are needed to further elucidate the bidirectional functionsand prognosis value of TGFβ1in GC. Furthermore, Cox’s proportional hazard modelshows that TGFβ1expression, age, clinicopathologicalical grading and serosalinvasion were independent risk factors.
In conclusion, this study has demonstrated that TGFβ1high expression wasrelated to poor prognosis of GC patients, who tend to be young, female, distantgastric tumor with poor differentiation, diffuse type, and lymph nodes metastasis.The present study perhaps has some enlightenment for TGFβ1treatment, namely inthe above subgroups GC patients, the treatment aim at TGFβ1would be moreeffective.
Part II: Tumor invasion unit in gastric cancer revealed byQDs-based in situ molecular imaging and multispectral analysis
Objective: Gastric cancer (GC) is the fourth most common cancer worldwide,and the third leading cancer cause in China. Despite recent progresses in the earlydiagnosis and the surgery-centered multidisciplinary treatments for GC, the overallclinical outcome of such patients is still far from satisfactory, mainly due to the post-treatment occurrence and metastasis, via blood circulation, lymphatic channelsor direct cancer cells invasion and seeding. To tackle this problem, many effortsfocusing on cancer cells have been made. And eventually, the oncology communityhas come to the understanding that cancer is a disease of imbalance, i.e., not merelya disease of rogue cells but the body’s mismanagement of those cells, thefundamental importance of such theoretical changes is that we have to pay particularattention to tumor microenvironment, in addition to cancer cells, because tumormicroenvironment plays an important role via the co-evolution of tumor cells andstroma. Thus, it is urgent ro explore the co-evolution of tumor cells and stroma.
In GC, tumor microenvironment is a complex and dynamic community, whichis undergoing constant evolutions during cancer invasion, involving tumor cellsescape from primary sites into vasculature (blood circulation and lymphatic channel),reside and adhere to endothelial cells, penetrate from vasculature into other organsand reside in them, accompanied with tumor neo-vessels growth. Many importantcomponents in tumor microenvironment work together to contribute to cancerinvasion. Major components in tumor microenvironment are inflammatory cells suchas macrophages, immune cells such as T and B lymphocytes, stromal cells such asfibroblasts, and neo-blood vessels of various stages of maturity. These players mustbe in an appropriate anatomic proximity and spatial vicinity with the tumor cells inorder to facilitate cancer invasion. And indeed, recent studies have shown that tumorcells, macrophages and tumor neo-vessels in close vicinity with one another form aunique structure called tumor microenvironment of metastasis (TMEM), or in moreeasily understandable terms, called ‘tumor invasion unit’. Therefore, thesimultaneous recognition and analysis of all the components in the tumor invasionunit is very important to understanding the new perspective of cancer invasion.
There are few techniques that can simultaneously image multiple components in complex tumor microenvironment of the same tissue section. Thus, it is urgent todevelop a more holistic method to image the complex interactions of stromalcomponents in situ. Quantum dots, with its unique size and surface effects, haveshown great potential in biomedical application, especially in multiplexed imagingin situ. In this study, taking the advantages of established QDs-based multiplexedimaging in situ, we analyzed on the interactions between macrophages, tumorneo-vessels and cancer cells, and developed a computer-based algorithm of tumorinvasion units.
