结直肠癌预后评估及预测模型的建立
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摘要
结直肠癌是严重危害人类健康的常见恶性肿瘤之一。在经济发达的美国,结直肠癌的死亡率位列恶性肿瘤中的第3位。我国结直肠癌的死亡率居于恶性肿瘤死亡率的第4-5位之间,并呈现增加的趋势。至今为止,传统的TNM分期依然是临床判断预后的金标准,仍旧没有一个可以替代它的预后标准。但TNM分期在预后评判上存在一定的缺陷,相同TNM分期的患者预后有时相差悬殊。一个完善的预后评估体系可以指导临床实践并节约医疗费用,我们认为随着一些有特色的临床和病理指标及大量分子标记在预后中作用的研究,寻求并建立一个更精确、更个性化的预后评估系统,已经是一个迫切的任务。
之前,我们实验室已建立有221例结直肠癌患者的基本信息数据库。这221例患者来自萧山肿瘤登记系统中1990-2000年期间发病的患者,通过复习患者病案和H.E.切片,调查收集了比较全面的临床信息和病理信息,并已包含4个分子指标的免疫组化结果(P53、Ki67、Bcl2、CXCR4)。该数据库中已剔除术后一个月内死亡的病例和死因与结直肠癌无关的病例。所有随访信息由萧山肿瘤登记系统提供,原有数据库中的随访信息截止于2002年12月31日。我们在原有数据库的基础上,又增加了9个分子指标的免疫组化结果(NF-κB、Sydecan-1、β-catenin、CD68、PPARγ、IGF1R、IGFBP7、IR、Thymidylate Synthase),并对随访信息进行了更新,所有随访信息更新至2007年3月31日。221例患者中结肠癌71例,直肠癌151例;男性121例、女性100例;确诊时年龄最大者85岁,最小者26岁,中位年龄为59岁;随访时间最长203个月,最短1个月,平均随访时间为92个月。TNMⅠ期35例、Ⅱ期84例、Ⅲ期80例、Ⅳ期22例;低级别癌148例、高级别癌73例;管状腺癌164例、乳头状癌26例、粘液腺癌27例、印戒细胞癌和未分化癌4例。总体1年、3年、5年、10年累积生存率分别为79%、72%、70%和67%。此221例结直肠癌患者在预后数学建模过程中被作为训练集。
另外,我们又从萧山肿瘤登记系统中,选取2001-2006年期间发病的患者,进行档案资料的调查,最终获得手术医院为萧山第一人民医院的288名档案资料完整的结直肠癌患者。复习所有病案,获取临床病理资料,同时剔除术后一个月内死亡的病例,并剔除死因与结直肠癌无关的病例。所有随访信息截止于2007年3月31日。全部288例病例均经病理证实,每例选择有代表性的含肿瘤组织蜡块进行切片,行H.E.染色和免疫组织化学染色。288例患者中结肠癌167例、直肠癌121例;男性153例、女性135例;确诊时年龄最大者91岁,最小者24岁,平均年龄为64岁;随访时间最长75个月,最短1个月,平均随访时间为30个月。TNMⅠ期62例、Ⅱ期81例、Ⅲ期114例、Ⅳ期31例;低级别癌215例、高级别癌73例;管状腺癌225例、乳头状癌12例、粘液腺癌25例、印戒细胞癌和其它26例。总体1年、3年、5年累积生存率分别为84%、76%、75%。此288例结直肠癌患者在预后数学建模过程中被作为验证集。
采用SPSS 16.0 for windows统计学软件(SPSS Inc.)进行以下统计学处理。用寿命表法计算累积生存率;用Kaplan-Meier法进行单因素生存分析(用log-rank法进行显著性检验),并绘制生存曲线。将单因素分析对预后有统计学意义的因素再纳入多因素COX比例风险模型进行分析。多因素COX比例风险模型采用向前逐步回归法。依据多因素COX比例风险模型结果计算每个病例的预后指数,并使用内插法计算1年、3年、5年基准死亡风险函数。从而建立基于预后指数的1年、3年、5年个体死亡风险函数的数学预测模型。
采用MedCalc software 9.3.0.0(Frank Schoonjans)软件,运用ROC曲线评价预后指数在生存评判中的价值,并依据Youden指数最大的原则确定cut off值,进行预后指数分级,比较预后指数分级与传统TNM分期的预后评判价值。
软件Cluster3.0(Stanford University)用于非监督系统聚类分析,采用spearman等级相关作为聚类统计量,类间归类方法采用最长距离法,结果用Treeview(Stanford University)显示。组间构成比比较采用χ~2检验或Fisher's精确检验。P值≤0.05认为有统计学意义,0.05 首先单因素生存分析训练集的17个临床和病理指标、13个分子指标的预后意义。这17个临床和病理指标包括:年龄、性别、部位、组织学类型、组织学分级、肿瘤浸润深度、淋巴结转移情况、生长方式、脉管侵犯、神经周围侵犯、肿瘤芽、肿瘤实质淋巴细胞浸润、肿瘤间质淋巴细胞浸润、Crohn样反应、化疗、远处转移情况、TNM分期;13个分子指标分别涉及抑癌基因(P53)、凋亡(Bcl2)、增殖(Ki67)、转录因子(NF-κB)、细胞外基质受体(Sydecan-1)、细胞粘附分子(β-catenin)、巨噬细胞(CD68)、趋化因子受体(CXCR4)、核激素受体(PPARγ)、胰岛素样生长因子家族(IGF1R、IGFBP7)、胰岛素受体(IR)、和化疗(ThymidylateSynthase)。结果表明组织学类型、组织学分级、肿瘤浸润深度、淋巴结转移情况、生长方式、脉管侵犯、神经周围侵犯、肿瘤芽、肿瘤实质淋巴细胞浸润、肿瘤间质淋巴细胞浸润、Crohn样反应、化疗、远处转移情况、TNM分期、P53、侵袭前缘巨噬细胞计数、CXCR4前缘增强现象、IGFBP7,共18个指标具有预后意义。将这18个指标以及年龄作为校正因素纳入多因素COX比例风险模型,结果表明年龄、淋巴结转移情况、神经周围侵犯、肿瘤芽、远处转移情况、P53阳性率、IGFBP7阳性率具有独立预后意义。
随后依据多因素COX比例风险模型提供的β值(回归系数),计算每位患者的预后指数(PI),用ROC曲线分析预后指数在预测患者1年、3年、5年生存情况中的准确性。ROC曲线分析结果显示PI值在预测1年生存情况时,选定cut off值为≤0.735,灵敏度为83.9%,特异度为72.0%,曲线下总面积(AUC)为0.833,95%可信区间(CI)为0.776-0.880,P=0.0001。根据cut off值0.735将病例分为两组,再分组进行ROC曲线分析。结果表明≤0.735组不再有统计学意义,而>0.735组具有统计学意义。>0.735组中选定cut off值为≤1.643,灵敏度为77.4%,特异度为72.2%,AUC为0.716,95%CI为0.569-0.835,P=0.0065。最后建立分级指标:PI(1年):1:PI≤0.735;2:0.7351.643。依据此分级标准,给所有病例分组,作单因素生存分析,结果显示PI(1年)各级之间存在统计学差异(P值均<0.0001)。比较PI(1年)和TNM分期在评判1年生存情况时的准确性,发现在评判1年生存情况时两者无统计学差异(P=0.603)。
ROC曲线分析结果显示PI值在预测3年生存情况时,选定cut off值为≤0.344,灵敏度为85.4%,特异度为67.8%,AUC为0.828,95%cI为0.772-0.876,P=0.0001。根据cut off值0.344将病例分为两组,再分组进行ROC曲线分析。结果表明≤0.344组有可疑的统计学意义,而>0.344组具有统计学意义。≤0.344组中cut off值选定为≤-0.955,灵敏度为36.3%,特异度为94.7%, AUC为0.638,95%CI为0.557-0.714,P=0.0565。>0.344组中选定cut off值为≤1.538,灵敏度为95.7%,特异度为55.0%,AUC为0.747,95%CI为0.621-0.848,P=0.0001。最后建立分级指标:PI(3年):1:PI≤-0.955;2:-0.9551.538。依据此分级标准,给所有病例分组,作单因素生存分析,结果显示PI(3年)各级之间存在统计学差异(1级vs 3级、1级vs 4级、2级vs 4级、3级vs 4级,P值均<0.0001;1级vs 2级,P=0.0011;2级vs 3级,P=0.0008)。比较PI(3年)和TNM分期在评判3年生存情况时的准确性,发现在评判3年生存情况时两者有统计学差异(P=0.001),PI(3年)较TNM分期更准确。
ROC曲线分析显示PI值在预测5年生存情况时,结果与术后三年生存情况类似,所选定的cut off值相同,因此PI(5年)分级标准与PI(3年)一致。比较PI(5年)和TNM分期在评判5年生存情况时的准确性,发现在评判5年生存情况时两者有统计学差异(P=0.003),PI(5年)较TNM分期更准确。
之后依据多因素COX比例风险模型结果提供的基准风险函数,我们对结直肠癌患者个体1年、3年、5年死亡风险预测建立数学模型。第一步:运用内插法分别计算1年(h_1)、3年(h_3)、5年(h_5)基准死亡风险函数,计算得h_1=0.0738;h_3=0.2596;h_5=0.2930。第二步:根据h_(t(x))=h_((0)t)exp(PI),分别将1年、3年、5年基准死亡风险函数代入上式,可得:
1年死亡风险函数H_(1(x))=0.0738×exp(PI)
3年死亡风险函数H_(3(x))=0.2596×exp(PI)
5年死亡风险函数H_(5(x))=0.2930×exp(PI)
以上即为最终建立的个体化时间点死亡风险预测模型,exp指以自然数e为底的指数函数,唯一可变量是PI。运用此模型可以计算每位患者的1年、3年、5年死亡风险值。还可以将此模型推广运用到各时间点的死亡风险计算。
为了对所建模型进行验证,我们将验证集中的288例患者(其中2例由于取材未能取到最前缘部位,肿瘤芽无法计数,实际计算PI值286例)依据之前的β值进行预后指数的计算,并依据之前建立的预后指数分级标准进行分级。PI在判断1年生存情况时,AUC为0.944,95%CI为0.907-0.969,P=0.0001;在判断3年生存情况时,AUC为0.954,95%CI为0.908-0.981,P=0.0001;在判断5年生存情况时,AUC为0.923,95%CI为0.848-0.968,P=0.000l。以PI(1年)为标准分级后,1级者177例,2级者45例,3级者64例。单因素生存分析表明PI(1年)分级对预后有统计学意义(P<0.0001)。以PI(3年)为标准分级后,1级者39例,2级者100例,3级者82例,4级者65例。单因素生存分析表明PI(3年)分级对预后有统计学意义(P<0.0001)。P1分级在评判1年(P=0.003)、3年(P=0.042)生存情况时,较TNM分期更准确,两者有统计学差异。
另一方面,非监督系统聚类分析被广泛运用于分子分型,为了比较采用不同指标进行聚类分析在生存预测上的差异,早在2007年,我们就利用221例结直肠癌患者,先后对13个免疫组化指标(13m,实为15个指标,CD68包括肿瘤主体和侵袭前缘,CXCR4包括阳性率和前缘增强现象的有无)、5个单因素生存分析有统计学意义的免疫组化指标(5m)、5个经典病理指标(5p,包括组织学类型、组织学分级、浸润深度、淋巴结转移情况、远处转移情况)、结合13个免疫组化指标和5个病理指标(13m5p)、结合5个免疫组化指标和5个病理指标进行了非监督系统聚类分析(5m5p)。结果表明只有5m5p聚类后(cluster5m5p)各组间存在统计学生存差异。我们根据聚类结果,将所有患者分成了三组:第1组包括130例患者,第2组67例患者,第3组24例患者。第1组和第2组之间生存有统计学差异(P<0.0001),第1组和第3组之间生存有统计学差异(P=0.0146),第2组和第3组之间生存无统计学差异(P=0.4251)。另外,5m聚类后(cluster5m)分成两组,两组间生存上有可疑的统计学意义(P=0.0532)。我们将年龄、cluster5m5p、cluster5m、TNM分期、组织学类型和组织学分级纳入多因素COX比例风险模型,分析结果显示只有5m5p聚类和TNM分期是独立的预后指标。我们将联合分子指标和病理指标进行分型称之为分子-病理分型,在预后的评估上其比分子分型更有价值。
在研究过程中,我们还对微乳头结构的意义进行了观察。微乳头结构是指无中心纤维血管束的紧密排列的肿瘤细胞团,周围被裂隙样结构包绕。微乳头结构在结直肠癌中的意义目前为止还只有两篇文献,其中均未对其生存意义进行研究。221例结直肠癌中,具有微乳头结构的30例(13.6%),无微乳头结构的191例。30例具有微乳头结构者中,微乳头结构占肿瘤面积的5%-75%。TNM分期Ⅰ期者1例(3.3%)、Ⅱ期者10例(33.3%)、Ⅲ期者18例(60.0%),Ⅳ期者1例(3.3%)。191例无微乳头结构的结直肠癌患者中56例死亡,30例伴微乳头结构的结直肠癌患者中15例死亡,两组之间存在统计学差异(P=0.0128)。多因素COX比例风险模型结果表明微乳头结构是独立的预后指标。进一步更细致的分析发现,微乳头结构的预后意义只存在于TNM分期Ⅰ-Ⅱ期患者(P<0.0001),而且具有独立预后意义;在TNM分期Ⅲ-Ⅳ期患者中,是否伴有微乳头结构在生存上并无统计学差异(P=0.7223)。伴有微乳头结构的结直肠癌组织学级别较高(P=0.0031)、较易发生脉管侵犯(P=0.0476)、神经周围侵犯(P=0.0242)、肿瘤芽(P=0.0096)、淋巴结转移(P=0.0281)和较高的TNM分期(P=0.0281)。进一步地分析表明,微乳头结构存在时,淋巴结转移发生率较高的现象只见于T1-2期,在T3-4期,两者并无相关性。
综上所述,我们可以得出以下结论:
1.在我们研究的17个临床病理指标和13个分子指标中,术后结直肠癌的独立预后因素包括:年龄、淋巴结转移情况、神经周围侵犯、肿瘤芽、远处转移情况、P53阳性率、IGFBP7阳性率;
2.依据我们所建立标准计算获得的预后指数在判断结直肠癌患者术后1年、3年、5年生存情况时,具有很高的准确性;在此基础上建立的预后指数分级制较TNM分期在预后评判上有更高的准确性;
3.最终建立的以预后指数为变量的1年、3年、5年死亡风险预测模型具有良好的准确性和适用性,实现了个体化生存预测;
4.对结直肠癌患者,我们联合分子、病理指标进行分型,实现了分子-病理分型,在预后评判上较分子分型更有价值;
5.结直肠癌中,T1-2期时,微乳头结构的存在往往伴有淋巴结转移;我们首次对微乳头结构进行生存分析,发现TNM分期为Ⅰ-Ⅱ期时,微乳头结构的存在是不利的预后指标。
Colorectal cancer is one of the common malignant cancers that severely harm the public health. In the United States of America, the mortality of colorectal cancer has ranked third among all malignant cancer. In China, the mortality is also increasing, currently ranking between the 4~(th) and the 5~(th). Up to now, the TNM staging used extensively is the gold standard for colorectal cancer prognosis. However, the patients operated on at the same TNM stage do not necessarily have the same prognosis. A perfect prognostic evaluation system can be used for guiding clinical treatments and reducing healthcare cost. A more accurately and individually prognostic evaluation model, based on histologic markers and bio-molecular markers, is anticipated.