Methods:1. Patients and specimens: Tissue sections (4m thickness) of90human GC cases were selected from the central database on GC established at ourcancer center, including30with detailed pathological information, and60withcomplete clinic-pathological and survival information available on the patients.Tumor tissues from the first set of30patients were used for a trial study, to explorethe correlation of tumor invasion unit with classical pathological features, in order totest if there was any relationship between tumor invasion unit unfavorablepathological features. Tumor tissues from the second set of60patients with detailedsurvival information were used for validation study, in order to further verify iftumor invasion unit could predict the overall survival (OS).2. QDs-based doublemolecular imaging: The QDs-based in situ molecular imaging procedures wereperformed with the following major steps: tissue slides de-paraffinizing→antigenretrieval→blocking→primary antibody for macrophages and CD105→washingand blocking→staining with QDs-525, QDs-585or QDs-655→washing→detectionand acquisition.3. Image capture and analysis: The QDs stained images werecaptured by Olympus DP72cameraunder CRi Nuance multispectral imaging system.The QDs-525, QDs-585, QDs-655were excited by UV light (330-385nm). Aspectral cube for each image, which contains the complete spectral information at10 nm wavelength intervals from420-720nm, was collected by the CRi Nuancemultispectral imaging system. And all the cubes were captured under the samecondition at×200magnification with the same settings for each image, so as toavoid the selection bias. The QDs fluorescence signal unmixing was processed bythe software package within the Nuance system. Then, after obtaining the images ofsignal unmixing, the macrophages were counted on each image and the totalmacrophages was documented as the counts for the patient for further analysis. Thesame calculation method was used for tumor neo-vessels counting. After processingQDs images, the images with double signals of both macrophages and tumorneo-vessels at the tumor nest area were acquired. As the tumor invasion unitconsisted of cancer cells, macrophages and tumor neo-vessels, a circle with thediameter of60m centered on macrophages was chosen, approximately three celldiameters across. With the computer-based algorithm, if there was any red signal inthis circle, it was counted as1, otherwise; it was counted as0. Then the total countsof five images for each patient was output as the result of tumor invasion units.4.Statistical analysis: Statistical analyses were performed with SPSS software version21.0. For the comparison of individual variables, Fisher’s exact test, t test andMann-Whitney Test were conducted as appropriate. The Kaplan-Meier survivalcurves were plotted to analyze the OS by different study parameters, with log ranktest to define the statistical differences between the subgroups. Two sided P <0.05was judged as statistically significant.
Results:1. Two types of tumor invasion unit with dynamic changes: Basedon the spreading and layout patterns of neo-vessels and the macrophages infiltrationsteps, two forms of tumor invasion units in constant dynamic changes could berecognized. In form one, the tumor neo-vessel was seen in longitudinal section. Withthe special longitudinal spreading and irregular morphology, the tumor neo-vessels presented to be multi-angled accompanied with macrophages infiltration at eachangle. Macrophages undergoing intravasation could also be observed, half in andhalf out of the blood vessel. In form two the tumor neo-vessel is seen in cross section,with macrophages lodging the vessel with membrane processes, crossing the vesselwall, and in close vicinity to the vessel, thus revealing the dynamic interactionsbetween macrophages and neo-vessels to facilitate tumor invasion.2. Tumorinvasion unit was correlated with worse clinico-pathological features in30GCcases in the trial study: First, we selected a trial set of30GC patients with the threemost common pathological types, including well differentiated, poorly differentiatedand signet-ring cell carcinoma. These patients were classified into3groups bydifferent pathological types,10cases in each group. According to the quantitativeanalysis, the mean macrophages density was higher in the poorly differentiatedgroup (1544) than the well differentiated group (317) and the signet-ring cell group(1011), with statistical significance in three groups. The results of tumor neo-vesselsanalyses were the same as the macrophages. The poorly differentiated group had thehighest count but the well differentiated group had the lowest counts, with statisticalsignificances among three groups (P <0.001, for between-group comparisons). Andalso, the number of tumor invasion units was much higher in the poorlydifferentiated group (373) than the well differentiated group (82) or the signet-ringcell group (177), with statistical significance in three groups (P=0.000, for allbetween-group comparisons). Thus, in this trial set, compared with the analysisresults of macrophages and tumor neo-vessels, tumor invasion units had the similareffects. However, the P value of tumor invasion unit was the smallest, suggesting itsstronger correlation with worst histological types.3. The impact of tumor invasionunit on clinical outcome: Taking the median value of macrophages, neo-vessels andtumor invasion units as the cut-off value, these parameters were divided into high density groups if the individual value was above the cut-off value, and low densitygroups if the individual value was below the cut-off value. Their OS curves bymacrophages density, neo-vessels density and tumor invasion units showed that bothmacrophages and tumor invasion units were independent factors to impact onsurvival, while tumor neo-vessel density itself was not an independent factor forsurvival. ROC analysis also demonstrated that tumor invasion unit had the largestarea under the curve (63.2%), suggesting that tumor invasion unit had biggerpredicting power than macrophages for OS prediction.4. Typical examples ofimpacts of tumor invasion unit on clinical prognosis: To further validate thisobservation, we selected6cases for detailed analysis,3cases of poorlydifferentiated adenocarcinoma with identical TNM stages and clinical treatments,and another3cases of highly differentiated adenocarcinoma with identical TNMstages and clinical treatments. From these cases, it could be observed more clearlythat the higher the number of tumor invasion units, the shorter the OS, regardless ofthe tumor differentiation and histological types. In the3cases of poorlydifferentiated adenocarcinoma, the tumor invasion units were7,20and58per×200magnification field, respectively; and the corresponding OS were20.6,11.1and3.3months, respectively. In another3cases of well differentiated adenocarcinoma, thetumor invasion units were3,11, and24per×200magnification field, respectively;and the corresponding OS were42.6,27.6, and14.3months, respectively.
Conclusion: This study demonstrated that the complex and constantinteractions between tumor cells and their microenvironment could have asignificant impact on the clinical outcomes of GC patients. Specifically, tumor cells,new blood vessels due to tumor angiogenesis and infiltrating macrophages in thetumor microenvironment could form a spatially very close entity called tumorinvasion unit that facilitates tumor invasion. The number of tumor invasion units in the tumor tissue is negatively correlated with poor tumor differentiation and worsesurvival. In addition, the tumor invasion unit has bigger predictive power of OS thansingle component counts of macrophages and tumor neo-vessels, respectively.Interestingly, we have found that the tumor neo-vessels density was not anindependent prognostic factor for OS. This may suggest that the effects of tumorneo-vessels on cancer invasion can be regulated by tumor macrophages infiltrationwith synergistic effect, even at early stages of cancer development. This could atleast account for the emerging concept of the tumor invasion unit. QDs-basedmolecular imaging and multispectral analysis could make a unique contribution inthis regard, as the current study demonstrated. QDs are engineered nanoparticleswith unique optical properties suitable for biomedical application. Compared withorganic dyes and fluorescent proteins, due to its unique size and surface feature, QDshave many advantages such as composition-tunable light emission, enhancedfluorescence brightness, strong resistance to photobleaching and chemicaldegradation. In addition, different colors of QDs can be simultaneously excited by asingle light source, with minimal spectral overlapping, which provides significantadvantages for multiplexed detection of target. This property is very suitable forinvestigating the complex interactions between tumor cells and their surroundingmicroenvironment at the architectural level. In this study, the infiltratingmacrophages and tumor angiogenesis were simultaneously labeled green and red,respectively. The blue auto-fluorescence also showed clear tumor backgroundstructure. Therefore, instead of producing artificial overlay images by conventionalimaging techniques, this QDs based molecular imaging technique could provideauthentic multicolor images to better reveal the complex interactions of differentcomponents in the tumor tissue.
Cancer recurrence and metastasis is the root cause of treatment failure in GC, and tumor cells escape is the first and most important step towards distant metastasis.There are currently no reliable methodologies to predict the risk for metastaticdisease. Taking the advantages of QDs-based multiplexed molecular imagingtechnology, this study has simultaneously revealed the spatial distribution of tumorinvasion unit, consisting of tumor cells, macrophages and tumor neo-vessels anddeveloped a computer-based algorithm for tumor invasion unit analysis at molecularlevel. Both the novel approach and the results in this study suggested the greatpredictive power of tumor invasion unit for OS. A next step will be to validate thesefindings in a larger, independent and standardized GC database with known clinicaloutcome. If the results can be substantiated, the tumor invasion unit could be apowerful tool addition to the current approach for guiding personalized therapy andassessing prognosis, so as to better prevent over-treatment and under-treatment ofcancer patient.
引文
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[6] Page-McCaw A, Ewald AJ, and Werb Z. Matrix metalloproteinases and theregulation of tissue remodelling. Nat Rev Mol Cell Biol,2007,8(3):221-233.
[7] Yasui W, Oue N, Aung PP, et al. Molecular-pathological prognostic factors ofgastric cancer: a review. Gastric Cancer,2005,8(2):86-94.