Our laboratory has possessed a previous colorectal cancer data set with 221 patients. Surgically and pathologically verified 221 colorectal cancer cases were taken from the Xiaoshan tumor registry system during 1990-2000. Patient and treatment data were collected from patient records. Those who died within one month from surgery or died of causes other than colorectal cancer were excluded from the study. Pathological data were collected by the review on sections (hematoxyline & eosin stained, H.E.). The data set also includes the score of 4 immunohistochemical predictors( P53、Ki67、Bcl2、CXCR4 ). The survival data was provided by the Xiaoshan Center for Disease Control. All follow-up ended on Dec. 31, 2002. Using the previous data set, we investigated 9 immunohistochemical predictors and added the results into the data set, including NF-kB、Sydecan-1、β-catenin、CD68、PPARγ、IGF1R、IGFBP7、IR and Thymidylate Synthase. We also updated the survival data which ended on Mar.31, 2007. Of the 221 carcinomas, 121 were male and 100 female. The median age was 59, with a range of 26-85 years. The duration of follow-up varied from 1 month to a maximum of 203 months, with the mean of 92 months. TNM stageⅠ,Ⅱ,ⅢandⅣaccounted for 35/221, 84/221, 80/221, and 22/221, respectively. One hundred and forty-eight were low histological grade and 73 were high histological grade. One hundred and sixty-four were tubular adenocarcinoma, 26 were papillary carcinoma, 27 were mutinous adenocarcinoma, and 4 were ring-cell carcinoma or other histological type. One-year, 3-year, 5-year and 10-year cumulative survival rate was 79%, 72%, 70% and 67%, respectively. The 221 colorectal cancers were used in training the model.
To confirm the model, we investigated an addition cohort of patients as validation set. Surgically and pathologically verified 288 colorectal cancer cases were taken from the Xiaoshan tumor registry system during 2001-2006. Clinical and pathological data were collected as mentioned above. All follow-up ended on Mar. 31, 2007. The archival block of representative tumor tissues were sliced for H.E. staining and immunohistochemical staining. Among the total of 288 cases, there were 167 colon carcinoma cases and 121 rectal carcinoma cases. Of the 288 carcinomas, 153 were males and 135 were females. The mean age was 64, with a range of 24-91 years. The duration of follow-up varied from 1 month to a maximum of 75 months, with the mean of 30 months. TNM stageⅠ,Ⅱ,ⅢandⅣaccounted for 62/288, 81/288, 114/288, and 31/288, respectively. Two hundred and fifteen-five were low histological grade and 73 were high histological grade. 225 were tubular adenocarcinoma, 12 were papillary carcinoma, 25 were mucinous adenocarcinoma, and 26 were ring-cell carcinoma or other histological type. One-year, 3-year and 5-yearr cumulative survival rate was 84%, 76% and 75%, respectively. The 221 colorectal cancers were used in validating the model.
Survival analysis was performed using SPSS 16.0 for Windows. Cumulative survival rate was calculated using life-table methods. Survival curve was drawn using the univariate survival analysis based on Kaplan-Meier methods (the significance level tested by Log-rank method). The statistically significant prognostic factors identified by univariate analysis were then analyzed using multivariate Cox proportional hazard model, and a forward stepwise method was used to bring variables into the model. According to the regression coefficients determined by the final model, the prognostic index (PI) was calculated. According to the baseline function determined by the COX proportional hazard model, 1-year, 3-year and 5-year baseline hazard function were calculated by interpolation method, and the individually hazard model to predict the 1-year, 3-year and 5-year hazard was built.
MedCalc software 9.3.0.0, Frank Schoonjans was used in Receive-operationg characteristic (ROC) analysis to evaluate the accuracy of PI on predicting prognosis. Then we established the criteria in grouping PI (PI grade) according to the cut off value with the maximum Youden's index, and compared the accuracy of PI grade and TNM stage on predicting prognosis.
Hierarchical cluster analyses were performed by Cluster3.0 (Stanford University), and the results were visualized by Treeview (Stanford University). Complete linkage's method was used as the cluster method, utilizing Spearman rank correlation as interval measure. Comparison of the resultant clusters was made by x~2 test or Fisher's exact test using SPSS 13.0 statistical software (SPSS Inc, Chicago, Illinois, USA). A significant difference was identified if the P-value was less than 0.05, and a potential significance was identified when the P-value was less than 0.1.
First we performed univariate survival analysis on 17 clinico-pathological markers and 13 molecular markers in training cohort. The 17 clinico-pathological markers included age, sex, location, histological type, histological grade, the depth of infiltration, metastasis in lymph node, growth pattern, lymphovascular invasion, perineural invasion, tumor budding, tumoral-lymphocytic infiltration, peritumoral-lymphocytic infiltration, Crohn's like reaction, chemotherapy, distant metastasis, and TNM stage.The 13 molecular markers included P53, Bcl2, Ki67, NF-kB, syndecan-1, P-catenin, CD68, CXCR4, PPARγ, IGF-1R, IGFBP7, IR and thymidylate synthase. Among those markers, 18 markers were identified as the prognostic factors by univariate survival analysis, including histological type, histological grade, the depth of infiltration, metastasis in lymph node, lymphovascular invasion, perineural invasion, tumor budding, tumoral-lymphocytic infiltration, peritumoral-lymphocytic infiltration, Crohn's like reaction, chemotherapy, distant metastasis, and TNM stage, P53, the numbers of macrophages (CD68 positive cells) in the invasive margin (CD68 margin), the increased expression of CXCR4 in the invasive margin of tumor (CXCR4 margin), and IGFBP7. Growth pattern had a trend toward significance. Only those markers and age were entered into multivariate analysis. Multivariate Cox proportional hazards analysis showed that age, metastasis in lymph node, perineural invasion, tumor budding, distant metastasis, P53, and IGFBP7 were independent prognostic factors for survival.
Then PI was calculated according to the regression coefficient generated by multivariate COX proportional hazard model, and analyzed the accuracy of PI in diagnosing 1-year, 3-year, and 5-year survival status. By ROC analysis, PI in diagnosing 1-year survival status achieved area under the curve (AUC) of 0.833 (95% CI: 0.776-0.880), and the cut off value of 0.735, sensitivity of 83.9%, specificity of 72.0%, P=0.0001. The cohort was dichotomized according to the cut off value of 0.735, and then analyzed by ROC curves. There was statistical significance in the group with PI > 0.735 but not in the group with PI≤0.735. The group with PI > 0.735 achieved AUC of 0.716 (95% CI: 0.569-0.835), and the cut off value of 1.643, sensitivity of 77.4%, specificity of 72.2%, P=0.0065. The criteria of PI grade (PI (1-year)) were defined as: 1: PI≤0.735; 2: 0.735≤PI≤1.643; 3: PI > 1.643. The 221 cases were grouped to 3 grade followed this criteria. By univariate survival analysis, there was significant difference between each PI grade (P < 0.0001). By ROC curves analysis, there was no significant difference on accuracy in predicting 1-year survival status between PI (1-year) and TNM stage (P=0.603).
By ROC analysis, PI in diagnosing 3-year survival status achieved area under the curve (AUC) of 0.828 (95% CI: 0.772-0.876), and the cut off value of 0.344, sensitivity of 85.4%, specificity of 67.8%, P=0.0001. The cohort was dichotomized according to the cut off value of 0.344, and then analyzed by ROC curves. There was a tendency to statistical significance in the group with PI <0.344, and definite statistical significance in the group with PI> 0.344. The group with PI≤0.344 achieved AUC of 0.638 (95% CI: 0.557-0.714), and the cut off value of -0.955, sensitivity of 36.3%, specificity of 94.7%, P=0.0565. The group with PI > 0.344 achieved AUC of 0.747 (95% CI: 0.621-0.848), and the cut off value of 1.538, sensitivity of 95.7%, specificity of 55.0%, P=0.0001. The criteria of PI grade (PI (3-year)) were defined as: 1: PI≤-0.955; 2: -0.955 < PI≤0.344; 3: 0.344 < PI≤1.538; 4: PI> 1.538. The 221 cases were grouped to 4 grade followed this criteria. By univariate survival analysis, there was significant difference between each PI grade (grade 1 vs 3, 1 vs 4, 2 vs 4, 3 vs 4, P < 0.0001; 1 vs 2, P=0.0011; 2vs 3, P=0.0008). By ROC curves analysis, there was significant difference on AUC in predicting 3-year survival status between PI (3-year) and TNM stage (P=0.001). PI (3-year) had higher accuracy.
The result of PI in diagnosing 5-year survival status was similar to PI in diagnosing 3-year survival status, and cut off value of PI (5-year) was equal to PI (3-year). By ROC curves analysis, there was significant difference on AUC in predicting 5-year survival status between PI (5-year) and TNM stage (P=0.003). PI (5-year) had higher accuracy.
Next, the individual hazard model to predict the 1-year, 3-year and 5-year hazard was built according to the baseline function determined by the COX proportional hazard model. First, 1-year (h_1), 3-year (h_3), and 5-year (h_5) baseline hazard function were calculated by interpolation method. The results showed h_1=0.0738, h_3=0.2596, and h_5=0.2930. Second, according the formula: h_(t(x))=h_((0)t)texp(PI), the 1-year, 3-year, and 5-year hazard model were established as:PI was the only variable in the model. Each patient's hazard probability at the end of 1-year, 3-year and 5-year could be predicted according to the hazard model.
In order to validate the built model, we calculated the PI in the validation cohort. Because there were 2 cases with missed tumor budding, only 286 cases were analyzed in model validation. Furthermore, we grouped the patients followed by the criteria of PI grade. PI in diagnosing 1-year survival status achieved AUC of 0.944 (95% CI: 0.907-0.969), and P=0.0001. PI in diagnosing 3-year survival status achieved AUC of 0.954 (95% CI: 0.908-0.981), and P=0.0001. PI in diagnosing 5-year survival status achieved AUC of 0.923 (95% CI: 0.848-0.968), and P=0.0001. After grouped with PI (1-year), 177 were grade 1, 45 were grade 2, and 64 were grade 3. PI (1-year) was a prognostic factor (P< 0.0001) by univariate survival analysis. After grouped with PI (3-year), 39 were grade 1, 100 were grade 2, 82 were grade 3, and 65 were grade 4. PI (3-year) was a prognostic factor (P < 0.0001) by univariate survival analysis. By ROC curves analysis, there was significant difference on AUC in predicting 1-year and 3-year survival status between PI (1-year) and TNM stage (P=0.003), PI (3-year) and TNM stage (P=0.042), respectively. PI (1-year) and PI (3-year) had higher accuracy.