[8] Peng CW, Tian Q, Yang GF, et al. Quantum-dots based simultaneous detectionof multiple biomarkers of tumor stromal features to predict clinical outcomesin gastric cancer. Biomaterials,2012,33(23):5742-5752.
[9] Robinson BD, Sica GL, Liu YF, et al. Tumor microenvironment of metastasisin human breast carcinoma: a potential prognostic marker linked tohematogenous dissemination. Clin Cancer Res,2009,15(7):2433-2441.
[10] Fang M, Yuan JP, Peng CW, et al. Quantum dots-based in situ molecularimaging of dynamic changes of collagen IV during cancer invasion.Biomaterials,2013,34(34):8708-8717.
[11] Fantin A, Vieira JM, Gestri G, et al. Tissue macrophages act as cellularchaperones for vascular anastomosis downstream of VEGF-mediatedendothelial tip cell induction. Blood,2010,116(5):829-840.
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[13] Wyckoff J, Wang W, Lin EY, et al. A paracrine loop between tumor cells andmacrophages is required for tumor cell migration in mammary tumors. CancerRes,2004,64(19):7022-7029.
[14] Kaplan RN, Riba RD, Zacharoulis S, et al. VEGFR1-positive haematopoieticbone marrow progenitors initiate the pre-metastatic niche. Nature,2005,438(7069):820-827.
[15] Psaila B and Lyden D. The metastatic niche: adapting the foreign soil. Nat RevCancer,2009,9(4):285-293.
[16] Kim MY, Oskarsson T, Acharyya S, et al. Tumor self-seeding by circulatingcancer cells. Cell,2009,139(7):1315-1326.
[17] Comen E, Norton L, and Massague J. Clinical implications of cancerself-seeding. Nat Rev Clin Oncol,2011,8(6):369-377.
[18] Liu Q, Zhang A, Xu W, et al. A new view of the roles of blood flow dynamicsand Kupffer cell in intra-hepatic metastasis of hepatocellular carcinoma. MedHypotheses,2011,77(1):87-90.
[19] Sethi N and Kang Y. Unravelling the complexity of metastasis-molecularunderstanding and targeted therapies. Nat Rev Cancer,2011,11(10):735-748.
[20] Kerbel RS. Tumor angiogenesis. N Engl J Med,2008,358(19):2039-2049.
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[22] Kairdolf BA, Smith AM, Stokes TH, et al. Semiconductor quantum dots forbioimaging and biodiagnostic applications. Annu Rev Anal Chem (Palo AltoCalif),2013,6:143-162.
[23] Chen C, Peng J, Sun SR, et al. Tapping the potential of quantum dots forpersonalized oncology: current status and future perspectives. Nanomedicine(Lond),2012,7(3):411-428.
[1] Jemal A, Bray F, Center MM, et al. Global cancer statistics. CA Cancer JClin,2011,61(2):69-90.
[2]季加孚.我国胃癌防治30年回顾.中国肿瘤临床，2013，40（22）：1346-1351.
[3] Bindra RS and Glazer PM. Genetic instability and the tumormicroenvironment: towards the concept of microenvironment-inducedmutagenesis. Mutat Res,2005,569(1-2):75-85.
[4] Coussens LM and Werb Z. Inflammation and cancer. Nature,2002,420(6917):860-867.
[5] Quante M and Wang TC. Inflammation and stem cells in gastrointestinalcarcinogenesis. Physiology (Bethesda),2008,23:350-359.
[6] Qian BZ and Pollard JW. Macrophage diversity enhances tumor progressionand metastasis. Cell,2010,141(1):39-51.
[7] Karnoub AE, Dash AB, Vo AP, et al. Mesenchymal stem cells within tumourstroma promote breast cancer metastasis. Nature,2007,449(7162):557-563.
[8] Grivennikov SI, Greten FR, and Karin M. Immunity, inflammation, andcancer. Cell,2010,140(6):883-899.
[9] de Visser KE, Eichten A, and Coussens LM. Paradoxical roles of the immunesystem during cancer development. Nat Rev Cancer,2006,6(1):24-37.