Moreover, unsupervised hierarchical cluster analysis was often used to classify molecular markers. However, classification based on combination of molecular and pathological predictors had never been performed using hierarchical cluster analysis. For this purpose, a total of 6 pathological predictors (p) and 13 immunohistochemical predictors (m) were investigated in 221 colorectal cancers. In 2007, we identified prognostic classification based on 13m, 5m (predictors with statistical significance by univariate survival analysis), 5p (including histological type, histological grade, the depth of infiltration, metastasis in lymph node, and distant metastasis), 13m5p, and 5m5p by unsupervised hierarchical cluster analysis. By univariate survival analysis, only classification based on 5m5p (cluster5m5p) had statistical significant on prognosis. Three groups were produced: group 1 including 130 cases, group 2 including 67 cases, and group 3 including 24 cases. By univariate survival analysis, there were significant difference when group 1 vs 2 (P <0.0001), and group 1 vs 3 (P=0.0146), and there was no significant difference between group 2 and 3 (P=0.4251). In addition, there was a tendency to significant difference between the two groups produced by classification based on 5m (cluster5m) (P=0.0532). When age, cluster5m5p, cluster5m, TNM stage, histological type, and histological grade were entered into multivariate COX proportional hazard model, the results showed that cluster5m5p and TNM stage were independent prognostic factors. We defined classification based on molecular and pathological predictors as molecular-pathological classification, which is superior to that based only on molecular predictors on prognosis.
Micropapillary structure is identified as tight neoplastic cell tufts which lack central fibrovascular cores and are surrounded by cleft-like spaces. Up to now, there are two reports in colorectal cancer with micropapillary component (MP), and survival analysis has never been investigated. Thirty colorectal carcinomas with a micropapillary component were identified from the series of 221 colorectal carcinomas. Of these 30 carcinomas with MP, the MP ranged from 5 to 75% of the tumor area in histological sections. TNM stageⅠ,Ⅱ,ⅢandⅣaccounted for 3.3% (n=1), 33.3% (n=10), 60.0% (n=18), and 3.3% (n=1), respectively. Among the 221 patients, 56 cases without MP, and 15 cases with MP died of disease by the end of follow-up. Carcinomas with MP had a worse prognosis compared with those without MP (P=0.0128). MP was an independent predictor identified by multivariate COX proportional hazard model. Furthermore, survival analysis stratified by TNM stage showed MP was a prognostic factor in TNM stageⅠ-Ⅱ(P < 0.0001) not in TNM stageⅢ-Ⅳ(P=0.7223). In TNM stageⅠ-Ⅱ, MP also was an independent prognostic factor. Carcinoma with MP compared with those without MP revealed a higher percentage of high-grade tumors (P=0.0031) and higher levels of lymphovascular invasion (P=0.0476), perineural invasion (P=0.0242), positive tumor budding (P=0.0096), positive lymph node metastasis (P=0.0281) and TNM stageⅢ-Ⅳ(P=0.0281). However, the results stratified by T stage indicated that the presence of MP predicted more frequent positive lymph node metastasis than the absence of MP only in T1-2 stage.
Based on above results, we drew the following conclusions:
1. Age, metastasis in lymph node, perineural invasion, tumor budding, distant metastasis, P53, and IGFBP7 are independent prognostic factors for survival in colorectal cancer.
2. Prognostic index have robust performance in predicting 1-year, 3-year, and 5-year survival status. The PI grade based on prognostic index is more accurate than TNM stage in evaluation on prognosis.
3. The hazard model based on prognostic index could be used to predict individual hazard at the end of 1-year, 3-year, and 5-year, with good repeatability.
4. Classification based on pathological and immunohistochemical predictors is superior to that based only on molecular predictors on prognosis, though they both have prognostic significance in colorectal cancer.
5. The presence of a micropapillary component predicts more frequent lymph node metastasis in T1-2 stage and worse prognosis in TNM stageⅠ-Ⅱ.
之前,我们实验室已建立有221例结直肠癌患者的基本信息数据库。这221例患者来自萧山肿瘤登记系统中1990-2000年期间发病的患者,通过复习患者病案和H.E.切片,调查收集了比较全面的临床信息和病理信息,并已包含4个分子指标的免疫组化结果(P53、Ki67、Bcl2、CXCR4)。该数据库中已剔除术后一个月内死亡的病例和死因与结直肠癌无关的病例。所有随访信息由萧山肿瘤登记系统提供,原有数据库中的随访信息截止于2002年12月31日。我们在原有数据库的基础上,又增加了9个分子指标的免疫组化结果(NF-κB、Sydecan-1、β-catenin、CD68、PPARγ、IGF1R、IGFBP7、IR、Thymidylate Synthase),并对随访信息进行了更新,所有随访信息更新至2007年3月31日。221例患者中结肠癌71例,直肠癌151例;男性121例、女性100例;确诊时年龄最大者85岁,最小者26岁,中位年龄为59岁;随访时间最长203个月,最短1个月,平均随访时间为92个月。TNMⅠ期35例、Ⅱ期84例、Ⅲ期80例、Ⅳ期22例;低级别癌148例、高级别癌73例;管状腺癌164例、乳头状癌26例、粘液腺癌27例、印戒细胞癌和未分化癌4例。总体1年、3年、5年、10年累积生存率分别为79%、72%、70%和67%。此221例结直肠癌患者在预后数学建模过程中被作为训练集。
另外,我们又从萧山肿瘤登记系统中,选取2001-2006年期间发病的患者,进行档案资料的调查,最终获得手术医院为萧山第一人民医院的288名档案资料完整的结直肠癌患者。复习所有病案,获取临床病理资料,同时剔除术后一个月内死亡的病例,并剔除死因与结直肠癌无关的病例。所有随访信息截止于2007年3月31日。全部288例病例均经病理证实,每例选择有代表性的含肿瘤组织蜡块进行切片,行H.E.染色和免疫组织化学染色。288例患者中结肠癌167例、直肠癌121例;男性153例、女性135例;确诊时年龄最大者91岁,最小者24岁,平均年龄为64岁;随访时间最长75个月,最短1个月,平均随访时间为30个月。TNMⅠ期62例、Ⅱ期81例、Ⅲ期114例、Ⅳ期31例;低级别癌215例、高级别癌73例;管状腺癌225例、乳头状癌12例、粘液腺癌25例、印戒细胞癌和其它26例。总体1年、3年、5年累积生存率分别为84%、76%、75%。此288例结直肠癌患者在预后数学建模过程中被作为验证集。
采用SPSS 16.0 for windows统计学软件(SPSS Inc.)进行以下统计学处理。用寿命表法计算累积生存率;用Kaplan-Meier法进行单因素生存分析(用log-rank法进行显著性检验),并绘制生存曲线。将单因素分析对预后有统计学意义的因素再纳入多因素COX比例风险模型进行分析。多因素COX比例风险模型采用向前逐步回归法。依据多因素COX比例风险模型结果计算每个病例的预后指数,并使用内插法计算1年、3年、5年基准死亡风险函数。从而建立基于预后指数的1年、3年、5年个体死亡风险函数的数学预测模型。
采用MedCalc software 9.3.0.0(Frank Schoonjans)软件,运用ROC曲线评价预后指数在生存评判中的价值,并依据Youden指数最大的原则确定cut off值,进行预后指数分级,比较预后指数分级与传统TNM分期的预后评判价值。
软件Cluster3.0(Stanford University)用于非监督系统聚类分析,采用spearman等级相关作为聚类统计量,类间归类方法采用最长距离法,结果用Treeview(Stanford University)显示。组间构成比比较采用χ~2检验或Fisher's精确检验。P值≤0.05认为有统计学意义,0.05
随后依据多因素COX比例风险模型提供的β值(回归系数),计算每位患者的预后指数(PI),用ROC曲线分析预后指数在预测患者1年、3年、5年生存情况中的准确性。ROC曲线分析结果显示PI值在预测1年生存情况时,选定cut off值为≤0.735,灵敏度为83.9%,特异度为72.0%,曲线下总面积(AUC)为0.833,95%可信区间(CI)为0.776-0.880,P=0.0001。根据cut off值0.735将病例分为两组,再分组进行ROC曲线分析。结果表明≤0.735组不再有统计学意义,而>0.735组具有统计学意义。>0.735组中选定cut off值为≤1.643,灵敏度为77.4%,特异度为72.2%,AUC为0.716,95%CI为0.569-0.835,P=0.0065。最后建立分级指标:PI(1年):1:PI≤0.735;2:0.735
ROC曲线分析结果显示PI值在预测3年生存情况时,选定cut off值为≤0.344,灵敏度为85.4%,特异度为67.8%,AUC为0.828,95%cI为0.772-0.876,P=0.0001。根据cut off值0.344将病例分为两组,再分组进行ROC曲线分析。结果表明≤0.344组有可疑的统计学意义,而>0.344组具有统计学意义。≤0.344组中cut off值选定为≤-0.955,灵敏度为36.3%,特异度为94.7%, AUC为0.638,95%CI为0.557-0.714,P=0.0565。>0.344组中选定cut off值为≤1.538,灵敏度为95.7%,特异度为55.0%,AUC为0.747,95%CI为0.621-0.848,P=0.0001。最后建立分级指标:PI(3年):1:PI≤-0.955;2:-0.955
ROC曲线分析显示PI值在预测5年生存情况时,结果与术后三年生存情况类似,所选定的cut off值相同,因此PI(5年)分级标准与PI(3年)一致。比较PI(5年)和TNM分期在评判5年生存情况时的准确性,发现在评判5年生存情况时两者有统计学差异(P=0.003),PI(5年)较TNM分期更准确。
之后依据多因素COX比例风险模型结果提供的基准风险函数,我们对结直肠癌患者个体1年、3年、5年死亡风险预测建立数学模型。第一步:运用内插法分别计算1年(h_1)、3年(h_3)、5年(h_5)基准死亡风险函数,计算得h_1=0.0738;h_3=0.2596;h_5=0.2930。第二步:根据h_(t(x))=h_((0)t)exp(PI),分别将1年、3年、5年基准死亡风险函数代入上式,可得:
1年死亡风险函数H_(1(x))=0.0738×exp(PI)
3年死亡风险函数H_(3(x))=0.2596×exp(PI)
5年死亡风险函数H_(5(x))=0.2930×exp(PI)
以上即为最终建立的个体化时间点死亡风险预测模型,exp指以自然数e为底的指数函数,唯一可变量是PI。运用此模型可以计算每位患者的1年、3年、5年死亡风险值。还可以将此模型推广运用到各时间点的死亡风险计算。
为了对所建模型进行验证,我们将验证集中的288例患者(其中2例由于取材未能取到最前缘部位,肿瘤芽无法计数,实际计算PI值286例)依据之前的β值进行预后指数的计算,并依据之前建立的预后指数分级标准进行分级。PI在判断1年生存情况时,AUC为0.944,95%CI为0.907-0.969,P=0.0001;在判断3年生存情况时,AUC为0.954,95%CI为0.908-0.981,P=0.0001;在判断5年生存情况时,AUC为0.923,95%CI为0.848-0.968,P=0.000l。以PI(1年)为标准分级后,1级者177例,2级者45例,3级者64例。单因素生存分析表明PI(1年)分级对预后有统计学意义(P<0.0001)。以PI(3年)为标准分级后,1级者39例,2级者100例,3级者82例,4级者65例。单因素生存分析表明PI(3年)分级对预后有统计学意义(P<0.0001)。P1分级在评判1年(P=0.003)、3年(P=0.042)生存情况时,较TNM分期更准确,两者有统计学差异。
另一方面,非监督系统聚类分析被广泛运用于分子分型,为了比较采用不同指标进行聚类分析在生存预测上的差异,早在2007年,我们就利用221例结直肠癌患者,先后对13个免疫组化指标(13m,实为15个指标,CD68包括肿瘤主体和侵袭前缘,CXCR4包括阳性率和前缘增强现象的有无)、5个单因素生存分析有统计学意义的免疫组化指标(5m)、5个经典病理指标(5p,包括组织学类型、组织学分级、浸润深度、淋巴结转移情况、远处转移情况)、结合13个免疫组化指标和5个病理指标(13m5p)、结合5个免疫组化指标和5个病理指标进行了非监督系统聚类分析(5m5p)。结果表明只有5m5p聚类后(cluster5m5p)各组间存在统计学生存差异。我们根据聚类结果,将所有患者分成了三组:第1组包括130例患者,第2组67例患者,第3组24例患者。第1组和第2组之间生存有统计学差异(P<0.0001),第1组和第3组之间生存有统计学差异(P=0.0146),第2组和第3组之间生存无统计学差异(P=0.4251)。另外,5m聚类后(cluster5m)分成两组,两组间生存上有可疑的统计学意义(P=0.0532)。我们将年龄、cluster5m5p、cluster5m、TNM分期、组织学类型和组织学分级纳入多因素COX比例风险模型,分析结果显示只有5m5p聚类和TNM分期是独立的预后指标。我们将联合分子指标和病理指标进行分型称之为分子-病理分型,在预后的评估上其比分子分型更有价值。
在研究过程中,我们还对微乳头结构的意义进行了观察。微乳头结构是指无中心纤维血管束的紧密排列的肿瘤细胞团,周围被裂隙样结构包绕。微乳头结构在结直肠癌中的意义目前为止还只有两篇文献,其中均未对其生存意义进行研究。221例结直肠癌中,具有微乳头结构的30例(13.6%),无微乳头结构的191例。30例具有微乳头结构者中,微乳头结构占肿瘤面积的5%-75%。TNM分期Ⅰ期者1例(3.3%)、Ⅱ期者10例(33.3%)、Ⅲ期者18例(60.0%),Ⅳ期者1例(3.3%)。191例无微乳头结构的结直肠癌患者中56例死亡,30例伴微乳头结构的结直肠癌患者中15例死亡,两组之间存在统计学差异(P=0.0128)。多因素COX比例风险模型结果表明微乳头结构是独立的预后指标。进一步更细致的分析发现,微乳头结构的预后意义只存在于TNM分期Ⅰ-Ⅱ期患者(P<0.0001),而且具有独立预后意义;在TNM分期Ⅲ-Ⅳ期患者中,是否伴有微乳头结构在生存上并无统计学差异(P=0.7223)。伴有微乳头结构的结直肠癌组织学级别较高(P=0.0031)、较易发生脉管侵犯(P=0.0476)、神经周围侵犯(P=0.0242)、肿瘤芽(P=0.0096)、淋巴结转移(P=0.0281)和较高的TNM分期(P=0.0281)。进一步地分析表明,微乳头结构存在时,淋巴结转移发生率较高的现象只见于T1-2期,在T3-4期,两者并无相关性。
综上所述,我们可以得出以下结论:
1.在我们研究的17个临床病理指标和13个分子指标中,术后结直肠癌的独立预后因素包括:年龄、淋巴结转移情况、神经周围侵犯、肿瘤芽、远处转移情况、P53阳性率、IGFBP7阳性率;
2.依据我们所建立标准计算获得的预后指数在判断结直肠癌患者术后1年、3年、5年生存情况时,具有很高的准确性;在此基础上建立的预后指数分级制较TNM分期在预后评判上有更高的准确性;
3.最终建立的以预后指数为变量的1年、3年、5年死亡风险预测模型具有良好的准确性和适用性,实现了个体化生存预测;
4.对结直肠癌患者,我们联合分子、病理指标进行分型,实现了分子-病理分型,在预后评判上较分子分型更有价值;
5.结直肠癌中,T1-2期时,微乳头结构的存在往往伴有淋巴结转移;我们首次对微乳头结构进行生存分析,发现TNM分期为Ⅰ-Ⅱ期时,微乳头结构的存在是不利的预后指标。
Colorectal cancer is one of the common malignant cancers that severely harm the public health. In the United States of America, the mortality of colorectal cancer has ranked third among all malignant cancer. In China, the mortality is also increasing, currently ranking between the 4~(th) and the 5~(th). Up to now, the TNM staging used extensively is the gold standard for colorectal cancer prognosis. However, the patients operated on at the same TNM stage do not necessarily have the same prognosis. A perfect prognostic evaluation system can be used for guiding clinical treatments and reducing healthcare cost. A more accurately and individually prognostic evaluation model, based on histologic markers and bio-molecular markers, is anticipated.
Our laboratory has possessed a previous colorectal cancer data set with 221 patients. Surgically and pathologically verified 221 colorectal cancer cases were taken from the Xiaoshan tumor registry system during 1990-2000. Patient and treatment data were collected from patient records. Those who died within one month from surgery or died of causes other than colorectal cancer were excluded from the study. Pathological data were collected by the review on sections (hematoxyline & eosin stained, H.E.). The data set also includes the score of 4 immunohistochemical predictors( P53、Ki67、Bcl2、CXCR4 ). The survival data was provided by the Xiaoshan Center for Disease Control. All follow-up ended on Dec. 31, 2002. Using the previous data set, we investigated 9 immunohistochemical predictors and added the results into the data set, including NF-kB、Sydecan-1、β-catenin、CD68、PPARγ、IGF1R、IGFBP7、IR and Thymidylate Synthase. We also updated the survival data which ended on Mar.31, 2007. Of the 221 carcinomas, 121 were male and 100 female. The median age was 59, with a range of 26-85 years. The duration of follow-up varied from 1 month to a maximum of 203 months, with the mean of 92 months. TNM stageⅠ,Ⅱ,ⅢandⅣaccounted for 35/221, 84/221, 80/221, and 22/221, respectively. One hundred and forty-eight were low histological grade and 73 were high histological grade. One hundred and sixty-four were tubular adenocarcinoma, 26 were papillary carcinoma, 27 were mutinous adenocarcinoma, and 4 were ring-cell carcinoma or other histological type. One-year, 3-year, 5-year and 10-year cumulative survival rate was 79%, 72%, 70% and 67%, respectively. The 221 colorectal cancers were used in training the model.
To confirm the model, we investigated an addition cohort of patients as validation set. Surgically and pathologically verified 288 colorectal cancer cases were taken from the Xiaoshan tumor registry system during 2001-2006. Clinical and pathological data were collected as mentioned above. All follow-up ended on Mar. 31, 2007. The archival block of representative tumor tissues were sliced for H.E. staining and immunohistochemical staining. Among the total of 288 cases, there were 167 colon carcinoma cases and 121 rectal carcinoma cases. Of the 288 carcinomas, 153 were males and 135 were females. The mean age was 64, with a range of 24-91 years. The duration of follow-up varied from 1 month to a maximum of 75 months, with the mean of 30 months. TNM stageⅠ,Ⅱ,ⅢandⅣaccounted for 62/288, 81/288, 114/288, and 31/288, respectively. Two hundred and fifteen-five were low histological grade and 73 were high histological grade. 225 were tubular adenocarcinoma, 12 were papillary carcinoma, 25 were mucinous adenocarcinoma, and 26 were ring-cell carcinoma or other histological type. One-year, 3-year and 5-yearr cumulative survival rate was 84%, 76% and 75%, respectively. The 221 colorectal cancers were used in validating the model.
Survival analysis was performed using SPSS 16.0 for Windows. Cumulative survival rate was calculated using life-table methods. Survival curve was drawn using the univariate survival analysis based on Kaplan-Meier methods (the significance level tested by Log-rank method). The statistically significant prognostic factors identified by univariate analysis were then analyzed using multivariate Cox proportional hazard model, and a forward stepwise method was used to bring variables into the model. According to the regression coefficients determined by the final model, the prognostic index (PI) was calculated. According to the baseline function determined by the COX proportional hazard model, 1-year, 3-year and 5-year baseline hazard function were calculated by interpolation method, and the individually hazard model to predict the 1-year, 3-year and 5-year hazard was built.
MedCalc software 9.3.0.0, Frank Schoonjans was used in Receive-operationg characteristic (ROC) analysis to evaluate the accuracy of PI on predicting prognosis. Then we established the criteria in grouping PI (PI grade) according to the cut off value with the maximum Youden's index, and compared the accuracy of PI grade and TNM stage on predicting prognosis.
Hierarchical cluster analyses were performed by Cluster3.0 (Stanford University), and the results were visualized by Treeview (Stanford University). Complete linkage's method was used as the cluster method, utilizing Spearman rank correlation as interval measure. Comparison of the resultant clusters was made by x~2 test or Fisher's exact test using SPSS 13.0 statistical software (SPSS Inc, Chicago, Illinois, USA). A significant difference was identified if the P-value was less than 0.05, and a potential significance was identified when the P-value was less than 0.1.
First we performed univariate survival analysis on 17 clinico-pathological markers and 13 molecular markers in training cohort. The 17 clinico-pathological markers included age, sex, location, histological type, histological grade, the depth of infiltration, metastasis in lymph node, growth pattern, lymphovascular invasion, perineural invasion, tumor budding, tumoral-lymphocytic infiltration, peritumoral-lymphocytic infiltration, Crohn's like reaction, chemotherapy, distant metastasis, and TNM stage.The 13 molecular markers included P53, Bcl2, Ki67, NF-kB, syndecan-1, P-catenin, CD68, CXCR4, PPARγ, IGF-1R, IGFBP7, IR and thymidylate synthase. Among those markers, 18 markers were identified as the prognostic factors by univariate survival analysis, including histological type, histological grade, the depth of infiltration, metastasis in lymph node, lymphovascular invasion, perineural invasion, tumor budding, tumoral-lymphocytic infiltration, peritumoral-lymphocytic infiltration, Crohn's like reaction, chemotherapy, distant metastasis, and TNM stage, P53, the numbers of macrophages (CD68 positive cells) in the invasive margin (CD68 margin), the increased expression of CXCR4 in the invasive margin of tumor (CXCR4 margin), and IGFBP7. Growth pattern had a trend toward significance. Only those markers and age were entered into multivariate analysis. Multivariate Cox proportional hazards analysis showed that age, metastasis in lymph node, perineural invasion, tumor budding, distant metastasis, P53, and IGFBP7 were independent prognostic factors for survival.
Then PI was calculated according to the regression coefficient generated by multivariate COX proportional hazard model, and analyzed the accuracy of PI in diagnosing 1-year, 3-year, and 5-year survival status. By ROC analysis, PI in diagnosing 1-year survival status achieved area under the curve (AUC) of 0.833 (95% CI: 0.776-0.880), and the cut off value of 0.735, sensitivity of 83.9%, specificity of 72.0%, P=0.0001. The cohort was dichotomized according to the cut off value of 0.735, and then analyzed by ROC curves. There was statistical significance in the group with PI > 0.735 but not in the group with PI≤0.735. The group with PI > 0.735 achieved AUC of 0.716 (95% CI: 0.569-0.835), and the cut off value of 1.643, sensitivity of 77.4%, specificity of 72.2%, P=0.0065. The criteria of PI grade (PI (1-year)) were defined as: 1: PI≤0.735; 2: 0.735≤PI≤1.643; 3: PI > 1.643. The 221 cases were grouped to 3 grade followed this criteria. By univariate survival analysis, there was significant difference between each PI grade (P < 0.0001). By ROC curves analysis, there was no significant difference on accuracy in predicting 1-year survival status between PI (1-year) and TNM stage (P=0.603).
By ROC analysis, PI in diagnosing 3-year survival status achieved area under the curve (AUC) of 0.828 (95% CI: 0.772-0.876), and the cut off value of 0.344, sensitivity of 85.4%, specificity of 67.8%, P=0.0001. The cohort was dichotomized according to the cut off value of 0.344, and then analyzed by ROC curves. There was a tendency to statistical significance in the group with PI <0.344, and definite statistical significance in the group with PI> 0.344. The group with PI≤0.344 achieved AUC of 0.638 (95% CI: 0.557-0.714), and the cut off value of -0.955, sensitivity of 36.3%, specificity of 94.7%, P=0.0565. The group with PI > 0.344 achieved AUC of 0.747 (95% CI: 0.621-0.848), and the cut off value of 1.538, sensitivity of 95.7%, specificity of 55.0%, P=0.0001. The criteria of PI grade (PI (3-year)) were defined as: 1: PI≤-0.955; 2: -0.955 < PI≤0.344; 3: 0.344 < PI≤1.538; 4: PI> 1.538. The 221 cases were grouped to 4 grade followed this criteria. By univariate survival analysis, there was significant difference between each PI grade (grade 1 vs 3, 1 vs 4, 2 vs 4, 3 vs 4, P < 0.0001; 1 vs 2, P=0.0011; 2vs 3, P=0.0008). By ROC curves analysis, there was significant difference on AUC in predicting 3-year survival status between PI (3-year) and TNM stage (P=0.001). PI (3-year) had higher accuracy.
The result of PI in diagnosing 5-year survival status was similar to PI in diagnosing 3-year survival status, and cut off value of PI (5-year) was equal to PI (3-year). By ROC curves analysis, there was significant difference on AUC in predicting 5-year survival status between PI (5-year) and TNM stage (P=0.003). PI (5-year) had higher accuracy.
Next, the individual hazard model to predict the 1-year, 3-year and 5-year hazard was built according to the baseline function determined by the COX proportional hazard model. First, 1-year (h_1), 3-year (h_3), and 5-year (h_5) baseline hazard function were calculated by interpolation method. The results showed h_1=0.0738, h_3=0.2596, and h_5=0.2930. Second, according the formula: h_(t(x))=h_((0)t)texp(PI), the 1-year, 3-year, and 5-year hazard model were established as:PI was the only variable in the model. Each patient's hazard probability at the end of 1-year, 3-year and 5-year could be predicted according to the hazard model.
In order to validate the built model, we calculated the PI in the validation cohort. Because there were 2 cases with missed tumor budding, only 286 cases were analyzed in model validation. Furthermore, we grouped the patients followed by the criteria of PI grade. PI in diagnosing 1-year survival status achieved AUC of 0.944 (95% CI: 0.907-0.969), and P=0.0001. PI in diagnosing 3-year survival status achieved AUC of 0.954 (95% CI: 0.908-0.981), and P=0.0001. PI in diagnosing 5-year survival status achieved AUC of 0.923 (95% CI: 0.848-0.968), and P=0.0001. After grouped with PI (1-year), 177 were grade 1, 45 were grade 2, and 64 were grade 3. PI (1-year) was a prognostic factor (P< 0.0001) by univariate survival analysis. After grouped with PI (3-year), 39 were grade 1, 100 were grade 2, 82 were grade 3, and 65 were grade 4. PI (3-year) was a prognostic factor (P < 0.0001) by univariate survival analysis. By ROC curves analysis, there was significant difference on AUC in predicting 1-year and 3-year survival status between PI (1-year) and TNM stage (P=0.003), PI (3-year) and TNM stage (P=0.042), respectively. PI (1-year) and PI (3-year) had higher accuracy.