[10] Balkwill F, Charles KA, and Mantovani A. Smoldering and polarizedinflammation in the initiation and promotion of malignant disease. CancerCell,2005,7(3):211-217.
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[1] Jemal A, Bray F, Center MM, et al. Global cancer statistics. CA Cancer J Clin,2011,61(2):69-90.
[2] Chen W, Zheng R, Zhang S, et al. The incidences and mortalities of majorcancers in China,2009. Chin J Cancer,2013,32(3):106-112.
[3] Hanahan D and Weinberg RA. Hallmarks of cancer: the next generation. Cell,2011,144(5):646-674.
[4] Gerlinger M, Rowan AJ, Horswell S, et al. Intratumor heterogeneity andbranched evolution revealed by multiregion sequencing. N Engl J Med,2012,366(10):883-892.
[5] Rosner M and Hengstschlager M. Targeting epigenetic readers in cancer. NEngl J Med,2012,367(18):1764-1765.
[6] Page-McCaw A, Ewald AJ, and Werb Z. Matrix metalloproteinases and theregulation of tissue remodelling. Nat Rev Mol Cell Biol,2007,8(3):221-233.
[7] Yasui W, Oue N, Aung PP, et al. Molecular-pathological prognostic factors ofgastric cancer: a review. Gastric Cancer,2005,8(2):86-94.
[8] Peng CW, Tian Q, Yang GF, et al. Quantum-dots based simultaneous detectionof multiple biomarkers of tumor stromal features to predict clinical outcomesin gastric cancer. Biomaterials,2012,33(23):5742-5752.
[9] Robinson BD, Sica GL, Liu YF, et al. Tumor microenvironment of metastasisin human breast carcinoma: a potential prognostic marker linked tohematogenous dissemination. Clin Cancer Res,2009,15(7):2433-2441.
[10] Fang M, Yuan JP, Peng CW, et al. Quantum dots-based in situ molecularimaging of dynamic changes of collagen IV during cancer invasion.Biomaterials,2013,34(34):8708-8717.
[11] Fantin A, Vieira JM, Gestri G, et al. Tissue macrophages act as cellularchaperones for vascular anastomosis downstream of VEGF-mediatedendothelial tip cell induction. Blood,2010,116(5):829-840.
[12] Stockmann C, Doedens A, Weidemann A, et al. Deletion of vascularendothelial growth factor in myeloid cells accelerates tumorigenesis. Nature,2008,456(7223):814-818.
[13] Wyckoff J, Wang W, Lin EY, et al. A paracrine loop between tumor cells andmacrophages is required for tumor cell migration in mammary tumors. CancerRes,2004,64(19):7022-7029.
[14] Kaplan RN, Riba RD, Zacharoulis S, et al. VEGFR1-positive haematopoieticbone marrow progenitors initiate the pre-metastatic niche. Nature,2005,438(7069):820-827.
[15] Psaila B and Lyden D. The metastatic niche: adapting the foreign soil. Nat RevCancer,2009,9(4):285-293.
[16] Kim MY, Oskarsson T, Acharyya S, et al. Tumor self-seeding by circulatingcancer cells. Cell,2009,139(7):1315-1326.
[17] Comen E, Norton L, and Massague J. Clinical implications of cancerself-seeding. Nat Rev Clin Oncol,2011,8(6):369-377.
[18] Liu Q, Zhang A, Xu W, et al. A new view of the roles of blood flow dynamicsand Kupffer cell in intra-hepatic metastasis of hepatocellular carcinoma. MedHypotheses,2011,77(1):87-90.
[19] Sethi N and Kang Y. Unravelling the complexity of metastasis-molecularunderstanding and targeted therapies. Nat Rev Cancer,2011,11(10):735-748.
[20] Kerbel RS. Tumor angiogenesis. N Engl J Med,2008,358(19):2039-2049.
[21] Rolny C, Mazzone M, Tugues S, et al. HRG inhibits tumor growth andmetastasis by inducing macrophage polarization and vessel normalizationthrough downregulation of PlGF. Cancer Cell,2011,19(1):31-44.
[22] Kairdolf BA, Smith AM, Stokes TH, et al. Semiconductor quantum dots forbioimaging and biodiagnostic applications. Annu Rev Anal Chem (Palo AltoCalif),2013,6:143-162.