Moreover, unsupervised hierarchical cluster analysis was often used to classify molecular markers. However, classification based on combination of molecular and pathological predictors had never been performed using hierarchical cluster analysis. For this purpose, a total of 6 pathological predictors (p) and 13 immunohistochemical predictors (m) were investigated in 221 colorectal cancers. In 2007, we identified prognostic classification based on 13m, 5m (predictors with statistical significance by univariate survival analysis), 5p (including histological type, histological grade, the depth of infiltration, metastasis in lymph node, and distant metastasis), 13m5p, and 5m5p by unsupervised hierarchical cluster analysis. By univariate survival analysis, only classification based on 5m5p (cluster5m5p) had statistical significant on prognosis. Three groups were produced: group 1 including 130 cases, group 2 including 67 cases, and group 3 including 24 cases. By univariate survival analysis, there were significant difference when group 1 vs 2 (P <0.0001), and group 1 vs 3 (P=0.0146), and there was no significant difference between group 2 and 3 (P=0.4251). In addition, there was a tendency to significant difference between the two groups produced by classification based on 5m (cluster5m) (P=0.0532). When age, cluster5m5p, cluster5m, TNM stage, histological type, and histological grade were entered into multivariate COX proportional hazard model, the results showed that cluster5m5p and TNM stage were independent prognostic factors. We defined classification based on molecular and pathological predictors as molecular-pathological classification, which is superior to that based only on molecular predictors on prognosis.
Micropapillary structure is identified as tight neoplastic cell tufts which lack central fibrovascular cores and are surrounded by cleft-like spaces. Up to now, there are two reports in colorectal cancer with micropapillary component (MP), and survival analysis has never been investigated. Thirty colorectal carcinomas with a micropapillary component were identified from the series of 221 colorectal carcinomas. Of these 30 carcinomas with MP, the MP ranged from 5 to 75% of the tumor area in histological sections. TNM stageⅠ,Ⅱ,ⅢandⅣaccounted for 3.3% (n=1), 33.3% (n=10), 60.0% (n=18), and 3.3% (n=1), respectively. Among the 221 patients, 56 cases without MP, and 15 cases with MP died of disease by the end of follow-up. Carcinomas with MP had a worse prognosis compared with those without MP (P=0.0128). MP was an independent predictor identified by multivariate COX proportional hazard model. Furthermore, survival analysis stratified by TNM stage showed MP was a prognostic factor in TNM stageⅠ-Ⅱ(P < 0.0001) not in TNM stageⅢ-Ⅳ(P=0.7223). In TNM stageⅠ-Ⅱ, MP also was an independent prognostic factor. Carcinoma with MP compared with those without MP revealed a higher percentage of high-grade tumors (P=0.0031) and higher levels of lymphovascular invasion (P=0.0476), perineural invasion (P=0.0242), positive tumor budding (P=0.0096), positive lymph node metastasis (P=0.0281) and TNM stageⅢ-Ⅳ(P=0.0281). However, the results stratified by T stage indicated that the presence of MP predicted more frequent positive lymph node metastasis than the absence of MP only in T1-2 stage.
Based on above results, we drew the following conclusions:
1. Age, metastasis in lymph node, perineural invasion, tumor budding, distant metastasis, P53, and IGFBP7 are independent prognostic factors for survival in colorectal cancer.
2. Prognostic index have robust performance in predicting 1-year, 3-year, and 5-year survival status. The PI grade based on prognostic index is more accurate than TNM stage in evaluation on prognosis.
3. The hazard model based on prognostic index could be used to predict individual hazard at the end of 1-year, 3-year, and 5-year, with good repeatability.
4. Classification based on pathological and immunohistochemical predictors is superior to that based only on molecular predictors on prognosis, though they both have prognostic significance in colorectal cancer.
5. The presence of a micropapillary component predicts more frequent lymph node metastasis in T1-2 stage and worse prognosis in TNM stageⅠ-Ⅱ.
引文
[1]Jemal A,Siegel R,Ward E,et al.Cancer statistics,2008.CA Cancer J Clin 2008;58:71-96.
[2]Wang LM,Kevans D,Mulcahy H,et al.Tumor budding is a strong and reproducible prognostic marker in T3N0 colorectal cancer.Am J Surg Pathol 2009;33:134-41.
[3]Compton CC,Greene FL.The staging of colorectal cancer:2004 and beyond.CA Cancer J Clin 2004;54:295-308.
[4]Singletary SE,Greene FL,Sobin LH.Classification of isolated tumor cells:clarification of the 6th edition of the American Joint Committee on Cancer Staging Manual.Cancer 2003;98:2740-1.
[5]Reed J,Rosman M,Verbanac KM,Mannie A,Cheng Z,Tafra L.Prognostic implications of isolated tumor cells and micrometastases in sentinel nodes of patients with invasive breast cancer:10-year analysis of patients enrolled in the prospective East Carolina University/Anne Arundel Medical Center Sentinel Node Multicenter Study.J Am Coll Surg 2009;208:333-40.
[6]Fukagawa T,Sasako M,Shimoda T,et al.The prognostic impact of isolated tumor cells in lymph nodes of T2N0 gastric cancer:comparison of American and Japanese gastric cancer patients.Ann Surg Oncol 2009;16:609-13.
[7]Yonemura Y,Endo Y,Hayashi I,Kawamura T,Yun HY,Bandou E.Proliferative activity of micrometastases in the lymph nodes of patients with gastric cancer.Br J Surg 2007;94:731-6.
[8]Hase K,Shatney C,Johnson D,Trollope M,Vierra M.Prognostic value of tumor "budding" in patients with colorectal cancer.Dis Colon Rectum 1993;36:627-35.
[9]Morodomi T,Isomoto H,Shirouzu K,Kakegawa K,Irie K,Morimatsu M.An index for estimating the probability of lymph node metastasis in rectal cancers.Lymph node metastasis and the histopathology of actively invasive regions of cancer.Cancer 1989;63:539-43.
[10]Kaihara T,Kusaka T,Nishi M,et al.Dedifferentiation and decreased expression of adhesion molecules,E-cadherin and ZO-1,in colorectal cancer are closely related to liver metastasis.J Exp Clin Cancer Res 2003;22:117-23.
[11]Guzinska-Ustymowicz K.MMP-9 and cathepsin B expression in tumor budding as an indicator of a more aggressive phenotype of colorectal cancer (CRC).Anticancer Res 2006;26:1589-94.
[12]Haupt B,Ro JY,Schwartz MR,Shen SS.Colorectal adenocarcinoma with micropapillary pattern and its association with lymph node metastasis.Mod Pathol 2007;20:729-33.
[13]Nassar H,Wallis T,Andea A,Dey J,Adsay V,Visscher D.Clinicopathologic analysis of invasive micropapillary differentiation in breast carcinoma.Mod Pathol 2001;14:836-41.
[14]Prat J,De Nictolis M.Serous borderline tumors of the ovary:a long-term follow-up study of 137 cases,including 18 with a micropapillary pattern and 20 with microinvasion.Am J Surg Pathol 2002;26:1111-28.
[15]Amin MB,Tamboli P,Merchant SH,et al.Micropapillary component in lung adenocarcinoma:a distinctive histologic feature with possible prognostic significance.Am J Surg Pathol 2002;26:358-64.
[16]Johansson SL,Borghede G,Holmang S.Micropapillary bladder carcinoma:a clinicopathological study of 20 cases.J Urol 1999;161:1798-802.
[17]Kim MJ,Hong SM,Jang SJ,et al.Invasive colorectal micropapillary carcinoma:an aggressive variant of adenocarcinoma.Hum Pathol 2006;37:809-15.
[18]Liang P,Nakada I,Hong JW,et al.Prognostic significance of immunohistochemically detected blood and lymphatic vessel invasion in colorectal carcinoma:its impact on prognosis.Ann Surg Oncol 2007;14:470-7.
[19]Matsumoto K,Nakayama Y,Inoue Y,et al.Lymphatic microvessel density is an independent prognostic factor in colorectal cancer.Dis Colon Rectum 2007;50:308-14.
[20]Stor Z,Frkovic GS,Bracko M,Repse S.Prognostic value of clinical,pathological and immunohistochemical markers in stage Ⅱ colon cancer patients.Acta Chir Iugosl 2008;55:39-44.
[21]Galon J,Costes A,Sanchez-Cabo F,et al.Type,density,and location of immune cells within human colorectal tumors predict clinical outcome.Science 2006;313:1960-4.
[22]Hamanishi J,Mandai M,Iwasaki M,et al.Programmed cell death 1 ligand 1 and tumor-infiltrating CD8+ T lymphocytes are prognostic factors of human ovarian cancer.Proc Natl Acad Sci U S A 2007;104:3360-5.
[23]Sharma P,Shen Y,Wen S,et al.CD8 tumor-infiltrating lymphocytes are predictive of survival in muscle-invasive urothelial carcinoma.Proc Natl Acad Sci U S A 2007;104:3967-72.
[24]Chiba T,Ohtani H,Mizoi T,et al.Intraepithelial CD8+ T-cell-count becomes a prognostic factor after a longer follow-up period in human colorectal carcinoma:possible association with suppression of micrometastasis.Br J Cancer 2004;91:1711-7.
[25]Oudejans JJ,Harijadi H,Kummer JA,et al.High numbers of granzyme B/CD8-positive tumour-infiltrating lymphocytes in nasopharyngeal carcinoma biopsies predict rapid fatal outcome in patients treated with curative intent.J Pathol 2002;198:468-75.
[26]Locker GY,Hamilton S,Harris J,et al.ASCO 2006 update of recommendations for the use of tumor markers in gastrointestinal cancer.J Clin Oncol 2006;24:5313-27.
[27]Anwar S,Frayling IM,Scott NA,Carlson GL.Systematic review of genetic influences on the prognosis of colorectal cancer.Br J Surg 2004;91:1275-91.
[28]Munro AJ,Lain S,Lane DR P53 abnormalities and outcomes in colorectal cancer:a systematic review.Br J Cancer 2005;92:434-44.
[29]Ruan W,Xu E,Xu F,et al.IGFBP7 plays a potential tumor suppressor role in colorectal carcinogenesis.Cancer Biol Ther 2007;6:354-9.
[30]Adachi Y,Itoh F,Yamamoto H,et al.Expression of angiomodulin (tumor-derived adhesion factor/mac25) in invading tumor cells correlates with poor prognosis in human colorectal cancer.Int J Cancer 2001;95:216-22.
[31]Burger AM,Zhang X,Li H,et al.Down-regulation of TlA12/mac25,a novel insulin-like growth factor binding protein related gene,is associated with disease progression in breast carcinomas.Oncogene 1998;16:2459-67.
[32]Lai MD.[Molecular classification of tumors].Zhejiang Da Xue Xue Bao Yi Xue Ban 2006;35:349-53.
[33]Golub TR,Slonim DK,Tamayo P,et al.Molecular classification of cancer:class discovery and class prediction by gene expression monitoring.Science 1999;286:531-7.
[34]Dhanasekaran SM,Barrette TR,Ghosh D,et al.Delineation of prognostic biomarkers in prostate cancer.Nature 2001;412:822-6.
[35]Weigelt B,Horlings HM,Kreike B,et al.Refinement of breast cancer classification by molecular characterization of histological special types.J Pathol 2008;216:141-50.
[36]Lyall MS,Dundas SR,Curran S,Murray GI.Profiling markers of prognosis in colorectal cancer.Clin Cancer Res 2006;12:1184-91.
[37]Compton CC.Colorectal carcinoma:diagnostic,prognostic,and molecular features.Mod Pathol 2003;16:376-88.
[38]Zlobec I,Lugli A.Prognostic and predictive factors in colorectal cancer.J Clin Pathol 2008;61:561-9.
[39]Weiser MR,Landmann RG,Kattan MW,et al.Individualized prediction of colon cancer recurrence using a nomogram.J Clin Oncol 2008;26:380-5.
[40]Quah HM,Chou JF,Gonen M,et al.Identification of patients with high-risk stage Ⅱ colon cancer for adjuvant therapy.Dis Colon Rectum 2008;51:503-7.
[41]Akasu T,Takawa M,Yamamoto S,et al.Intersphincteric resection for very low rectal adenocarcinoma:univariate and multivariate analyses of risk factors for recurrence.Ann Surg Oncol 2008;15:2668-76.
[42]Masaki T,Matsuoka H,Sugiyama M,et al.Tumor budding and evidence-based treatment of T2 rectal carcinomas.J Surg Oncol 2005;92:59-63.
[43]Tanaka M,Hashiguchi Y,Ueno H,Hase K,Mochizuki H.Tumor budding at the invasive margin can predict patients at high risk of recurrence after curative surgery for stage Ⅱ,T3 colon cancer.Dis Colon Rectum 2003;46:1054-9.
[44]Ueno H,Price AB,Wilkinson KH,Jass JR,Mochizuki H,Talbot IC.A new prognostic staging system for rectal cancer.Ann Surg 2004;240:832-9.
[45]Shimizu Y,Yamamichi N,Saitoh K,et al.Kinetics of v-src-induced epithelial-mesenchymal transition in developing glandular stomach.Oncogene 2003;22:884-93.
[46]Galon J,Fridman WH,Pages F.The adaptive immunologic microenvironment in colorectal cancer:a novel perspective.Cancer Res 2007;67:1883-6.
[47]Dave SS,Wright G,Tan B,et al.Prediction of survival in follicular lymphoma based on molecular features of tumor-infiltrating immune cells.N Engl J Med 2004;351:2159-69.
[48]Menon AG,Janssen-van Rhijn CM,Morreau H,et al.Immune system and prognosis in colorectal cancer:a detailed immunohistochemical analysis.Lab Invest 2004;84:493-501.
[49]Pages F,Berger A,Camus M,et al.Effector memory T cells,early metastasis,and survival in colorectal cancer.N Engl J Med 2005;353:2654-66.
[50]Koike Y,Miki C,Okugawa Y,et al.Preoperative C-reactive protein as a prognostic and therapeutic marker for colorectal cancer.J Surg Oncol 2008;98:540-4.
[51]Tatokoro M,Saito K,Iimura Y,Fujii Y,Kawakami S,Kihara K.Prognostic impact of postoperative C-reactive protein level in patients with metastatic renal cell carcinoma undergoing cytoreductive nephrectomy.J Urol 2008;180:515-9.
[52]Balkwill F,Mantovani A.Inflammation and cancer:back to Virchow? Lancet 2001;357:539-45.
[53]Schmidt H,Suciu S,Punt CJ,et al.Pretreatment levels of peripheral neutrophils and leukocytes as independent predictors of overall survival in patients with American Joint Committee on Cancer Stage IV Melanoma:results of the EORTC 18951 Biochemotherapy Trial.J Clin Oncol 2007;25:1562-9.
[54]Kobayashi T,Teruya M,Kishiki T,et al.Inflammation-based prognostic score,prior to neoadjuvant chemoradiotherapy,predicts postoperative outcome in patients with esophageal squamous cell carcinoma.Surgery 2008;144:729-35.
[55]Sasaki A,Kai S,Endo Y,et al.Prognostic value of preoperative peripheral blood monocyte count in patients with colorectal liver metastasis after liver resection.J Gastrointest Surg 2007;11:596-602.
[56]Paesmans M,Sculier JP,Lecomte J,et al.Prognostic factors for patients with small cell lung carcinoma:analysis of a series of 763 patients included in 4 consecutive prospective trials with a minimum follow-up of 5 years.Cancer 2000;89:523-33.
[57]Halazun KJ,Aldoori A,Malik HZ,et al.Elevated preoperative neutrophil to lymphocyte ratio predicts survival following hepatic resection for colorectal liver metastases.Eur J Surg Oncol 2008;34:55-60.
[58]Neal CP,Mann CD,Sutton CD,et al.Evaluation of the prognostic value of systemic inflammation and socioeconomic deprivation in patients with resectable colorectal liver metastases.Eur J Cancer 2009;45:56-64.
[59]Teramukai S,Kitano T,Kishida Y,et al.Pretreatment neutrophil count as an independent prognostic factor in advanced non-small-cell lung cancer:An analysis of Japan Multinational Trial Organisation LC00-03.Eur J Cancer 2009.
[60]Knosel T,Emde A,Schluns K,et al.Immunoprofiles of 11 biomarkers using tissue microarrays identify prognostic subgroups in colorectal cancer.Neoplasia 2005;7:741-7.
[61]Au NH,Cheang M,Huntsman DG,et al.Evaluation of immunohistochemical markers in non-small cell lung cancer by unsupervised hierarchical clustering analysis:a tissue microarray study of 284 cases and 18 markers.J Pathol 2004;204:101-9.
[62]Makretsov NA,Huntsman DG,Nielsen TO,et al.Hierarchical clustering analysis of tissue microarray immunostaining data identifies prognostically significant groups of breast carcinoma.Clin Cancer Res 2004;10:6143-51.
[63]Etoh T,Shibuta K,Barnard GF,Kitano S,Mori M.Angiogenin expression in human colorectal cancer:the role of focal macrophage infiltration.Clin Cancer Res 2000;6:3545-51.
[64]Maruyama K,Ii M,Cursiefen C,et al.Inflammation-induced lymphangiogenesis in the cornea arises from CD lib-positive macrophages.J Clin Invest 2005;115:2363-72.
[65]Shimura S,Yang G,Ebara S,Wheeler TM,Frolov A,Thompson TC.Reduced infiltration of tumor-associated macrophages in human prostate cancer:association with cancer progression.Cancer Res 2000;60:5857-61.
[66]Klintrup K,Makinen JM,Kauppila S,et al.Inflammation and prognosis in colorectal cancer.Eur J Cancer 2005;41:2645-54.
[67]Chen JJ,Yao PL,Yuan A,et al.Up-regulation of tumor interleukin-8 expression by infiltrating macrophages:its correlation with tumor angiogenesis and patient survival in non-small cell lung cancer.Clin Cancer Res 2003;9:729-37.
[68]Forssell J,Oberg A,Henriksson ML,Stenling R,Jung A,Palmqvist R.High macrophage infiltration along the tumor front correlates with improved survival in colon cancer.Clin Cancer Res 2007;13:1472-9.
[69]Balkwill F.Cancer and the chemokine network.Nat Rev Cancer 2004;4:540-50.
[70]Zeelenberg IS,Ruuls-Van Stalle L,Roos E.The chemokine receptor CXCR4 is required for outgrowth of colon carcinoma micrometastases.Cancer Res 2003;63:3833-9.
[71]Kim J,Mori T,Chen SL,et al.Chemokine receptor CXCR4 expression in patients with melanoma and colorectal cancer liver metastases and the association with disease outcome.Ann Surg 2006;244:113-20.
[72]Muller A,Homey B,Soto H,et al.Involvement of chemokine receptors in breast cancer metastasis.Nature 2001;410:50-6.
[73]Sun YX,Wang J,Shelburne CE,et al.Expression of CXCR4 and CXCL12 (SDF-1) in human prostate cancers (PCa) in vivo.J Cell Biochem 2003;89:462-73.
[74]Kato M,Kitayama J,Kazama S,Nagawa H.Expression pattern of CXC chemokine receptor-4 is correlated with lymph node metastasis in human invasive ductal carcinoma.Breast Cancer Res 2003;5:R144-50.
[75]Begley L,Monteleon C,Shah RB,Macdonald JW,Macoska J A.CXCL12 overexpression and secretion by aging fibroblasts enhance human prostate epithelial proliferation in vitro.Aging Cell 2005;4:291-8.
[76]Chen H,Zha J,Gowans RE,et al.Alcohol enhances HIV type 1 infection in normal human oral keratinocytes by up-regulating cell-surface CXCR4 corcceptor.AIDS Res Hum Retroviruses 2004;20:513-9.
[77]Henderson BR,Fagotto F.The ins and outs of APC and beta-catenin nuclear transport.EMBO Rep 2002;3:834-9.
[78]Gill S,Loprinzi CL,Sargent DJ,et al.Pooled analysis of fiuorouracil-based adjuvant therapy for stage Ⅱ and Ⅲ colon cancer:who benefits and by how much? J Clin Oncol 2004;22:1797-806.
[79]Wolpin BM,Mayer RJ.Systemic treatment of colorectal cancer.Gastroenterology 2008;134:1296-310.
[1] Dave SS, Wright G, Tan B, et al. Prediction of survival in follicular lymphoma based on molecular features of tumor-infiltrating immune cells. N Engl J Med, 2004, 13(21):2159-69.
[2] Galon J, Costes A, Sanchez-Cabo F, et al. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science, 2006, 313(5795): 1960-4.
[3] Pages F, Berger A, Camus M, et al. Effector memory T cells, early metastasis, and survival in colorectal cancer. N Engl J Med, 2005, 353(25):2654-66.
[4] Ohtani H. Focus on TILs: prognostic significance of tumor infiltrating lymphocytes in human colorectal cancer.Cancer Immun,2007,7(4):1-9.
[5]Natio Y,Saito K,Ohuchi A,et al.CD8 T cells infiltrated within cancer cell nests as a prognostic factor in human colorectal cancer.Cancer Res,1998,58(16):3491-4.
[6]Galon J,Fridman W,Pages F,et al.The adaptive immunologic microenvironment in colorectal cancer:a novel perspective.Cancer Res,2007,67(5):1883-6.
[7]Menon AG,Rhijn CMJ,Morreau H,et al.Immune system and prognosis in colorectal cancer:a detailed immunohistochemical analysis.Lab Invest,2004,84(4):493-501.
[8]Hamanishi J,Mandai M,Iwasaki M,et al.Programmed Cell Death 1 Ligand 1 and tumor-infiltrating CD8+ T lymphocytes are prognostic factors of human ovarian cancer.Proc Natl Acad Sci USA,2007,104(9):3360-5.
[9]Sharma P,Shen Y,Wen S,et al.CD8 tumor-infiltrating lymphocytes are predictive of survival in muscle-invasive urothelial carcinoma.Proc Natl Acad Sci USA,2007,104(10):3967-72.
[10]Chiba T,Ohtani H,Mizoi T,et al.Intraepithelial CD8+ T-cell-count becomes a prognostic factor after a longer follow-up period in human colorectal carcinoma:possible association with suppression of micrometastasis.Br J Cancer,2004,91(9):1711-7.
[11]Oudejans JJ,Harijadi H,Kummer JA,et al.High numbers of granzyme B/CD8-positive tumor-infiltrating lymphocytes in nasopharyngeal carcinoma biopsies predict rapid fatal outcome in patients treated with curative intent.J Pathol,2002,198(4):468-75.
[12]Grabenbauer GG,Lahmer G,Distel L,et al.Tumor-infiltrating cytotoxic T cells but not regulatory T cells predict outcome in anal squamous cell carcinoma.Clin Cancer Res,2006,12(11):3355-60.
[13]Sato E,Olson SH,Ahn J,et al.Intraepithelial CD8+ tumor-infiltrating lymphocytes and a high CD8+/regulatory T cell ratio are associated with favorable prognosis in ovarian cancer.Proc Natl Acad Sci USA,2005,102(51):18538-43.
[14]Bates GJ,Fox SB,Han C,et al.Quantification of regulatory T cells enables the identification of high-risk breast cancer patients and those at risk of late relapse.J Clin Oncol,2006,24(34):5373-80.
[15]Gao Q,Qiu SJ,Fan J,et al.Intratumoral balance of regulatory and cytotoxic T cells is associated with prognosis of hepatocellular carcinoma after resection.J Clin Oncol,2007,25(18):2586-93.
[16]Hasselblom S,Sigurdadottir M,Hansson U,et al.The number of tumor-infiltrating TIA-1~+ cytotoxic T cells but not FOXP3~+ regulatory T cells predicts outcome in diffuse large B-cell lymphoma.Br J Haematol,2007,137(4):364-73.
[17]Carreras J,Sigurdadottir M,Hansson U,et al.High numbers of tumor infiltrating FOXP3-positive regulatory T cells are associated with improved overall survival in follicular lymphoma.Blood,2006,108(9):2957-64.
[18]Hoffmann P,Eder R,Boeld TJ,et al.Only the CD45RA~+ subpopulation of CD4~+CD25 ~(high) T cells gives rise to homogeneous regulatory T-cell lines upon in vitro expansion.Blood,2006,108(13):4260-7.
[19]Strauss L,Bergmann C,Szczepanski M,et al.A unique subset of CD4~+CD25~(high) FOXP3~+ T cells secreting interleukin-10 and transforming growth factor-pi mediates suppression in the tumor microenvironment.Clin Cancer Res,2007,13(15):4345-54.
[20]Wolf AM,Rumpold H,Wolf D,et al.Role of Forkhead Box Protein 3 expression in invasive breast cancer.J Clin Oncol,2007,25(28):4499-500.
[21]Bates GJ,Bignone PA,Banham AH.Reply.J Clin Oncol,2007,25(28):4500-1.
[22]Klebanoff CA,Gattinoni L,Torabi-Parizi P,et al.Central memory self/tumor-reactive CD8~+ T cells confer superior antitumor immunity compared with effector memory T cells.Proc Natl Acad Sci USA,2005,102(27):9571-6.
[23]Moiling JW,Langius JA,Langendijk LJA,et al.Low levels of circulating invariant natural killer T cells predict poor clinical outcome in patients with head and neck squamous cell carcinoma.J Clin Oncol,2007,25(7):862-8.
[24]Metelitsa LS,Wu HW,Wang H,et al.Natural killer T cells infiltrate neuroblastomas expressing the chemokine CCL2.J Exp Med,2004,199(9):1213-21.