[23] Chen C, Peng J, Sun SR, et al. Tapping the potential of quantum dots forpersonalized oncology: current status and future perspectives. Nanomedicine(Lond),2012,7(3):411-428.
[1] Jemal A, Bray F, Center MM, et al. Global cancer statistics. CA Cancer JClin,2011,61(2):69-90.
[2]季加孚.我国胃癌防治30年回顾.中国肿瘤临床，2013，40（22）：1346-1351.
[3] Bindra RS and Glazer PM. Genetic instability and the tumormicroenvironment: towards the concept of microenvironment-inducedmutagenesis. Mutat Res,2005,569(1-2):75-85.
[4] Coussens LM and Werb Z. Inflammation and cancer. Nature,2002,420(6917):860-867.
[5] Quante M and Wang TC. Inflammation and stem cells in gastrointestinalcarcinogenesis. Physiology (Bethesda),2008,23:350-359.
[6] Qian BZ and Pollard JW. Macrophage diversity enhances tumor progressionand metastasis. Cell,2010,141(1):39-51.
[7] Karnoub AE, Dash AB, Vo AP, et al. Mesenchymal stem cells within tumourstroma promote breast cancer metastasis. Nature,2007,449(7162):557-563.
[8] Grivennikov SI, Greten FR, and Karin M. Immunity, inflammation, andcancer. Cell,2010,140(6):883-899.
[9] de Visser KE, Eichten A, and Coussens LM. Paradoxical roles of the immunesystem during cancer development. Nat Rev Cancer,2006,6(1):24-37.
[10] Balkwill F, Charles KA, and Mantovani A. Smoldering and polarizedinflammation in the initiation and promotion of malignant disease. CancerCell,2005,7(3):211-217.
[11] Colotta F, Allavena P, Sica A, et al. Cancer-related inflammation, the seventhhallmark of cancer: links to genetic instability. Carcinogenesis,2009,30(7):1073-1081.
[12] Condeelis J and Pollard JW. Macrophages: obligate partners for tumor cellmigration, invasion, and metastasis. Cell,2006,124(2):263-266.
[13] Oguma K, Oshima H, Aoki M, et al. Activated macrophages promote Wntsignalling through tumour necrosis factor-alpha in gastric tumour cells.EMBO J,2008,27(12):1671-1681.
[14] Wu Y, Deng J, Rychahou PG, et al. Stabilization of snail by NF-kappaB isrequired for inflammation-induced cell migration and invasion. Cancer Cell,2009,15(5):416-428.
[15] Peng CW, Liu XL, Liu X, et al. Co-evolution of cancer microenvironmentreveals distinctive patterns of gastric cancer invasion: laboratory evidenceand clinical significance. J Transl Med,2010,8:101.
[16] Peng CW, Liu XL, Chen C, et al. Patterns of cancer invasion revealed byQDs-based quantitative multiplexed imaging of tumor microenvironment.Biomaterials,2011,32(11):2907-2917.
[17] Peng CW, Tian Q, Yang GF, et al. Quantum-dots based simultaneousdetection of multiple biomarkers of tumor stromal features to predict clinicaloutcomes in gastric cancer. Biomaterials,2012,33(23):5742-5752.
[18] Hanahan D and Folkman J. Patterns and emerging mechanisms of theangiogenic switch during tumorigenesis. Cell,1996,86(3):353-364.
[19] Baeriswyl V and Christofori G. The angiogenic switch in carcinogenesis.Semin Cancer Biol,2009,19(5):329-337.
[20] Bergers G and Benjamin LE. Tumorigenesis and the angiogenic switch. NatRev Cancer,2003,3(6):401-410.
[21] Carmeliet P and Jain RK. Angiogenesis in cancer and other diseases. Nature,2000,407(6801):249-257.
[22] Ferrara N. Pathways mediating VEGF-independent tumor angiogenesis.Cytokine Growth Factor Rev,2010,21(1):21-26.
[23] Mac Gabhann F and Popel AS. Systems biology of vascular endothelialgrowth factors. Microcirculation,2008,15(8):715-738.
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