[25]Tachibana T,Onodera H,Tsuruyama T,et al.Increased intratumor Va24-positive natural killer T cells:a prognostic factor for primary colorectal carcinomas.Clin Cancer Res,2005,11(20):7322-7.
[26]Klintrup K,Makinen JM,Karppila S,et al.Inflammation and prognosis in colorectal cancer.Eur J Cancer,2005,41(17):2645-54.
[27]Leek RD,Lewis CE,Whitehouse R,et al.Association of macrophage infiltration with angiogenesis and prognosis in invasive breast carcinoma.Cancer Res,1996,56(20):4625-9.
[28]Shimura S,Yang G,Ebara S,et al.Reduced infiltration of tumor-associated macrophages in human prostate cancer:association with cancer progression.Cancer Res,2000,60(20):5857-61.
[29]Lissbrant IF,Stattin P,Wikstrom P,et al.Tumor associated macrophages in human prostate cancer:relation to clinicopathological variables and survival.Int J Oncol,2000,17(3):445-51.
[30]Xu F,Wang F,Di M,et al.Classification based on the combination of molecular and pathologic predictors is superior to molecular classification on prognosis in colorectal carcinoma.Clin Cancer Res,2007,13(17):5082-8.
[31]Taskinen M,Karjalainen-Lindsberg ML,Nyman H,et al.A high tumor-associated macrophage content predicts favorable outcome in follicular lymphoma patients treated with rituximab and cyclophosphamide-Doxorubicin-vincristine-prednisone.Clin Cancer Res,2007,13(19):5784-9.
[32]Cai XY,Gao Q,Qiu SJ,et al.Dendritic cell infiltration and prognosis of human hepatocellular carcinoma.J Cancer Res Clin Oncol,2006,132(5):293-301.
[33]Movassagh M,Spatz A,Davoust J,et al.Selective accumulation of mature DC-LAMP~+ dendritic cells in tumor sites is associated with efficient T-cell mediated antitumor response and control of metastatic dissemination in melanoma.Cancer Res,2004,64(6):2192-8.
[34]Elliott B,Scolyer RA,Suciu S,et al.Long-term protective effect of mature DC-LAMP~+ dendritic cell accumulation in sentinel lymph nodes containing micrometastatic melanoma.Clin Cancer Res,2007,13(13):3825-9.
[2]Wang LM,Kevans D,Mulcahy H,et al.Tumor budding is a strong and reproducible prognostic marker in T3N0 colorectal cancer.Am J Surg Pathol 2009;33:134-41.
[3]Compton CC,Greene FL.The staging of colorectal cancer:2004 and beyond.CA Cancer J Clin 2004;54:295-308.
[4]Singletary SE,Greene FL,Sobin LH.Classification of isolated tumor cells:clarification of the 6th edition of the American Joint Committee on Cancer Staging Manual.Cancer 2003;98:2740-1.
[5]Reed J,Rosman M,Verbanac KM,Mannie A,Cheng Z,Tafra L.Prognostic implications of isolated tumor cells and micrometastases in sentinel nodes of patients with invasive breast cancer:10-year analysis of patients enrolled in the prospective East Carolina University/Anne Arundel Medical Center Sentinel Node Multicenter Study.J Am Coll Surg 2009;208:333-40.
[6]Fukagawa T,Sasako M,Shimoda T,et al.The prognostic impact of isolated tumor cells in lymph nodes of T2N0 gastric cancer:comparison of American and Japanese gastric cancer patients.Ann Surg Oncol 2009;16:609-13.
[7]Yonemura Y,Endo Y,Hayashi I,Kawamura T,Yun HY,Bandou E.Proliferative activity of micrometastases in the lymph nodes of patients with gastric cancer.Br J Surg 2007;94:731-6.
[8]Hase K,Shatney C,Johnson D,Trollope M,Vierra M.Prognostic value of tumor "budding" in patients with colorectal cancer.Dis Colon Rectum 1993;36:627-35.
[9]Morodomi T,Isomoto H,Shirouzu K,Kakegawa K,Irie K,Morimatsu M.An index for estimating the probability of lymph node metastasis in rectal cancers.Lymph node metastasis and the histopathology of actively invasive regions of cancer.Cancer 1989;63:539-43.
[10]Kaihara T,Kusaka T,Nishi M,et al.Dedifferentiation and decreased expression of adhesion molecules,E-cadherin and ZO-1,in colorectal cancer are closely related to liver metastasis.J Exp Clin Cancer Res 2003;22:117-23.
[11]Guzinska-Ustymowicz K.MMP-9 and cathepsin B expression in tumor budding as an indicator of a more aggressive phenotype of colorectal cancer (CRC).Anticancer Res 2006;26:1589-94.
[12]Haupt B,Ro JY,Schwartz MR,Shen SS.Colorectal adenocarcinoma with micropapillary pattern and its association with lymph node metastasis.Mod Pathol 2007;20:729-33.
[13]Nassar H,Wallis T,Andea A,Dey J,Adsay V,Visscher D.Clinicopathologic analysis of invasive micropapillary differentiation in breast carcinoma.Mod Pathol 2001;14:836-41.
[14]Prat J,De Nictolis M.Serous borderline tumors of the ovary:a long-term follow-up study of 137 cases,including 18 with a micropapillary pattern and 20 with microinvasion.Am J Surg Pathol 2002;26:1111-28.
[15]Amin MB,Tamboli P,Merchant SH,et al.Micropapillary component in lung adenocarcinoma:a distinctive histologic feature with possible prognostic significance.Am J Surg Pathol 2002;26:358-64.
[16]Johansson SL,Borghede G,Holmang S.Micropapillary bladder carcinoma:a clinicopathological study of 20 cases.J Urol 1999;161:1798-802.
[17]Kim MJ,Hong SM,Jang SJ,et al.Invasive colorectal micropapillary carcinoma:an aggressive variant of adenocarcinoma.Hum Pathol 2006;37:809-15.
[18]Liang P,Nakada I,Hong JW,et al.Prognostic significance of immunohistochemically detected blood and lymphatic vessel invasion in colorectal carcinoma:its impact on prognosis.Ann Surg Oncol 2007;14:470-7.
[19]Matsumoto K,Nakayama Y,Inoue Y,et al.Lymphatic microvessel density is an independent prognostic factor in colorectal cancer.Dis Colon Rectum 2007;50:308-14.
[20]Stor Z,Frkovic GS,Bracko M,Repse S.Prognostic value of clinical,pathological and immunohistochemical markers in stage Ⅱ colon cancer patients.Acta Chir Iugosl 2008;55:39-44.
[21]Galon J,Costes A,Sanchez-Cabo F,et al.Type,density,and location of immune cells within human colorectal tumors predict clinical outcome.Science 2006;313:1960-4.
[22]Hamanishi J,Mandai M,Iwasaki M,et al.Programmed cell death 1 ligand 1 and tumor-infiltrating CD8+ T lymphocytes are prognostic factors of human ovarian cancer.Proc Natl Acad Sci U S A 2007;104:3360-5.
[23]Sharma P,Shen Y,Wen S,et al.CD8 tumor-infiltrating lymphocytes are predictive of survival in muscle-invasive urothelial carcinoma.Proc Natl Acad Sci U S A 2007;104:3967-72.
[24]Chiba T,Ohtani H,Mizoi T,et al.Intraepithelial CD8+ T-cell-count becomes a prognostic factor after a longer follow-up period in human colorectal carcinoma:possible association with suppression of micrometastasis.Br J Cancer 2004;91:1711-7.
[25]Oudejans JJ,Harijadi H,Kummer JA,et al.High numbers of granzyme B/CD8-positive tumour-infiltrating lymphocytes in nasopharyngeal carcinoma biopsies predict rapid fatal outcome in patients treated with curative intent.J Pathol 2002;198:468-75.
[26]Locker GY,Hamilton S,Harris J,et al.ASCO 2006 update of recommendations for the use of tumor markers in gastrointestinal cancer.J Clin Oncol 2006;24:5313-27.
[27]Anwar S,Frayling IM,Scott NA,Carlson GL.Systematic review of genetic influences on the prognosis of colorectal cancer.Br J Surg 2004;91:1275-91.
[28]Munro AJ,Lain S,Lane DR P53 abnormalities and outcomes in colorectal cancer:a systematic review.Br J Cancer 2005;92:434-44.
[29]Ruan W,Xu E,Xu F,et al.IGFBP7 plays a potential tumor suppressor role in colorectal carcinogenesis.Cancer Biol Ther 2007;6:354-9.
[30]Adachi Y,Itoh F,Yamamoto H,et al.Expression of angiomodulin (tumor-derived adhesion factor/mac25) in invading tumor cells correlates with poor prognosis in human colorectal cancer.Int J Cancer 2001;95:216-22.
[31]Burger AM,Zhang X,Li H,et al.Down-regulation of TlA12/mac25,a novel insulin-like growth factor binding protein related gene,is associated with disease progression in breast carcinomas.Oncogene 1998;16:2459-67.
[32]Lai MD.[Molecular classification of tumors].Zhejiang Da Xue Xue Bao Yi Xue Ban 2006;35:349-53.
[33]Golub TR,Slonim DK,Tamayo P,et al.Molecular classification of cancer:class discovery and class prediction by gene expression monitoring.Science 1999;286:531-7.
[34]Dhanasekaran SM,Barrette TR,Ghosh D,et al.Delineation of prognostic biomarkers in prostate cancer.Nature 2001;412:822-6.
[35]Weigelt B,Horlings HM,Kreike B,et al.Refinement of breast cancer classification by molecular characterization of histological special types.J Pathol 2008;216:141-50.
[36]Lyall MS,Dundas SR,Curran S,Murray GI.Profiling markers of prognosis in colorectal cancer.Clin Cancer Res 2006;12:1184-91.
[37]Compton CC.Colorectal carcinoma:diagnostic,prognostic,and molecular features.Mod Pathol 2003;16:376-88.
[38]Zlobec I,Lugli A.Prognostic and predictive factors in colorectal cancer.J Clin Pathol 2008;61:561-9.
[39]Weiser MR,Landmann RG,Kattan MW,et al.Individualized prediction of colon cancer recurrence using a nomogram.J Clin Oncol 2008;26:380-5.
[40]Quah HM,Chou JF,Gonen M,et al.Identification of patients with high-risk stage Ⅱ colon cancer for adjuvant therapy.Dis Colon Rectum 2008;51:503-7.
[41]Akasu T,Takawa M,Yamamoto S,et al.Intersphincteric resection for very low rectal adenocarcinoma:univariate and multivariate analyses of risk factors for recurrence.Ann Surg Oncol 2008;15:2668-76.
[42]Masaki T,Matsuoka H,Sugiyama M,et al.Tumor budding and evidence-based treatment of T2 rectal carcinomas.J Surg Oncol 2005;92:59-63.
[43]Tanaka M,Hashiguchi Y,Ueno H,Hase K,Mochizuki H.Tumor budding at the invasive margin can predict patients at high risk of recurrence after curative surgery for stage Ⅱ,T3 colon cancer.Dis Colon Rectum 2003;46:1054-9.
[44]Ueno H,Price AB,Wilkinson KH,Jass JR,Mochizuki H,Talbot IC.A new prognostic staging system for rectal cancer.Ann Surg 2004;240:832-9.
[45]Shimizu Y,Yamamichi N,Saitoh K,et al.Kinetics of v-src-induced epithelial-mesenchymal transition in developing glandular stomach.Oncogene 2003;22:884-93.
[46]Galon J,Fridman WH,Pages F.The adaptive immunologic microenvironment in colorectal cancer:a novel perspective.Cancer Res 2007;67:1883-6.
[47]Dave SS,Wright G,Tan B,et al.Prediction of survival in follicular lymphoma based on molecular features of tumor-infiltrating immune cells.N Engl J Med 2004;351:2159-69.
[48]Menon AG,Janssen-van Rhijn CM,Morreau H,et al.Immune system and prognosis in colorectal cancer:a detailed immunohistochemical analysis.Lab Invest 2004;84:493-501.
[49]Pages F,Berger A,Camus M,et al.Effector memory T cells,early metastasis,and survival in colorectal cancer.N Engl J Med 2005;353:2654-66.
[50]Koike Y,Miki C,Okugawa Y,et al.Preoperative C-reactive protein as a prognostic and therapeutic marker for colorectal cancer.J Surg Oncol 2008;98:540-4.
[51]Tatokoro M,Saito K,Iimura Y,Fujii Y,Kawakami S,Kihara K.Prognostic impact of postoperative C-reactive protein level in patients with metastatic renal cell carcinoma undergoing cytoreductive nephrectomy.J Urol 2008;180:515-9.
[52]Balkwill F,Mantovani A.Inflammation and cancer:back to Virchow? Lancet 2001;357:539-45.
[53]Schmidt H,Suciu S,Punt CJ,et al.Pretreatment levels of peripheral neutrophils and leukocytes as independent predictors of overall survival in patients with American Joint Committee on Cancer Stage IV Melanoma:results of the EORTC 18951 Biochemotherapy Trial.J Clin Oncol 2007;25:1562-9.
[54]Kobayashi T,Teruya M,Kishiki T,et al.Inflammation-based prognostic score,prior to neoadjuvant chemoradiotherapy,predicts postoperative outcome in patients with esophageal squamous cell carcinoma.Surgery 2008;144:729-35.
[55]Sasaki A,Kai S,Endo Y,et al.Prognostic value of preoperative peripheral blood monocyte count in patients with colorectal liver metastasis after liver resection.J Gastrointest Surg 2007;11:596-602.
[56]Paesmans M,Sculier JP,Lecomte J,et al.Prognostic factors for patients with small cell lung carcinoma:analysis of a series of 763 patients included in 4 consecutive prospective trials with a minimum follow-up of 5 years.Cancer 2000;89:523-33.
[57]Halazun KJ,Aldoori A,Malik HZ,et al.Elevated preoperative neutrophil to lymphocyte ratio predicts survival following hepatic resection for colorectal liver metastases.Eur J Surg Oncol 2008;34:55-60.
[58]Neal CP,Mann CD,Sutton CD,et al.Evaluation of the prognostic value of systemic inflammation and socioeconomic deprivation in patients with resectable colorectal liver metastases.Eur J Cancer 2009;45:56-64.
[59]Teramukai S,Kitano T,Kishida Y,et al.Pretreatment neutrophil count as an independent prognostic factor in advanced non-small-cell lung cancer:An analysis of Japan Multinational Trial Organisation LC00-03.Eur J Cancer 2009.
[60]Knosel T,Emde A,Schluns K,et al.Immunoprofiles of 11 biomarkers using tissue microarrays identify prognostic subgroups in colorectal cancer.Neoplasia 2005;7:741-7.
[61]Au NH,Cheang M,Huntsman DG,et al.Evaluation of immunohistochemical markers in non-small cell lung cancer by unsupervised hierarchical clustering analysis:a tissue microarray study of 284 cases and 18 markers.J Pathol 2004;204:101-9.
[62]Makretsov NA,Huntsman DG,Nielsen TO,et al.Hierarchical clustering analysis of tissue microarray immunostaining data identifies prognostically significant groups of breast carcinoma.Clin Cancer Res 2004;10:6143-51.
[63]Etoh T,Shibuta K,Barnard GF,Kitano S,Mori M.Angiogenin expression in human colorectal cancer:the role of focal macrophage infiltration.Clin Cancer Res 2000;6:3545-51.
[64]Maruyama K,Ii M,Cursiefen C,et al.Inflammation-induced lymphangiogenesis in the cornea arises from CD lib-positive macrophages.J Clin Invest 2005;115:2363-72.
[65]Shimura S,Yang G,Ebara S,Wheeler TM,Frolov A,Thompson TC.Reduced infiltration of tumor-associated macrophages in human prostate cancer:association with cancer progression.Cancer Res 2000;60:5857-61.
[66]Klintrup K,Makinen JM,Kauppila S,et al.Inflammation and prognosis in colorectal cancer.Eur J Cancer 2005;41:2645-54.
[67]Chen JJ,Yao PL,Yuan A,et al.Up-regulation of tumor interleukin-8 expression by infiltrating macrophages:its correlation with tumor angiogenesis and patient survival in non-small cell lung cancer.Clin Cancer Res 2003;9:729-37.
[68]Forssell J,Oberg A,Henriksson ML,Stenling R,Jung A,Palmqvist R.High macrophage infiltration along the tumor front correlates with improved survival in colon cancer.Clin Cancer Res 2007;13:1472-9.
[69]Balkwill F.Cancer and the chemokine network.Nat Rev Cancer 2004;4:540-50.
[70]Zeelenberg IS,Ruuls-Van Stalle L,Roos E.The chemokine receptor CXCR4 is required for outgrowth of colon carcinoma micrometastases.Cancer Res 2003;63:3833-9.
[71]Kim J,Mori T,Chen SL,et al.Chemokine receptor CXCR4 expression in patients with melanoma and colorectal cancer liver metastases and the association with disease outcome.Ann Surg 2006;244:113-20.
[72]Muller A,Homey B,Soto H,et al.Involvement of chemokine receptors in breast cancer metastasis.Nature 2001;410:50-6.
[73]Sun YX,Wang J,Shelburne CE,et al.Expression of CXCR4 and CXCL12 (SDF-1) in human prostate cancers (PCa) in vivo.J Cell Biochem 2003;89:462-73.
[74]Kato M,Kitayama J,Kazama S,Nagawa H.Expression pattern of CXC chemokine receptor-4 is correlated with lymph node metastasis in human invasive ductal carcinoma.Breast Cancer Res 2003;5:R144-50.
[75]Begley L,Monteleon C,Shah RB,Macdonald JW,Macoska J A.CXCL12 overexpression and secretion by aging fibroblasts enhance human prostate epithelial proliferation in vitro.Aging Cell 2005;4:291-8.
[76]Chen H,Zha J,Gowans RE,et al.Alcohol enhances HIV type 1 infection in normal human oral keratinocytes by up-regulating cell-surface CXCR4 corcceptor.AIDS Res Hum Retroviruses 2004;20:513-9.
[77]Henderson BR,Fagotto F.The ins and outs of APC and beta-catenin nuclear transport.EMBO Rep 2002;3:834-9.
[78]Gill S,Loprinzi CL,Sargent DJ,et al.Pooled analysis of fiuorouracil-based adjuvant therapy for stage Ⅱ and Ⅲ colon cancer:who benefits and by how much? J Clin Oncol 2004;22:1797-806.
[79]Wolpin BM,Mayer RJ.Systemic treatment of colorectal cancer.Gastroenterology 2008;134:1296-310.
[1] Dave SS, Wright G, Tan B, et al. Prediction of survival in follicular lymphoma based on molecular features of tumor-infiltrating immune cells. N Engl J Med, 2004, 13(21):2159-69.
[2] Galon J, Costes A, Sanchez-Cabo F, et al. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science, 2006, 313(5795): 1960-4.
[3] Pages F, Berger A, Camus M, et al. Effector memory T cells, early metastasis, and survival in colorectal cancer. N Engl J Med, 2005, 353(25):2654-66.
[4] Ohtani H. Focus on TILs: prognostic significance of tumor infiltrating lymphocytes in human colorectal cancer.Cancer Immun,2007,7(4):1-9.
[5]Natio Y,Saito K,Ohuchi A,et al.CD8 T cells infiltrated within cancer cell nests as a prognostic factor in human colorectal cancer.Cancer Res,1998,58(16):3491-4.
[6]Galon J,Fridman W,Pages F,et al.The adaptive immunologic microenvironment in colorectal cancer:a novel perspective.Cancer Res,2007,67(5):1883-6.
[7]Menon AG,Rhijn CMJ,Morreau H,et al.Immune system and prognosis in colorectal cancer:a detailed immunohistochemical analysis.Lab Invest,2004,84(4):493-501.
[8]Hamanishi J,Mandai M,Iwasaki M,et al.Programmed Cell Death 1 Ligand 1 and tumor-infiltrating CD8+ T lymphocytes are prognostic factors of human ovarian cancer.Proc Natl Acad Sci USA,2007,104(9):3360-5.
[9]Sharma P,Shen Y,Wen S,et al.CD8 tumor-infiltrating lymphocytes are predictive of survival in muscle-invasive urothelial carcinoma.Proc Natl Acad Sci USA,2007,104(10):3967-72.
[10]Chiba T,Ohtani H,Mizoi T,et al.Intraepithelial CD8+ T-cell-count becomes a prognostic factor after a longer follow-up period in human colorectal carcinoma:possible association with suppression of micrometastasis.Br J Cancer,2004,91(9):1711-7.
[11]Oudejans JJ,Harijadi H,Kummer JA,et al.High numbers of granzyme B/CD8-positive tumor-infiltrating lymphocytes in nasopharyngeal carcinoma biopsies predict rapid fatal outcome in patients treated with curative intent.J Pathol,2002,198(4):468-75.
[12]Grabenbauer GG,Lahmer G,Distel L,et al.Tumor-infiltrating cytotoxic T cells but not regulatory T cells predict outcome in anal squamous cell carcinoma.Clin Cancer Res,2006,12(11):3355-60.
[13]Sato E,Olson SH,Ahn J,et al.Intraepithelial CD8+ tumor-infiltrating lymphocytes and a high CD8+/regulatory T cell ratio are associated with favorable prognosis in ovarian cancer.Proc Natl Acad Sci USA,2005,102(51):18538-43.
[14]Bates GJ,Fox SB,Han C,et al.Quantification of regulatory T cells enables the identification of high-risk breast cancer patients and those at risk of late relapse.J Clin Oncol,2006,24(34):5373-80.
[15]Gao Q,Qiu SJ,Fan J,et al.Intratumoral balance of regulatory and cytotoxic T cells is associated with prognosis of hepatocellular carcinoma after resection.J Clin Oncol,2007,25(18):2586-93.
[16]Hasselblom S,Sigurdadottir M,Hansson U,et al.The number of tumor-infiltrating TIA-1~+ cytotoxic T cells but not FOXP3~+ regulatory T cells predicts outcome in diffuse large B-cell lymphoma.Br J Haematol,2007,137(4):364-73.
[17]Carreras J,Sigurdadottir M,Hansson U,et al.High numbers of tumor infiltrating FOXP3-positive regulatory T cells are associated with improved overall survival in follicular lymphoma.Blood,2006,108(9):2957-64.
[18]Hoffmann P,Eder R,Boeld TJ,et al.Only the CD45RA~+ subpopulation of CD4~+CD25 ~(high) T cells gives rise to homogeneous regulatory T-cell lines upon in vitro expansion.Blood,2006,108(13):4260-7.
[19]Strauss L,Bergmann C,Szczepanski M,et al.A unique subset of CD4~+CD25~(high) FOXP3~+ T cells secreting interleukin-10 and transforming growth factor-pi mediates suppression in the tumor microenvironment.Clin Cancer Res,2007,13(15):4345-54.
[20]Wolf AM,Rumpold H,Wolf D,et al.Role of Forkhead Box Protein 3 expression in invasive breast cancer.J Clin Oncol,2007,25(28):4499-500.
[21]Bates GJ,Bignone PA,Banham AH.Reply.J Clin Oncol,2007,25(28):4500-1.
[22]Klebanoff CA,Gattinoni L,Torabi-Parizi P,et al.Central memory self/tumor-reactive CD8~+ T cells confer superior antitumor immunity compared with effector memory T cells.Proc Natl Acad Sci USA,2005,102(27):9571-6.
[23]Moiling JW,Langius JA,Langendijk LJA,et al.Low levels of circulating invariant natural killer T cells predict poor clinical outcome in patients with head and neck squamous cell carcinoma.J Clin Oncol,2007,25(7):862-8.
[24]Metelitsa LS,Wu HW,Wang H,et al.Natural killer T cells infiltrate neuroblastomas expressing the chemokine CCL2.J Exp Med,2004,199(9):1213-21.
[25]Tachibana T,Onodera H,Tsuruyama T,et al.Increased intratumor Va24-positive natural killer T cells:a prognostic factor for primary colorectal carcinomas.Clin Cancer Res,2005,11(20):7322-7.
[26]Klintrup K,Makinen JM,Karppila S,et al.Inflammation and prognosis in colorectal cancer.Eur J Cancer,2005,41(17):2645-54.
[27]Leek RD,Lewis CE,Whitehouse R,et al.Association of macrophage infiltration with angiogenesis and prognosis in invasive breast carcinoma.Cancer Res,1996,56(20):4625-9.
[28]Shimura S,Yang G,Ebara S,et al.Reduced infiltration of tumor-associated macrophages in human prostate cancer:association with cancer progression.Cancer Res,2000,60(20):5857-61.
[29]Lissbrant IF,Stattin P,Wikstrom P,et al.Tumor associated macrophages in human prostate cancer:relation to clinicopathological variables and survival.Int J Oncol,2000,17(3):445-51.
[30]Xu F,Wang F,Di M,et al.Classification based on the combination of molecular and pathologic predictors is superior to molecular classification on prognosis in colorectal carcinoma.Clin Cancer Res,2007,13(17):5082-8.
[31]Taskinen M,Karjalainen-Lindsberg ML,Nyman H,et al.A high tumor-associated macrophage content predicts favorable outcome in follicular lymphoma patients treated with rituximab and cyclophosphamide-Doxorubicin-vincristine-prednisone.Clin Cancer Res,2007,13(19):5784-9.
[32]Cai XY,Gao Q,Qiu SJ,et al.Dendritic cell infiltration and prognosis of human hepatocellular carcinoma.J Cancer Res Clin Oncol,2006,132(5):293-301.
[33]Movassagh M,Spatz A,Davoust J,et al.Selective accumulation of mature DC-LAMP~+ dendritic cells in tumor sites is associated with efficient T-cell mediated antitumor response and control of metastatic dissemination in melanoma.Cancer Res,2004,64(6):2192-8.
[34]Elliott B,Scolyer RA,Suciu S,et al.Long-term protective effect of mature DC-LAMP~+ dendritic cell accumulation in sentinel lymph nodes containing micrometastatic melanoma.Clin Cancer Res,2007,13(13):3825-9.